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Tech

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Amazon Undercuts Apple With Quicker MacBook Neo Delivery and Discounted Price

Amazon is offering same-day delivery on Apple's MacBook Neo in select locations alongside its ongoing $9 discount on the notebook. You can get all four colors of the 256GB delivered same-day in many locations, plus a few colors of the 512GB model.

Note: MacRumors is an affiliate partner with some of these vendors. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

If your location doesn't have a same-day delivery option, Amazon is still providing a delivery estimate between June 4 and June 8 on every MacBook Neo computer. This beats Apple's current delivery estimates by up to a week.

$9 OFFMacBook Neo for $589.99

Apple's MacBook Neo has recently been struck by delayed delivery estimates on Apple.com, due to the notebook's booming popularity. Amazon's quicker delivery estimates are bolstered by the small $9 discounts on each model of the notebook, and Amazon is still the only retailer hosting these deals.

Following its launch in March, the MacBook Neo has become a big hit for Apple, with the company struggling to keep the computer in stock online and in Apple stores. As of writing, Apple.com quotes a 1-2 week delivery estimate on every model of the Neo in the U.S. and many other countries.

If you're on the hunt for more discounts, be sure to visit our Apple Deals roundup where we recap the best Apple-related bargains of the past week.



Deals Newsletter

Interested in hearing more about the best deals you can find in 2026? Sign up for our Deals Newsletter and we'll keep you updated so you don't miss the biggest deals of the season!




Related Roundup: Apple Deals
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MacBook Neo is So Popular That Apple Reportedly Doubled Production

On an earnings call in late April, Apple's CEO Tim Cook said that customer response to the MacBook Neo was "off the charts," and the popularity of the laptop has reportedly led the company to significantly boost production.


Apple supply chain analyst Ming-Chi Kuo this week said he believes that MacBook Neo shipments to Apple were doubled from an initial target of 5 million units to 10 million units in 2026 at some point after the laptop launched in March.

Apple was very optimistic about the MacBook Neo before announcing it, but the company still "undercalled" the level of enthusiasm that the laptop would generate, according to Cook. He said that MacBook Neo demand exceeded Apple's expectations and helped to drive a record number of first-time Mac buyers last quarter.

New figures from market research firm IDC support Apple's claim that the MacBook Neo is selling well, and the Windows PC industry has taken notice. For example, Dell recently introduced a redesigned XPS 13 laptop from $699 and said it has features "you won't find on a MacBook Neo," such as a touch screen and a backlit keyboard.

"Apple's MacBook Neo is a capable machine, and its arrival confirms that there's real appetite for premium quality at accessible prices," admitted Dell.

With a starting price of $599 in the U.S., or $499 for college students, the MacBook Neo is Apple's most affordable MacBook ever. Powered by the iPhone's A18 Pro chip, the laptop is available in colorful finishes like Citrus and Blush.

A second-generation MacBook Neo is expected to launch next year with an A19 Pro chip and 12GB of RAM.Related Roundup: MacBook NeoTag: Ming-Chi KuoBuyer's Guide: MacBook Neo (Buy Now)Related Forum: MacBook Neo
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Apple Says macOS 27 Won't Be Compatible With These Macs

During WWDC 2025, Apple revealed that macOS 26 Tahoe would be the final major macOS version for Intel-based Macs.


macOS 27 will be compatible with Apple silicon Macs only, meaning that you will need a Mac with an M-series chip or a MacBook Neo with an A18 Pro chip in order to install the software update. Apple will unveil macOS 27 during its WWDC 2026 keynote this Monday, June 8, and the update should be widely released in September.

Intel-based Macs that can run macOS Tahoe but will not be compatible with macOS 27:13-inch MacBook Pro (2020, Four Thunderbolt 3 Ports)
16-inch MacBook Pro (2019)
27-inch iMac (2020)
Mac Pro (2019)Apple said Intel-based Macs will continue to receive security updates for three years.

macOS 27's exact compatibility with Apple silicon Macs remains to be seen, but presumably the update will support all Macs with an M1 chip or newer.

macOS 27 will still be able to run Intel apps, as it will be the final major macOS release to include Apple's full Rosetta translation layer.

"Rosetta was designed to make the transition to Apple silicon easier, and we plan to make it available for the next two major macOS releases – through macOS 27 – as a general-purpose tool for Intel apps to help developers complete the migration of their apps," said Apple. "Beyond this timeframe, we will keep a subset of Rosetta functionality aimed at supporting older unmaintained gaming titles, that rely on Intel-based frameworks."Related Roundup: macOS 27
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LG Kicks Off Summer Sale With Big Deals on Monitors, TVs, and Appliances

LG is hosting a big savings event on its website this week, with deals on monitors, TVs, home appliances, and more. LG's discounts have been automatically applied and do not require any discount codes or special memberships.

Note: MacRumors is an affiliate partner with LG. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

Highlights of the event include up to $500 off select LG monitors and up to $1,500 off LG's best TV sets. If you're planning to buy multiple monitors, LG is offering an extra 5 percent off your order with select models.

SITEWIDE SALELG Summer Sale

TVs

55-inch LG UHD 4K Smart TV - $299.99 ($80 off)
75-inch LG Mini LED 4K Smart TV - $729.99 ($70 off)
86-inch LG QNED 4K Smart TV - $999.99 ($299 off)
65-inch LG OLED 4K Smart TV - $1,199.99 ($800 off)
77-inch LG evo AI 4K Smart TV - $2,199.99 ($1,500 off)

Monitors

34-inch UltraGear Curved Monitor - $249.99 ($150 off)
34-inch UltraWide Curved Monitor - $299.99 ($200 off)
27-inch UltraGear OLED Gaming Monitor - $499.99 ($400 off)
34-inch UltraGear OLED Curved Gaming Monitor - $799.99 ($500 off)
27-inch UltraGear OLED Gaming Monitor - $819.99 ($180 off)
39-inch UltraGear OLED Curved Gaming Monitor - $999.99 ($500 off)
32-inch UltraFine 6K Monitor - $1,599.99 ($400 off)

Appliances

24-inch QuadWash Front Control Dishwasher - $549.00 ($250 off)
24-inch FlushFit Top Control Dishwasher - $749.00 ($400 off)
26 cu. ft. Wide Bottom Freezer Refrigerator - $1,699.00 ($500 off)
27 cu. ft. Side-by-Side InstaView Refrigerator - $1,799.00 ($800 off)
Single Unit Front Load WashTower - $1,799.00 ($800 off)
27 cu. ft. Smart InstaView French Door Refrigerator - $1,949.00 ($950 off)
Washer/Dryer LG WashCombo All-in-One - $2,099.00 ($1,200 off)
30 cu. ft. Smart French Door Refrigerator - $2,599.00 ($1,400 off)

If you're on the hunt for more discounts, be sure to visit our Apple Deals roundup where we recap the best Apple-related bargains of the past week.



Deals Newsletter

Interested in hearing more about the best deals you can find in 2026? Sign up for our Deals Newsletter and we'll keep you updated so you don't miss the biggest deals of the season!




Related Roundup: Apple Deals
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Apple TV Shares Trailer for New Heist Series Starring Anya Taylor-Joy

Apple TV today released a trailer for "Lucky," an upcoming crime series starring and executive produced by award-winning actress Anya Taylor-Joy.


The show revolves around a heist gone wrong.

"When a multimillion-dollar heist goes sideways, con artist Lucky (Taylor-Joy) is forced to go on the run," said Apple. "Pursued by both the FBI and a ruthless crime boss, Lucky must fight for her life — and a way out."


The limited series is based on Marissa Stapley's bestselling novel of the same name.

The first two episodes will be released on Wednesday, July 15, and one new episode will follow every Wednesday through August 19.

Apple's streaming service is available on the web and in the Apple TV app across many platforms. U.S. pricing is set at $12.99 per month or $129 per year.Related Roundup: Apple TVTags: Apple TV Service, Apple TV ShowsBuyer's Guide: Apple TV (Don't Buy)Related Forum: Apple TV and Home Theater
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Apple Store Seemingly Opening in Japan's Second-Largest City

As noted by Kazuto Kusakari, new job listings suggest that Apple is preparing to open its first store in Yokohama.

Yokohama is Japan's second-largest city by population, after Tokyo. Apple has 11 stores in Japan already.

Thanks, @hamu_3nd!
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MacBook Neo Disrupts a PC Market in Decline, IDC Says

The global memory shortage that has already squeezed Mac mini and Mac Studio supply is now set to weigh heavily on the broader PC market, with IDC forecasting an 11.3% decline in global shipments for 2026.


According to IDC's Worldwide Quarterly Personal Computing Device Tracker, conditions are expected to worsen progressively through the fourth quarter, when shipments are forecast to fall 20% year-over-year, with no meaningful relief expected before the end of 2027. Average selling prices are rising and PC manufacturers are struggling to maintain full product portfolios.

The first quarter of 2026 offered a deceptively encouraging signal, with shipments growing 3% versus the same period last year, but that strength was largely artificial; both consumer and commercial buyers pulled purchases forward ahead of anticipated price increases and availability constraints. Some of that first quarter momentum is carrying into the second quarter, but the remaining quarters are expected to deteriorate. IDC forecasts average selling price growth of 17% in 2026, and even as memory capacity expands over the next two years, pricing is unlikely to return to 2025 levels. TrendForce previously warned that surging memory and CPU costs could push mainstream laptop prices up by nearly 40% this year.

Against that backdrop, Apple's MacBook Neo has driven stronger-than-expected notebook demand and prompted IDC to revise its notebook forecast upward. Launched in March at $599, the ‌MacBook Neo‌ pairs the A18 Pro chip with 8GB of memory and targets the sub-$700 notebook segment. This market accounts for approximately 75 million units annually, nearly 40% of total notebook volume, which is a tier historically dominated by Windows and ChromeOS devices.

The ‌MacBook Neo‌'s competitive ripple effects cut both ways. IDC said the device is "putting real pressure on the entire PC ecosystem," and expects rivals to respond with new silicon, a more efficient OS from Microsoft, and aggressive promotional pricing. The competitive pressure from the ‌MacBook Neo‌ is providing a partial offset to broader price increases, keeping some low-cost notebook options alive, though the overall average selling price trajectory remains firmly upward.

While rising memory costs are pushing many PC vendors toward higher-priced systems or forcing specification cuts to defend lower price points, Apple has moved in the opposite direction. The memory shortage has had a more direct impact on Apple's higher-end Mac models, with ‌Mac mini‌ and ‌Mac Studio‌ models seeing configuration cuts and significant shipping delays as the company struggles to secure supply.Related Roundup: MacBook NeoTag: IDCBuyer's Guide: MacBook Neo (Buy Now)Related Forum: MacBook Neo
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Apple Announces Europe's First Developer Center

Apple today announced it will open Europe's first Apple Developer Center in Berlin later this year.


The facility joins existing Developer Centers in Bengaluru, Cupertino, Shanghai, and Singapore. Apple said the Berlin center, located in Mitte district, will offer developers throughout Europe in-person sessions, workshops, and one-on-one appointments in multiple languages, with consultation areas and dedicated labs staffed by Apple experts. Apple's vice president of Worldwide Developer Relations, Susan Prescott, said:



The center will host a regular cadence of events covering iOS, iPadOS, macOS, tvOS, visionOS, and watchOS development, aimed at teams of all sizes and at every stage of app development. Apple said the programming is intended to help developers improve the design, quality, and performance of their apps.

Apple noted that storefronts across Europe saw more than 150 million average weekly users in 2025, and that eligible developers can access the App Store Small Business Program, which offers a reduced 15% commission rate for small and individual developers.

The announcement builds on Apple's existing developer investments in Europe, which include the Swift Student Challenge, 19 Apple Developer Academies worldwide, and Apple Foundation Programs in Italy and France. The company pointed out that developers also have access to more than 250,000 APIs across frameworks including HealthKit, Metal, Core ML, MapKit, and SwiftUI.Tags: Developer, Europe, Germany
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Apple Music Classical Announces New Partnership With London's Wigmore Hall

Wigmore Hall Live today relaunches as a digital-only platform in partnership with Apple Music Classical, with all recording royalties passed directly to artists, Gramophone reports.


Wigmore Hall is a prestigious 550-seat concert hall on Wigmore Street in London's Marylebone, widely regarded as one of the world's foremost venues for chamber music, early music, and vocal recitals. Opened in 1901 and noted for its particularly good acoustics, the Grade II listed building hosts over 500 concerts each year. The new partnership with Apple was announced as part of the Hall's 125th anniversary celebrations this year.

Under the artist-first model, Wigmore Hall will cover all production costs for every release and take no share of recording income, passing 100% of royalties received directly to the performing artists. The platform will release four digital-only recordings per year, drawn from live performances at the Hall and developed in close collaboration with artists. Each new Wigmore Hall Live release will premiere exclusively on Apple Music Classical for three months.

Director John Gilhooly said the partnership would allow listeners "to experience Wigmore Hall concerts as close to the live event as possible," citing ‌Apple Music‌ Classical's sound quality as central to that goal.

The first release under the new model is Pianist Boris Giltburg's recording of Beethoven's Piano Sonatas Nos. 4, 8, 9, 20 ("Pathétique"), and 26 ("Les Adieux"), recorded live at Wigmore Hall in February 2025. The Piano Sonata No. 26 in E-flat major is available now, with the entire album to launch tomorrow. The release includes an artist commentary track in which Giltburg offers deeper insight into the repertoire.

‌Apple Music‌ Classical has previously partnered with institutions including the Berlin Philharmonic, Carnegie Hall, the Chicago Symphony Orchestra, the London Symphony Orchestra, the Metropolitan Opera, the New York Philharmonic, and the Vienna Philharmonic. The app launched in most countries in March 2023 and is included with a standard ‌Apple Music‌ subscription at no additional cost, offering access to over five million classical music tracks. It is based on Primephonic, a classical music streaming service acquired by Apple in 2021. Tags: Apple Music, Apple Music Classical, United Kingdom
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Apple Agrees to Hand Over Financial Data to India's Antitrust Regulator

Apple has agreed to hand over financial data to India's competition regulator, in a move that could bring a years-long antitrust case significantly closer to a penalty decision.


According to Reuters, a confidential Competition Commission of India (CCI) order showed that Apple last month agreed to supply its India-specific financials, which the watchdog typically needs to calculate potential fines. At a hearing on May 21, Apple's lawyer asked for a "final extension" until June 25 to file the information, and the CCI granted the request.

The development is an important reversal for Apple, which had previously refused to provide financial information to the regulator. The company argued the case should be paused while it separately challenges India's revised antitrust penalty law, which allows fines to be levied against a company's global revenue rather than just local earnings, which could expose Apple to up to $38 billion in fines.

The CCI repeatedly rejected that argument, saying it required only India financials to begin with and accused Apple of using the parallel court challenge to delay proceedings. Last month, a Delhi High Court judge directed Apple to cooperate with the investigation after the company sought to put the case on hold.

The case dates back to 2021, when a coalition of complainants including Match Group, the owner of Tinder, and the Alliance of Digital India Foundation, which represents Indian startups, filed a complaint regarding App Store policies. The CCI concluded its investigation in 2024, finding that Apple had abused its dominant position in the market for iPhone apps and that the ‌App Store‌ was "an unavoidable trading partner" for developers, who were not permitted to use third-party payment services for in-app purchases.

The case is unfolding as India becomes one of Apple's most consequential markets. The iPhone accounts for 9% of India's smartphone market, up from roughly 2% five years ago, and the company has significantly ramped up manufacturing in the country as part of its broader effort to reduce dependence on China.Tags: Apple Antitrust, India
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Microsoft wants to put AI agents on a short leash

As enterprises race to adopt AI agents across software development workflows, Microsoft is rolling out new controls aimed at keeping the transformation from becoming a security headache.
At its annual developer conference, Microsoft Build, the company unveiled a set of initiatives, including a brand new runtime containment offering, Microsoft Execution Container (MXC), for agentic AI workloads, and improvements to its recently launched multi-agent vulnerability research system MDASH, among others.
“AI is accelerating development and introducing new issues around insecure code, opaque models, data exposure, and compliance,” Aleš Holeček, chief architect at Microsoft Security, said in a blog post. The new tools and capabilities will “give developers clear guidance in real time, scale with the complexity of tasks, and provide security teams with a consistent view across the full lifecycle,” he added.
The idea of sandboxing untrusted code is obviously not new. Containers, VMs, browser sandboxes, and GitHub Codespaces all exist. What’s new is that Microsoft is positioning MXC as a dedicated runtime containment environment for agentic AI workloads, where autonomous agents can take actions, invoke tools, modify code, and access resources.
A lot is said and seen about what could happen when these agents have a little too much autonomy. Coding agents today can access files they shouldn’t, leak secrets, make unauthorized network calls, and execute other unexpected actions.
Microsoft puts AI agents in a security sandbox
Microsoft Execution Containers are a new containment technology intended to place guardrails around autonomous AI agents. It is a policy-driven execution workflow that lets developers specify what an AI agent can access, such as files, networks, resources, credentials, and then enforces those boundaries at runtime.
“MXC is a sandboxed code execution system for running untrusted code (model output, plugins, tools) on Windows, Linux, and macOS,” Microsoft’s official description of the offering reads. “It provides multiple containment backends — from OS-native process sandboxes to full VMs — behind a unified JSON configuration schema and TypeScript SDK.”
Build announcements also included Microsoft’s two new offerings made public in May 2026. These included the Agent 365 SDK, which provides developers with tools to build, deploy, and manage AI agents, and Windows 365 for Agents, a managed environment intended to give autonomous agents dedicated cloud-based workspaces.
Microsoft also revealed its plans for MXC, serving as a security foundation for several agent platforms. Agent 365 will integrate with the framework to bring controls from Defender, Entra, Intune, and Purview to agent environments, while OpenClaw and NVIDIA’s OpenShell are already adopting MXC to run AI agents within isolated execution containers designed to limit risk and improve runtime security.
MDASH moves beyond a research project
While MXC fell under Microsoft’s “secure your agents” initiative at Build, the “secure your code” drive had the company announce updates to its Security Multi-model Agentic Scanning Harness (MDASH). The system claims to use more than 100 specialized AI agents operating across multiple models to identify vulnerabilities, assess exploitability, and reduce false positives before findings reach security teams.
At Build, Microsoft positioned MDASH as part of a broader enterprise security workflow, announcing expanded preview availability and integration with Microsoft Defender.
MDASH was first introduced in May, when it was revealed to have helped uncover multiple Windows vulnerabilities, including critical remote code execution flaws.
Open-source controls aim to govern agent behavior
Microsoft also used Build to introduce two open-source initiatives designed to address the governance challenges around AI agents.
The first, Adaptive Spec-driven Scoring for Evaluation and Regression Testing (ASSERT), is intended to help organizations evaluate agent behavior against defined security and operational requirements.
The second, the Agent Control Specifications (ACS), provides an open standard framework for defining and enforcing governance policies in a portable manner, capable of moving with the agent across different frameworks, platforms, and runtimes instead of being tied to a specific vendor’s technology stack. Together, MXC, MDASH, ASSERT, and ACS sum up Microsoft’s attempt at securing AI models’ entire lifecycle, from the code they generate to the actions they take later.
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AI may finally unlock the cyber budgets CISOs have wanted for years

For nearly two decades, cybersecurity leaders have faced the same reality: No matter how catastrophic the latest breach, ransomware attack, or nation-state intrusion, security spending often struggled against competition with every other business priority.
AI may finally be changing that equation.
The rapid emergence of frontier AI systems capable of autonomous cyber operations — combined with the spread of agentic AI inside enterprises — has created something security leaders rarely enjoy: urgency at the board level.
That urgency was unmistakable at the recent SANS AI Cyber Summit in Washington, DC, where former deputy national security adviser Anne Neuberger urged security leaders to capitalize on the moment.
“We have a moment in time now where the knowledge of how LLMs are enabling attacks … [means] let’s change the culture, let’s operate with speed,” Neuberger said during a keynote address.
Her comments came just days after Bain & Co. warned that many organizations may need to double or even triple cybersecurity investments to prepare for the operational challenges created by advanced AI systems such as Anthropic’s Mythos.
“What I’m seeing is very refreshing,” Nate Rollings, CISO at threat exposure management vendor Zafran Security, told attendees at the recent CSO Cybersecurity Awards and Conference in Nashville.
“Over the last couple years, we’ve seen these budgets for the business and IT to adopt AI … to drive revenue-generating activity,” he noted. “Because of Mythos and Glasswing, there’s been this realization that we haven’t enabled AI as much as we need to in security.” As a result, “we’re seeing this buy-in from the top down to say, ‘Listen, we need to increase some of the budget so we can use AI within security in response to AI threats.”
For many CISOs, the convergence feels less like another hype cycle than a structural shift — especially as organizations rapidly deploy autonomous systems that security teams barely understand how to govern.
How AI is expanding enterprise risk
With the rapid adoption of AI agents, organizations are creating a new operational layer across their enterprises. These systems are increasingly capable of making decisions, initiating actions, accessing sensitive systems, and interacting with other software at machine speed with minimal human oversight.
“Agentic AI is operating in ways we have not seen before in business,” Diana Kelley, CISO at Noma Security, tells CSO. “We’re now protecting a decision and automation layer with AI because agentic AI is making decisions.”
Bernard Brantley, CISO at Corelight, tells CSO that AI is exposing years of accumulated technical debt by collapsing operational boundaries that security teams once relied on to isolate systems, data, and identity domains.
“I’ve got a single potential agent that can go interact with all 50 interfaces available in the company in a sub-second,” he says. “Now we have to think about how much and how widely it proliferates.”
“If we said every person in the company now has three agents, we’re now three orders of magnitude bigger in the landscape that we need to go secure,” Brantley adds.
Existing security architectures were built for human-driven systems, not autonomous agents operating continuously at machine speed, forcing organizations to rethink identity management, monitoring, behavioral controls, and boundaries around AI systems.
“You have to monitor it,” Kyle Lai, president and CISO of KLC Consulting, tells CSO. “If it starts misbehaving, capture it just like a human account.”
Security leaders say one of the biggest emerging challenges is visibility. Many organizations still lack reliable ways to monitor what AI agents are accessing, what decisions they are making, which systems they are interacting with, and whether those actions remain aligned with corporate policy over time.
Unlike traditional software, autonomous agents can dynamically chain together actions across multiple enterprise systems, making it significantly harder for security teams to predict behavior or constrain access using conventional privilege models.
Lai says organizations increasingly recognize that AI agents require the same identity, logging, auditing, and behavioral controls historically applied to employees and privileged users.
At the same time, AI is accelerating operational risk elsewhere inside enterprises. AI-assisted coding systems, for example, are enabling developers to generate enormous amounts of software quickly — but often without fully understanding the resulting security implications.
Risk is accelerating faster than security teams can adapt
Security leaders say generative coding systems are compressing development cycles faster than many organizations’ existing security review processes can realistically keep pace.
Developers are increasingly deploying AI-generated code they may not fully understand, potentially introducing vulnerabilities, insecure dependencies, authentication flaws, and configuration errors into production environments at scale.
“AI is generating a lot of code,” Lai says. “If you don’t manage the vulnerabilities generated by the AI, then it’s going to create more issues because now you’re creating all these vulnerabilities.”
The operational implications are forcing many organizations to rethink cybersecurity less as a defensive IT function and more as a governance layer for autonomous enterprise systems.
That shift is helping elevate cybersecurity discussions into broader conversations surrounding AI adoption, operational resilience, workforce automation, and business risk.
Enterprise leaders are listening in ways they rarely have before
AI is also changing C-suite and boardroom behavior.
For years, many security leaders struggled to persuade boards that cyber risk represented a strategic business issue rather than simply an IT expense.
“We often talk about culture as a defense mechanism to change,” Neuberger said at the SANS summit. “What we’re also seeing is suddenly CEOs talking about LLMs, talking about projects, and concerned about cybersecurity. That’s a massive change.”
That attention matters because security spending has historically surged only when cyber risk becomes tied to broader business transformation.
AI now sits at the center of boardroom conversations about competitiveness, automation, workforce productivity, and digital strategy, giving CISOs a rare opportunity to frame cybersecurity as an operational prerequisite for safe AI adoption.
However, Brantley believes security leaders should resist fear-based messaging and instead position cybersecurity as a business enabler. “The increase in cyber budget should actually be oriented toward delivering business value with respect to the current or strategic AI goal,” he says. “There’s no way to address things at the speed of AI without using AI.”
And what that often means is spending more on AI to tackle AI challenges. “I think [the increased spend] is going to be a blend of, say, 10 new people who are well-versed in this AI problem and potentially a contractor or a vendor who’s got a solution there, and then I will spend the money on the AI tokens to get to that answer.”
The most effective leader-level pitch may be that cybersecurity is becoming the operational foundation that allows organizations to scale AI safely without losing visibility, governance, or control.
“Data poisoning, indirect prompt injection, agents taking rogue actions — that’s all part of the risk conversation at an organization,” Kelley says. “This is a risk conversation about how the business is making decisions.”
Budget requests need a business case
Not everyone believes AI will trigger a cyber spending boom.
Ian Thornton-Trump, CISO at Inversion6, warns that some organizations risk treating AI as a catch-all justification for spending without clearly articulating underlying business risks.
“I think waving the flag of AI is the wrong answer,” Thornton-Trump says. “I would be laughing as an executive at a company if somebody came to me and said, ‘I want to spend a ton of money on AI for cyber.’”
Thornton-Trump argues that boards continue to balance cybersecurity against a long list of competing strategic concerns, including geopolitical instability, climate risk, fraud, supply chain disruption, and rising operational costs.
“Ask for more money, but have a plan,” he says. “Especially a plan that incorporates the fact that you’re not going to get everything you ask for.”
The debate, in other words, isn’t really about whether to spend — it’s about whether security leaders can articulate why clearly enough to be heard.
Whether the advent of AI is enough to boost budgets, it’s clear that frontier AI, autonomous enterprise systems, and executive fear of falling behind competitors have suddenly aligned cybersecurity with core business strategy.
The result would be the most significant shift in enterprise security spending since the rise of cloud computing — not because leaders suddenly fear cyberattacks more, but because they increasingly view cybersecurity as the operational foundation that makes large-scale AI adoption possible.
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CSOonline

Lessons from the Canvas cyberattack

Canvas cyberattack: Who, what, when, how?
What and when?
Over May 6 and 7, 2026, Canvas learning management system (LMS) users were served up a defaced web page in place of the expected login page. The altered web page displayed a warning by the ShinyHunters criminal hacker and extortion group advising of the Instructure compromise. Instructure, a leading educational technology company based in Salt Lake City, Utah, was founded in 2008 and its Canvas LMS was launched in 2011. The ShinyHunters warning gave Instructure a deadline of May 12, 2026, by which to contact them and negotiate a ransom deal in order to prevent the disclosure of Canvas data.
As early as May 1, 2026, ShinyHunters claimed responsibility for the Instructure/Canvas attack that reportedly affected nearly 9,000 educational institutions globally and exposed sensitive information tied to 275 million students, faculty members and staff. Names, email addresses, student identifiers and private communications comprising a staggering 3.65 terabytes were stolen. The timing of the attack was especially damaging since it caused widespread operational disruption during final examinations and temporarily blocked access to coursework, assignments and collaboration systems at colleges and universities worldwide.
Who?
The ShinyHunters criminal hacker group’s name is believed to be derived from the rare Shiny Pokémon video game character. The character is an aspect of the Pokémon video game franchise where Pokémon appear in an alternate color scheme and produce a special sparkle animation when entering battle. Players who try to collect the scarce Shiny Pokémon through in-game strategies are often referred to as “shiny hunters.”  
Ransomware.live, a free and independent website, continuously updates its threat intelligence platform and tracks ransomware groups and their victims. Their statistics on ShinyHunter’s nefarious activities identify staggering statistics. Starting in 2020, ShinyHunters successfully compromised 104 victims across 14 countries and stole trillions of records. Of the 104 victims on the list, 73 are located in the United States and include some big names: Microsoft, Ticketmaster, Google, Cisco Systems, 7-Eleven, CarMax, Amtrak, McDonald’s, Disney/Hulu, Princeton, Harvard and the University of Pennsylvania. AT&T Wireless was compromised more than once as was Instructure.
The Instructure/Canvas attack represents far more than an isolated technology outage – it is a high-profile demonstration of how centralized digital ecosystems, third-party dependencies and modern extortion operations are reshaping enterprise cyber risk. While the attack primarily disrupted the education sector, the lessons emerging from the incident are directly applicable to CISOs, boards of directors, risk management leaders and executive teams across every industry.
How?
Specific technical details about how Canvas was compromised are thin. But on Instructure’s Security Incident & Update page, the company identified a vulnerability with support tickets in their Free for Teacher environment was exploited. In the wake of the attack, Canvas temporarily disabled the Free for Teacher service while they complete a full security review. Free for Teacher is a standalone, no-cost version of the Canvas LMS, allowing teachers to build interactive classes and manage students independently, even if their school does not use Canvas.
Attackers target lower-security environments, legacy systems, support portals, testing infrastructure, API integrations and less-monitored external services because they often possess weaker controls than primary production environments. Organizations often invest heavily in protecting their primary customer-facing infrastructure while underestimating risks associated with support ecosystems, development platforms and auxiliary services.
Lessons learned
Reliance on third-party cloud platforms that aggregate enormous quantities of sensitive data
Educational institutions increasingly rely upon digital ecosystems not only for learning management but also for communication, grading, identity management, scheduling and operational continuity. Similar dependencies exist throughout the private sector. Modern enterprises increasingly centralize operational workflows within cloud-based Software as a Service (SaaS) providers, creating concentrated risk exposure. When these platforms fail, the consequences cascade rapidly.
I recently asked one professor whose university was affected by the incident as to how she was impacted. She replied that the impact was somewhat insignificant since she stores all her class and student information locally in spreadsheets and similar offline formats.
CISOs must reconsider how vendor risk is evaluated. Historically, many third-party risk programs focused heavily on compliance artifacts such as SOC reports, ISO certifications, penetration testing summaries and questionnaire-based responses. While these remain useful, the Canvas incident demonstrates that such controls alone do not guarantee operational security and resilience. Organizations must begin evaluating vendors not only on preventive security controls, but also on their incident response maturity, crisis communications capabilities, architectural resilience, data segmentation strategies, recovery timelines and executive transparency.
As I researched Instructure for this article, I found an impressive website, the Instructure Trust Center. The site displays eleven compliance “badges” – SOC 2 Type 2, SOC 3, PCI, ISO 27001, GDPR, etc. The site also provides access to 74 compliance-supporting documents and 57 FAQ items. To illustrate an earlier point about organizations focusing on primary product offerings rather than risks associated with secondary products and services, I accessed and reviewed Instructure’s ISO 27001 certificate, which is current and expires October 15, 2027.
The certificate states that “The scope of this ISO/IEC 27001:2022 certificate includes Instructure’s products, teams and ISMS managed at its HQ location in Salt Lake City, UT, USA. The in-scope people, processes, technology and locations are defined within the Instructure Scope of the Information Security Management System (ISMS), dated August 1, 2025, and the Statement of Applicability, dated April 16, 2025. The scope of the ISMS implemented by Instructure includes the following elements:
Products: Canvas, Studio, Mastery Connect, Impact, Parchment Award, Parchment Pathways, Parchment. Services:  Parchment Digitary Services (MyEquals and MyCreds), Intelligent Insights, Elevate Standards Note that the Instructure in-scope product list reviewed as part of the ISO 27001 assessment does not include Free for Teachers.
Communications management
Subsequent to the compromise, Instructure took the defaced web page offline and served up a status page referring to the outage as a “scheduled maintenance event.” Then, the following day, Instructure officials declared that the incident had been contained, even though it was at least the third time in the past eight months that Instructure had been breached by ShinyHunters.
Public reporting suggested confusion surrounding the timeline, scope and nature of the compromise. Some institutions reportedly struggled to determine whether their local environments had been breached directly or whether the exposure was isolated to the vendor platform.
For executive leadership teams, this reinforces a critical lesson: cyber incidents are communications crises as much as technical events. Organizations that navigate major cyber incidents most successfully are often those capable of delivering clear, transparent and credible communications early in the response lifecycle.
Delayed or incomplete communication during a crisis often magnifies reputational damage because stakeholders begin filling information vacuums with speculation and distrust.
Economics of attacks
Boards of directors should also take note of the strategic implications surrounding ransomware and extortion economics. Although public details remain incomplete, multiple reports suggested that ransom negotiations or agreements may have occurred between the vendor and the attackers. This reflects a broader trend facing enterprises globally. Ransomware has evolved from operational disruption into multidimensional extortion campaigns involving data theft, reputational pressure, public exposure threats and business interruption leverage.
Business continuity and recovery
Executives must recognize that resilience planning cannot focus solely on technical recovery metrics. Business continuity strategies must incorporate operational timing risk, reputational escalation scenarios, communications management, regulatory exposure and executive decision-making frameworks surrounding extortion events. Organizations frequently underestimate how rapidly cyber incidents evolve into enterprise-wide crisis management situations requiring legal, public relations, compliance, insurance and board-level coordination.
Data minimization
Many organizations continue accumulating vast quantities of historical data without sufficiently evaluating whether long-term retention remains operationally necessary. The larger the centralized data repository, the more attractive the environment becomes for extortion-oriented threat actors. Healthcare and educational institutions are particularly vulnerable since supporting data management systems often contain years of communications, coursework, behavioral data, grading information and identity records. Data retention governance must therefore become a board-level strategic discussion rather than a purely operational records management issue.
Long-term impacts and secondary breach concerns
An often-overlooked concern with ransom/data exfiltration incidents is the potential long-term impact associated with exposed communications data. Even when passwords or financial information are reportedly unaffected, large-scale exposure of communications metadata, institutional relationships and personal identifiers creates significant downstream risk. Threat actors can leverage such information for future phishing campaigns, social engineering operations, credential harvesting and identity fraud. Cybersecurity leaders must think beyond immediate containment and evaluate how stolen information may fuel future attacks months or even years later.
What’s next?
In a letter dated May 11, 2026, from United States Congressman Andrew R. Garbarino, Chairman of the Committee on Homeland Security, requested Steve Daly, Chief Executive Officer Instructure Holdings, Inc., to participate in a briefing with the Committee, to be scheduled at a mutually convenient time no later than Thursday, May 21, 2026.
Stay tuned!
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CSOonline

Weedhack Attacks Minecraft Users, CountLoader Hits 86K, Miners Spread via Pirated Content

Cybersecurity researchers have flagged a new campaign targeting Minecraft players via YouTube to spread malware capable of gaining control of victims' systems. The Minecraft-focused malware-as-a-service (MaaS) campaign has been codenamed Weedhack by McAfee Labs, stating the activity has been active since January 2026 and impersonates Minecraft clients and mods to infect users. In all, 3820View the full article
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The Ultimate Guide to Avoiding Critical DevOps Beginner Mistakes in Your Career

Introduction
Entering the world of DevOps can feel like walking directly into a massive storm of technologies, terminology, and confusing industry jargon. New learners are often told they must master automation, cloud computing, continuous integration, containerization, and monitoring all at the same time. This overwhelming amount of information creates immediate confusion, leaving many capable individuals feeling entirely lost before they even write their first configuration script.
The primary issue is not a lack of available documentation or learning material. Instead, it is the sheer volume of tools and buzzwords thrown at beginners. When you try to absorb everything simultaneously, you lose confidence, experience severe burnout, and start questioning whether you are cut out for a career in cloud engineering.
To build a sustainable career, you must recognize that DevOps is a cultural philosophy and an operational methodology, not just a massive checklist of software programs. By identifying and avoiding early learning traps, you can save months of wasted effort. Aspiring professionals can find a structured, clear, and highly practical guide through the educational frameworks provided by DevOpsSchool, which helps untangle this chaotic landscape. Focusing on systematic progression ensures that you spend your time building stable skills that production environments actually require.
Why Beginners Struggle With DevOps
The initial barrier to entry in DevOps is completely different from traditional software development or system administration. When learning to code, your focus is primarily on syntax, logic, and a single programming language. In contrast, DevOps requires you to understand how code moves, breaks, scales, and communicates across an entire infrastructure network.
Too Many Technologies
Beginners often look at industry landscape maps and see hundreds of logos ranging from Jenkins and GitHub Actions to Terraform, Ansible, Docker, and Kubernetes. Without guidance, a novice will try to install and learn five different tools in a single weekend. This leads to a superficial understanding where you know the names of tools but have no idea why one is chosen over another in a real architectural setup.
No Clear Roadmap
Many self-taught learners pull learning materials from random blog posts, outdated video playlists, and conflicting forum threads. One guide might tell you to master deep Linux administration, while another tells you to skip it entirely and deploy code directly to a managed cloud platform. This lack of a standardized sequence leaves massive gaps in your core technical knowledge.
Lack of Practical Learning
Watching an instructor configure an infrastructure pipeline on a screen is completely different from troubleshooting that same pipeline when an unexpected permission error occurs. Beginners frequently fall into the habit of passive consumption. They watch hours of video tutorials without ever opening a terminal, creating a false sense of competence that falls apart completely during a technical job interview.
Overview Table: Biggest DevOps Mistakes Beginners Make
MistakeImpact on LearningLearning too many tools at onceCauses severe cognitive overload, surface-level knowledge, and rapid burnout.Ignoring Linux basicsPrevents you from understanding server management, container runtimes, and permissions.Skipping networking fundamentalsMakes troubleshooting application deployment errors and cloud routing impossible.Avoiding scriptingLimits your capacity to build automated pipelines and custom infrastructure fixes.Watching tutorials onlyCreates a false sense of progress without developing real troubleshooting skills.Learning Kubernetes too earlyLeads to extreme confusion regarding orchestration without knowing core container behavior.Ignoring cloud fundamentalsResults in poorly optimized, insecure, and highly expensive cloud resource deployments.Not building projectsLeaves you without a visible portfolio to prove your engineering capabilities to employers.Comparing yourself with expertsDamages your learning confidence and creates unnecessary psychological anxiety.Chasing certifications onlyProduces a resume that passes initial filters but fails the practical technical round. Mistake #1: Trying to Learn Every Tool at Once
The urge to learn every tool listed on a job description is the quickest path to failure for a beginner. When you try to study Jenkins, GitLab CI, Terraform, Ansible, Chef, and Puppet all within your first month, your brain cannot form the logical connections required to understand the underlying patterns of automation.
The Overwhelm Problem
Every tool has its own syntax, configuration language, and operational logic. Trying to memorize all of them simultaneously means you never truly master the core concept of why these tools exist in the first place. You end up knowing how to copy and paste code snippets, but you remain entirely unable to design a basic automation workflow from scratch.
Realistic Example
Consider a beginner who spends three days trying to configure a Jenkins pipeline, gets frustrated, switches to GitLab CI because of a trendy social media post, and then drops both to try GitHub Actions. At the end of three weeks, this person does not know how to build a continuous integration pipeline. They only know how to write three completely different, incomplete configuration files that do absolutely nothing.
Learning Lesson
Pick a single tool for each category of the lifecycle and stick with it until you understand the underlying concepts. If you are learning Continuous Integration, master GitHub Actions thoroughly. Once you understand jobs, steps, runners, environments, and secrets in one tool, translating that exact knowledge to Jenkins or GitLab CI becomes a straightforward task that takes days rather than months.
Mistake #2: Ignoring Linux Basics
Many modern beginners want to jump directly into writing complex infrastructure-as-code files or configuring microservice meshes. However, the vast majority of enterprise infrastructure, cloud servers, and containerized deployments run on top of Linux operating systems. Skipping this foundation is like trying to build a skyscraper on a foundation of sand.
Terminal Confidence Matters
If you are afraid of the command line interface, you cannot succeed in a DevOps role. You must be comfortable navigating directories, managing file permissions, analyzing system logs, and monitoring CPU and memory usage using native terminal utilities. Relying on graphical user interfaces will completely limit your capabilities as an engineer.
Realistic Example
A junior engineer deploys an application inside a Docker container, but the application immediately crashes with a vague data storage error. Because the engineer skipped learning Linux file permissions and ownership standards, they spend two full days rewriting the deployment manifest. In reality, the issue was simply a basic directory ownership mismatch that could have been identified and resolved in two minutes using standard Linux commands.
Learning Lesson
Spend your earliest learning weeks entirely inside a Linux terminal. Master standard command-line tools, learn how the file system structure works, understand how processes are managed, and get comfortable configuration files using text editors like Vim or Nano. This foundational comfort will pay massive dividends throughout your entire career.
Mistake #3: Skipping Networking Fundamentals
An incredibly common pitfall for DevOps beginners is treating networking as a separate discipline that only network engineers need to understand. In modern cloud architecture, every single application deployment involves microservices communicating across virtual routers, firewalls, load balancers, and gateways.
IP, DNS, and Ports
You cannot properly configure a secure infrastructure environment if you do not understand how IP addresses are assigned, how Subnets segment traffic, how DNS resolves domain names to server endpoints, and how specific network ports regulate application access. Without these concepts, your deployments will consistently suffer from communication failures.
Practical Scenario
A beginner configures a web server and a private database instance on a cloud provider. They cannot figure out why the web application displays a connection timeout error whenever it tries to fetch data from the database. They assume the database software is broken and reinstall it multiple times. A foundational understanding of networking would have revealed that the security group or firewall was blocking inbound traffic on the database port.
Learning Lesson
Dedicate structured time to learning core networking protocols. Make sure you can comfortably explain the OSI model, TCP/IP handshake, public versus private IP subnets, CIDR blocks, and how HTTP/HTTPS requests travel over the internet. This knowledge turns guesswork into systematic troubleshooting.
Mistake #4: Avoiding Scripting
DevOps is centered heavily on the concept of automation. If you avoid learning how to write scripts, you are essentially refusing to learn how to automate. You cannot rely on manually clicking buttons in a cloud console or running individual terminal commands one by one for every single deployment.
Bash Basics Matter
You do not need to be an expert software developer who writes complex algorithms, but you absolutely must understand how to write clean, predictable scripts. Bash scripting is the native language of automation inside Linux environments, and it is the glue that holds many enterprise deployment pipelines together.
Realistic Example
Imagine a team that needs to back up log files from fifty servers every single night at midnight. An engineer who avoids scripting will attempt to log into each server manually to copy the files, which is an error-prone process that consumes hours of time. An engineer with basic Bash scripting knowledge will write a fifteen-line script, attach it to a cron job, and automate the entire operational task permanently.
Learning Lesson
Start by automating your own daily computer tasks. Write basic Bash scripts to automate local directory backups, clear out your system’s temporary storage, or batch rename configuration files. Once you understand variables, loops, conditional statements, and exit codes in Bash, consider learning Python to handle more complex automation and API integration challenges.
Mistake #5: Watching Tutorials Without Practicing
It is highly comforting to sit back and watch a highly skilled professional build a complex, automated deployment infrastructure in a pre-recorded video. Everything works flawlessly for the instructor because they have edited out the errors and configuration mistakes. This creates a psychological trap known as tutorial hell.
The Passive Learning Problem
Passive consumption of tech videos tricks your brain into believing you understand a topic when you actually only understand the explanation of the topic. The moment you close the video, open a blank text editor, and try to replicate the setup on your own machine, you will immediately hit walls that the video did not prepare you to face.
Practical Scenario
A learner watches a twelve-hour comprehensive course on infrastructure automation. They feel incredibly accomplished, add the tool to their LinkedIn profile, and apply for positions. In the technical assessment round, they are asked to debug a broken deployment script. Because they never manually ran the commands or encountered the errors themselves, they have no idea how to interpret the log data or fix the bug.
Learning Lesson
Enforce a strict practice rule for your study routine. For every single hour of video content or technical documentation you consume, spend at least two hours manually typing out code, running commands inside your local terminal, breaking the setup on purpose, and reading the resulting error messages to figure out how to repair it.
Mistake #6: Learning Kubernetes Too Early
Kubernetes is one of the most popular, highly discussed technologies in the modern tech ecosystem. Because of this massive industry hype, many beginners believe they must learn Kubernetes immediately during their first few weeks of DevOps study. This is a massive tactical mistake that leads directly to confusion and frustration.
The Docker First Approach
Kubernetes is a container orchestration engine. It is designed to manage hundreds or thousands of containerized applications running across vast server clusters. If you do not completely understand what a container is, how container runtimes operate, how storage volumes mount, and how container networking functions inside Docker, Kubernetes will look like complete magic that you cannot comprehend.
Realistic Example
A beginner sets up a local Kubernetes cluster using a minified local development tool. They try to deploy an application but get an image pull error. Because they skipped learning how Docker builds images, tags versions, and pushes them to registries, they spend hours troubleshooting the orchestration cluster config when the issue was simply an incorrectly built container image.
Learning Lesson
Completely ignore Kubernetes when you are starting out. Focus your energy entirely on Docker. Learn how to write clean Dockerfiles, manage container life cycles, use multi-stage builds to optimize image sizes, and orchestrate multi-container local environments using Docker Compose. Only when you can comfortably manage containers manually should you progress to cluster orchestration with Kubernetes.
Mistake #7: Ignoring Cloud Basics
With the rise of modern cloud computing, almost all DevOps pipelines interface directly with providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). A common mistake among beginners is treating the cloud as a magical, infinite server room where you do not need to understand the underlying infrastructure.
Cloud Fundamentals Matter
Deploying applications in the cloud without understanding resource provisioning, security access parameters, and cost metrics leads to highly unstable configurations. You must understand that cloud resources are simply virtualized representations of real physical servers, storage disks, and network cables located in global data centers.
Practical Example
A beginner sets up a test application deployment on a cloud provider. Eager to make it work, they configure the security settings to allow all incoming internet traffic on every single port and provision massive enterprise-grade database instances. A month later, they are shocked to receive a massive cloud billing statement and find out their open database instance was targeted by malicious automated scanning bots.
Learning Lesson
Learn the foundational concepts of at least one major cloud provider before trying to automate it. Understand identity and access management configurations, learn how to monitor operational costs, use free-tier resources carefully, and master the creation of virtual private clouds. Treat the cloud provider as a serious infrastructure environment, not an unregulated playground.
Mistake #8: Not Building Real Projects
Reading technical textbooks and passing multiple-choice quizzes will never substitute for the real engineering experience gained by building functional systems. Many beginners complete numerous isolated tutorial exercises but never tie those skills together into a unified project repository that they can showcase.
GitHub Portfolio Importance
When a hiring manager looks at a candidate’s profile, they want to see a history of code commits, clear documentation files, and working examples of automated infrastructure. An empty GitHub profile tells an employer that you might possess theoretical knowledge but lack the practical initiative to build real solutions.
[Local Code Commit] │ ▼ [GitHub Actions Pipeline] ──► [Runs Tests] ──► [Builds Docker Image] │ ▼ [Target Cloud Server] ◄── [Deploys Container] ◄── [Pushes to Registry] Realistic Example
A candidate applies for an entry-level DevOps role with a resume listing every tool under the sun. However, they cannot provide a single link to a working repository. Another candidate applies with a shorter list of skills, but includes a clean GitHub link demonstrating a fully automated workflow that builds a web app container and deploys it cleanly to a cloud instance. The second candidate will almost always secure the position.
Learning Lesson
Stop building disconnected, throwaway test setups. Instead, design a single comprehensive project that evolves along with your learning path. Start by writing a basic web application. Then, containerize it using a Dockerfile. Next, build a continuous integration pipeline to test it automatically. Finally, write an infrastructure script to deploy it safely to a cloud provider. Document this entire evolutionary process clearly within your repository README file.
Mistake #9: Comparing Yourself With Experienced Engineers
It is easy to scroll through professional social networks or technical tech blogs and feel completely inadequate. You see Senior DevOps Architects discussing intricate multi-region architectures, complex zero-downtime deployment strategies, and massive automated scaling systems, and you start to feel like you will never catch up.
Confidence Damage
Comparing your day-one understanding with someone else’s year-ten reality creates massive psychological imposter syndrome. You lose sight of your own daily progress and begin rushing through critical foundational topics just to try and understand high-level discussions that do not apply to your current learning stage.
Relatable Scenario
A beginner sees an online discussion where experts are arguing about the niche architectural differences between two highly advanced service mesh tools. The beginner panics, drops their current study plan on basic file administration, and spends an entire week reading complex architectural whitepapers that they cannot understand, leaving them feeling completely discouraged.
Learning Lesson
Understand that every single senior engineer, principal architect, and technical lead started exactly where you are standing right now. They once struggled with basic terminal commands and spent hours debugging simple configuration typos. Measure your educational success solely by comparing your current skills today against your skills from one week ago.
Mistake #10: Chasing Certifications Without Skills
The technical training market is heavily saturated with marketing campaigns urging beginners to collect as many cloud and DevOps certifications as humanly possible. This creates an unhealthy phenomenon where learners spend months memorizing specific exam questions and answers just to obtain a digital badge.
Practical Learning Matters
Certifications can help structure a curriculum and assist your resume in passing automated human resource filters. However, a certification badge holds zero value if you cannot explain the fundamental technology during a live technical screening panel. Employers are looking for problem-solving engineering capabilities, not memorization skills.
Realistic Example
An individual passes three separate cloud certifications in a row by using online practice exams and brain dumps. They proudly display the credentials on their profile and secure an initial technical interview. During the interview, the lead engineer asks them to explain how they would diagnose a web application server that is running out of disk space. The candidate cannot answer because they never actually spent time managing real systems.
Learning Lesson
Use certification blueprints purely as a structural guide for what topics to study, but do not prioritize the actual exam over real hands-on implementation. For every domain topic covered in an exam syllabus, spend days building practical implementations until you can explain the core architecture fluidly without looking at a study guide.
Real-World Example: Beginner Learning DevOps the Wrong Way
To see how these learning mistakes play out in real life, let us examine a common learning scenario that results in burnout and failure.
The Fragmented Learning Path
John decides to transition into DevOps. He reads that Kubernetes is highly valuable, so he immediately purchases an advanced orchestration course. On day one, the course instructs him to launch a multi-node cluster using a command line tool. John is working on a Windows machine and has never used a terminal interface before. He gets stuck for four hours just trying to configure his local system paths.
The next day, John sees a trending tech article declaring that a brand-new infrastructure automation tool has made all older tools obsolete. He abandons his Kubernetes course entirely and spends three days trying to read the documentation for this new tool, but fails because he does not understand basic networking configurations or server provisioning.
The Negative Outcome
After a month of chaotic, unorganized study, John has spent substantial money on courses, watched parts of forty different video tutorials, and generated hundreds of configuration errors that he could not resolve. He feels entirely overwhelmed, concludes that DevOps is far too difficult for anyone without a computer science degree, and completely gives up on his career transition goals.
Real-World Example: Beginner Learning DevOps Successfully
Now, let us examine an alternative approach based on clear planning, steady progression, and a commitment to understanding fundamental core concepts.
The Structured Learning Path
Sarah decides to transition into DevOps with a patient, organized mindset. She ignores the trending industry hype and starts by installing a local Linux distribution on a virtual machine. She spends two weeks learning how to move files, manage system users, and check system resource levels entirely through the command line terminal interface.
Once she feels completely confident inside the terminal, she dedicates a week to studying basic networking concepts, focusing on how data packets travel across ports and subnets. With this solid foundation established, Sarah begins studying Docker. She spends three weeks manually building basic container images, running containers locally, and troubleshooting common application port mismatches.
[Weeks 1-2: Linux CLI] ──► [Week 3: Networking] ──► [Weeks 4-6: Docker] ──► [Weeks 7-9: CI/CD Pipelines] The Positive Outcome
Sarah does not rush into high-level tools like Kubernetes or complex multi-cloud deployments. Instead, she connects her foundational skills together step by step, creating a clear continuous integration pipeline on GitHub to automate her Docker configurations.
By taking things slowly, Sarah avoids burnout, builds genuine technical confidence, and creates a visible portfolio of small, functional projects that clearly demonstrate her engineering progression to prospective employers.
Common Beginner Misunderstandings
When starting out, it is highly beneficial to clear away the common myths that cause unnecessary anxiety and hinder your professional growth.
The belief that you must master every single concept immediatelyDevOps is an incredibly vast, constantly evolving field. No engineer knows everything. Success lies in mastering core structural fundamentals, learning how to research documentation efficiently, and adapting to changes smoothly over time. The idea that DevOps requires expert software engineering coding skillsYou do not need to write advanced machine learning models or complex backend data structures. Your primary focus is on writing clear automation scripts, defining configurations, and building reliable delivery pipelines. The misconception that encountering configuration errors means you are failingErrors are the primary mechanism through which you learn. An experienced DevOps professional is simply an engineer who has broken systems thousands of times and figured out how to fix them. The assumption that a certification automatically guarantees a high-paying jobCertifications open initial doors, but your hands-on problem-solving skills, portfolio projects, and fundamental technical knowledge are what actually secure employment offers. Best Practices for Learning DevOps Successfully
To ensure your learning journey is efficient and rewarding, implement this practical framework into your daily technical study routine:
Focus on a single topic at a timeNever move to a brand-new automation tool until you thoroughly understand the fundamental technical concept behind the one you are currently practicing. Commit to hands-on practice every single dayConsistency is infinitely more valuable than intensity. Spending one focused hour typing commands in a terminal every day builds better muscle memory than studying for eight hours straight once a week. Prioritize core technical fundamentalsEnsure your understanding of Linux operations, basic networking, and containerization is absolutely rock-solid before attempting to learn complex orchestration tools. Document your entire learning journey publiclyWrite clear, accessible summary notes, create helpful architectural diagrams, and share your technical troubleshooting fixes on a personal blog platform or GitHub repository. Role of DevOpsSchool in Beginner DevOps Learning
Navigating this vast educational landscape completely alone often results in wasted time and directionless study habits. This is where a structured, expert-led framework becomes incredibly valuable for long-term career growth.
Programs offered by DevOpsSchool address these early challenges by removing the guesswork from your study plan. Instead of pushing beginners directly into advanced, high-level automation toolsets, the curriculum emphasizes building strong foundational skills in Linux administration, network architecture, and scripting languages first.
By focusing heavily on practical labs and realistic technical scenarios, learners are guided away from passive tutorial consumption and pushed toward real engineering problem-solving. This methodical approach ensures that you do not just memorize commands, but actually learn how to architect, optimize, and troubleshoot production-grade delivery pipelines, preparing you effectively for real-world enterprise environments.
Career Importance of Learning DevOps Correctly
Acquiring a deep, structurally sound foundation opens up a diverse array of professional career opportunities across the modern technology landscape.
Junior DevOps Engineer
Focuses on maintaining existing continuous integration pipelines, monitoring infrastructure health alerts, and automating basic operational tasks under senior guidance.
Cloud Engineer
Specializes in provisioning secure cloud environments, managing virtual networks, optimizing cloud resource usage, and ensuring system availability.
Site Reliability Engineer (SRE)
Concentrates heavily on system reliability, automated recovery workflows, deep performance monitoring, and managing complex production incidents.
Platform Engineer
Designs and maintains internal developer platforms, templates, and tools to help internal software development teams deploy code safely and efficiently.
Automation Engineer
Focuses on replacing repetitive manual human tasks with highly reliable automated configuration management systems and custom deployment scripts.
Industries Hiring DevOps Professionals
Virtually every modern industry that relies heavily on digital software applications requires skilled engineering professionals to manage their systems.
SaaS Platforms
Cloud-native software companies require continuous, rapid deployment cycles to ship features to millions of global users multiple times per day without causing system downtime.
Banking & Finance
Financial institutions require highly secure, automated infrastructures that strictly comply with international regulatory frameworks while handling millions of transactions safely.
Healthcare
Medical platforms use automation to manage sensitive patient data environments securely, ensuring high availability, deep encryption standards, and strict system privacy compliance.
E-Commerce
Digital retail platforms rely heavily on automated scaling infrastructure to smoothly handle massive traffic spikes during global holiday shopping events without system crashes.
Telecom & Enterprise IT
Traditional telecommunications providers and massive enterprise corporations use modern infrastructure practices to modernize legacy setups and streamline massive global communication networks.
Future of DevOps Learning
As technology progresses, the methods we use to study and implement automation practices are evolving significantly.
AI-Assisted Learning
Modern artificial intelligence tools are changing how engineers learn to write code and debug systems. Beginners can leverage AI to explain obscure error messages, generate basic configuration boilerplates, and suggest optimizations for infrastructure scripts, turning AI into an interactive, round-the-clock technical study assistant.
Cloud-Native Growth and Platform Engineering
The industry is moving steadily away from complex custom manual server configurations and toward highly standardized cloud-native ecosystems. Platform engineering is emerging as a dominant discipline, focused on building robust, reusable internal application pathways that eliminate operational friction for software developers.
The Rise of DevSecOps
Security is no longer treated as a final, disconnected step at the very end of a software development cycle. Modern engineering methodologies require integrating automated security scanning, vulnerability analysis, and compliance checks directly into every stage of the continuous delivery pipeline from day one.
FAQs (15 Questions)
Why is DevOps difficult for beginners?
DevOps feels difficult because it requires a broad understanding of multiple operational disciplines simultaneously, including development, systems administration, networking, and security, rather than focusing on a single programming language syntax.
Should I learn Linux first?
Yes, learning Linux first is absolutely essential. The vast majority of production infrastructures, cloud computing servers, and container environments run natively on Linux, making command-line fluency a critical core skill.
Is Kubernetes hard to learn?
Kubernetes possesses a steep learning curve because it manages complex clustered environments. It becomes drastically easier to understand if you master container fundamentals using Docker before attempting cluster orchestration.
Do I need advanced coding skills for DevOps?
No, you do not need to be an expert software developer. You need a solid understanding of logic, variables, and loops to write reliable configuration files and automation scripts in languages like Bash or Python.
How do I practice DevOps skills locally?
You can practice locally by installing a virtual machine manager or lightweight container tools on your personal computer, allowing you to run isolated Linux environments and build test networks entirely for free.
Are certifications important for landing a job?
Certifications help pass initial resume screening filters and structure your study path, but they must be backed up by a strong portfolio of practical projects and clear conceptual knowledge during technical interviews.
How long does it take to learn DevOps fundamentals?
With structured, consistent study, it typically takes six to nine months for a beginner to build a reliable, comfortable understanding of core foundational concepts and automation tools.
Can freshers or career switchers get a job in DevOps?
Yes, freshers and switchers can secure entry-level roles by demonstrating solid foundational skills in Linux, basic networking, git workflows, and presenting a portfolio of clean, working projects on GitHub.
What programming language should a DevOps beginner learn?
Beginners should start by mastering Bash scripting for basic operating system automation, and then learn Python due to its readability and widespread use in cloud APIs and automation frameworks.
What is the difference between Agile and DevOps?
Agile is a project management methodology focused on streamlining how software development teams plan and write code, while DevOps focuses on automating how that code is safely deployed, monitored, and maintained in production.
Why do deployment pipelines fail so frequently?
Pipelines usually fail due to minor syntax mistakes in configuration files, incorrect permission settings, network port blockages, or version mismatches between local environments and cloud servers.
Should I learn AWS, Azure, or GCP first?
Start with AWS as it holds the largest global market share and offers extensive documentation, but focus on understanding the underlying concepts of cloud computing, which translate easily to any provider.
What is Infrastructure as Code?
Infrastructure as Code is the practice of managing and provisioning server networks, storage disks, and cloud resources using readable, automated configuration files rather than manually clicking buttons in web consoles.
How does monitoring fit into the learning path?
Monitoring is critical because it provides visibility into application performance. Learning how to read logs and analyze system resource alerts helps you proactively detect and fix infrastructure failures before they impact users.
Can I learn DevOps without knowing anything about cloud computing?
You can learn local administration, basic scripting, and local containerization on your own machine, but you must eventually learn cloud basics to understand how modern enterprise systems scale globally.
Final Thoughts
Building a successful career in cloud engineering requires patience, continuous curiosity, and a deep respect for foundational technical concepts. It is easy to get caught up in market hype and try to rush through high-level tools, but true engineering competence is built step by step, command by command, and error by error.
Do not be discouraged by configuration errors or complex terminology. Mistakes are an essential part of the educational process, and every senior architect you look up to was once a confused beginner trying to understand their very first terminal window. Stay disciplined, practice with real setups every day, focus on the core principles of system administration, and remember that long-term consistency will always win over speed.
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Anthropic grants Project Glasswing access to 150 more companies, with a focus on critical infrastructure

Anthropic on Tuesday announced that it was adding 150 more companies to its Project Glasswing AI-based vulnerability hunting initiative, with a particular focus on critical infrastructure companies including those involved in “power, water, healthcare, communications and hardware.”
Analysts and security vendors agreed that the move is a positive step, noting that the more companies involved in bug identification, the better. But the bigger background issue is a practical one: the bottleneck problem. 
If Project Glasswing, and similar projects from other major AI vendors, increase the stream of vulnerability identifications by 10 or more times, will vendors be able to triage and patch them in a timely manner? Vendors have historically been notoriously slow to patch known security issues. Microsoft, for example, recently argued with a security researcher who went public with holes because he felt that Microsoft was too slow in addressing them. 
And even if those vendors can keep up, are enterprise SOCs going to be able to keep up with the avalanche of patches? And if extensive automation is deployed to generate those patches, will CISOs trust them enough to let them be deployed without manual verification? Trust is not a common CISO trait.
“What each partner has in common is that a successful attack on their codebase could be catastrophic. For most partners, we estimate that a major attack could affect more than 100 million people, with important ramifications for both global and national security,” Anthropic said in its blog post announcing the new participants. “This expansion is the next step toward our long-term goals: for AI to make all software more secure, and for us to help the industry adjust to how AI could change many of the core assumptions of cybersecurity.”
Glasswing was announced on April 7 and was initially supported by AWS, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks. Okta later confirmed that it was also involved. 
The patch bottleneck
The bottleneck problem is a difficult one to solve, given that even the largest vendors can only cost-justify so many resources for patching security holes and distributing those patches.
“The biggest issue is adaptability: once a vulnerability or weakness is found, defenders have to validate it, prioritize it, and fix it before attackers can operationalize the same insight. And that validation step matters,” said Tom Findling, CEO of Conifers.ai. “While testing the tool ourselves, we saw a lot of false positives, which means organizations cannot simply treat every finding as immediately actionable. They need the ability to separate signal from noise quickly, then adapt their processes, engineering workflows, and patching pipelines around the real issues.”
“The most important metric for organizations to track may not just be how many vulnerabilities are found, but how long it takes them to adapt once a credible issue is identified. For some organizations, that adaptation cycle can still take months,” he added. “Reducing that time-to-adapt is what will determine whether AI-assisted vulnerability discovery actually improves defense or just increases the speed and volume of security noise.”
A remediation problem
Justin Greis, CEO of consulting firm Acceligence, agreed that the Glasswing expansion may simply demonstrate to CISOs how much the security hole problem is shifting, not shrinking. 
“It’s no secret that cybersecurity has been treated as a vulnerability discovery problem. AI is proving that it was really a remediation problem all along. The industry already struggles to validate, prioritize, patch, test, and deploy fixes fast enough. It may even be worse if security teams own the vulnerability identification and the IT teams, or the business teams, own the patching itself,” Greis said. “If AI can identify vulnerabilities 10x or 100x faster than humans, the bottleneck simply moves downstream. Organizations may soon find themselves in the uncomfortable position of knowing about far more vulnerabilities than they can realistically address. AI is turning cybersecurity from a visibility problem into an execution problem.”
Greis added a frightening prediction: “AI could make organizations simultaneously more secure and more overwhelmed, if that’s possible. They’ll have unprecedented visibility into their risk, but they’ll also discover just how large that risk really is.”
Trust required
Grace Trinidad, research director for AI security at IDC, said the bottleneck problem at the enterprise needs to be addressed via extensive automation. But given the lack of trust by cybersecurity staff, vendors must have a rigorous method for producing a numerical confidence score for every patch. 
“Having a confidence score accompanying these patches is a new concept. There must be an ability of the enterprise to identify, triage and address the vulnerabilities that are specific to their environment,” Trinidad said. “We are learning a skillset that we are not ready for: How do we trust automated technologies? Given that we are having to move at this speed, that trust is going to get broken. Confidence scoring is a discipline that needs transparency. Don’t make the confidence [explanation] so complicated that you can’t explain it to a human being.”
Trinidad also noted that the Anthropic announcement pointed out that each of the 150 new participants, in Anthropic’s phrasing, “will need to meet our security requirements before they gain access.”
Trinidad said the security requirement claim doesn’t build confidence, because “nobody knows what those security requirements are.”
One possible solution is for security vendors to use high-trust third parties so that they are not seen as ‘grading their own homework’. Enterprise software vendor Workday is using a similar third-party approach, relying on trusted services that use public standards such as Mitre ATLAS to validate the security and compliance of AI agents using its platform. Workday’s approach deals with security checks and not reliability scores, but the idea could potentially be tweaked. 
Expansion creates security concerns
Carmi Levy, an independent technology analyst, was more skeptical about what Glasswing will ultimately be able to accomplish by adding 150 more participants.
“The entire point of Project Glasswing was to allow Anthropic to work closely with a small, fully vetted group of vendors to develop stronger defenses against the cybersecurity risks posed by what was, and is, an entirely new LLM class that would otherwise pose unacceptable risks to existing protective technologies and protocols,” Levy said. “Expanding access into the hundreds may very well bring in more minds to build better defensive measures, but it simultaneously introduces significant concerns around potential leaks. And this from a company that has already reported two leaks involving this same model.”
Levy added, “in an ideal world, Anthropic would announce alongside this major expansion a parallel effort to tighten internal security protocols to ensure the code doesn’t fall into the wrong hands. Bringing in a much larger cohort of researchers signals to potential attackers that they will soon have a larger pool of potential targets, and fails to allay fears of future breaches.”
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Two-year old Oracle WebLogic Server vulnerability is being exploited

US federal government departments have been given until Thursday to patch a two-year old high severity vulnerability in Oracle WebLogic Server that could allow an unauthenticated attacker to access critical data.
The vulnerability, CVE-2024-21182, was added Monday to the Cybersecurity and Infrastructure Security Agency’s (CISA) Known Exploited Vulnerabilities (KEV) catalog, giving federal Oracle admins a mere four days to plug the hole.
Supported versions that are affected are 12.2.1.4.0 and 14.1.1.0.0.
While the KEV is aimed at US federal departments, inclusion of a vulnerability on the list should be taken as a warning to the private sector as well.
At the time it was discovered, this vulnerability was rated 7.3 on the CVSS scale, nowhere near the 9+ rating that many infosec pros would see as signaling a need for immediate attention.
However, Robert Enderle, a consultant who heads the Enderle Group, said the inclusion of this vulnerability in the KEV now means that CISA has recently confirmed that threat actors are actively weaponizing it.
“To make the CISA KEV means that we’re seeing active exploitations,” agreed Tyler Reguly, Fortra’s associate director of security R&D. “Given that this CVE was patched by Oracle in the July 2024 Critical Patch Update (CPU), I would expect most admins to have patched this by now, particularly since it is a WebLogic vulnerability and, prior to the addition of this CVE, there were already a dozen WebLogic vulnerabilities listed in the KEV catalog.”
Older vulns under exploit
Reguly also had an observation about how fast vulnerabilities are added to the KEV. Based on a cursory review, he figured only about 41% of CVEs in the list were added during the same year they were released. Looking at release year + 1, that goes up to about 58%. That still means that, surprisingly, more than 40% of the CVEs added to the CISA KEV catalog are added two or more years after they are released. “I suppose it makes sense that it [the two-year-old Oracle hole] is just popping up now, if you consider that an organization that hasn’t patched their systems in multiple years is likely an easier target than an organization that patches regularly. After all, regular patching probably implies a more security-conscious environment.”
Asked for comment on why this vulnerability is being added two years after its discovery, a CISA spokesperson referred to the department’s webpage explaining criteria for including bugs in the catalog, which says the list is of vulnerabilities that have been exploited in the wild. The spokesperson didn’t answer a question about how many federal servers were still unpatched after so long.
Oracle WebLogic Server is a unified and extensible platform for developing, deploying, and running enterprise applications in Java, on-premises and in the cloud. It’s fully supported on Kubernetes, and enables users to migrate and efficiently build modern container apps with comprehensive Java services. It short, it’s a vital piece of middleware that can host sensitive corporate data.
Not surprisingly, threat actors are eager to exploit any vulnerabilities of this type. In 2019 it was reported that threat actors were scanning for WebLogic servers vulnerable to a new method of bypassing protections that Oracle had fixed the year before.
Earlier this year, security firm CloudSek set up a honeypot to study threat actor response to a newly discovered and extremely serious WebLogic Server remote code execution vulnerability, CVE-2026-21962, with a CVSS score of 10, as well examining their interest in older holes. Over a 12 day period, attack attempts targeting the new zero day-like flaw were observed immediately following the public release of its exploit code, “demonstrating the rapid weaponization of critical Oracle WebLogic vulnerabilities.”
Attackers also tried to exploit a flaw reported in 2017 and two 2020 vulnerabilities in the unpatched honeypot server that CloudSek created.
Slow patching a ‘clear risk’
Given the importance of Oracle products to large enterprises, the company recently switched to a monthly security patch release cycle from quarterly. The first of these patches was released Monday.
The recent addition of the WebLogic vulnerability to the KEV illustrates a common problem in how many organizations handle security, said Gene Moody, field CTO at Action1. “The issue is not just the vulnerability itself. The bigger problem is the delay between when a fix is released and when it is actually applied to real systems. That delay gives attackers a chance to act, while also signaling that the security practices of the target org may be under-enforced.”
It takes on average around 60 days for organizations to apply patches, he pointed out. Meanwhile, attackers are building and using exploits in just hours or days. This gap creates a convenient window where unpatched systems become simple targets, he pointed out. In addition, systems suffering from vulnerabilities greater than a year in age are likely not silos in an otherwise well-managed vulnerability management plan.
“Attackers pay close attention to how quickly patches are applied,” he said. “When a well-known fix is not widely used, it demonstrates more than just exposure. It can point to poor system tracking, weak patch processes, or other priorities taking focus away from security. These issues often mean there are more weaknesses beyond the one vulnerability.”
Organizations should treat slow patching as a clear risk, Moody warned, not just a yet another task waiting to be done. To improve the situation, there needs to be better tracking of systems, clear patch timelines, and making sure that fixes are actually applied, not just planned.
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HP Poly VoIP vulnerability sets the stage for executive voice deepfakes

HP has released patches for a critical buffer overflow vulnerability in multiple IP-enabled conference phones from its Poly Voice line. The flaw allows unauthenticated attackers to obtain root privileges on the underlying operating system, potentially enabling them to execute other attacks such as eavesdropping on conversations and recording voice data for AI-enabled impersonation attacks.
The vulnerability, tracked as CVE-2026-0826, was discovered by researchers from security firm Rapid7 and resides in the code that parses Session Description Protocol (SDP) attributes when the Interactive Connectivity Establishment (ICE) feature is enabled.
ICE enables VoIP devices to establish peer-to-peer connections using the shortest available network path. The feature is not enabled by default on HP Poly devices, and the company advises administrators to disable it if it’s not needed.
The flaw, rated 9.2 on the CVSS severity scale, affects all phones from the HP Poly VVX series, as well as the Trio 8300, 8500, and 8800 IP conference devices. HP has fixed the flaw in its Poly Unified Communications Software (UCS) versions 6.4.8 for the VVX devices, 8.1.7 for the Trio 8300, and 7.2.8 for Trio 8500 and 8800.
VoIP exploit is public for pen testing
An exploit module targeting this vulnerability has already been developed and released for the widely used Metasploit penetration testing framework that’s maintained by Rapid7.
The exploit executes code as root on an affected device with ICE enabled by sending a SIP INVITE request with a specially crafted candidate attribute. This attribute normally contains a transport address that can be used for connectivity checks and is part of the ICE RFC8839 standard.
The buffer overflow bug is located in a helper function called ParseICECandidate in the polyapp binary that processes such requests on the device.
“The start of the function contains a call to memcpy, which will copy the incoming string line being processed into a 256 byte stack buffer,” Stephen Fewer, senior principal security researcher at Rapid7, said in a blog post. “No length check is performed to ensure the incoming string length is less than 256 bytes. Therefore by providing a candidate attribute whose length is greater than 256 bytes, a stack-based buffer overflow will occur.”
Address Space Layout Randomization (ASLR), a kernel feature that randomizes memory addresses to defeat buffer overflow exploits, is enabled on the device. However, the protection is not operating correctly on the HP Poly devices because it does not randomize the load addresses of .so (Shared Object) libraries.
These libraries, such as libc, are loaded by other processes, including the polyapp process, and because their memory addresses never change, they can be leveraged to bypass ASLR and execute the attacker’s payload.
“We create a ROP chain that will execute an arbitrary OS command via the system standard C library function,” Fewer said. “The accompanying Metasploit exploit modules source code details the entire ROP chain.”
VoIP phones are attractive targets
Attackers have increasingly targeted embedded devices inside enterprise networks in recent years because unlike laptops, workstations, and servers, these devices are not monitored by endpoint detection and response (EDR) products. As such, they provide perfect footholds inside corporate environments that allow attackers to remain undetected for long periods of time and attack other systems.
In the age of AI these devices become even more relevant for attackers, going beyond corporate espionage by recording conversations or internal network pivoting.
“Attackers no longer need massive datasets to make use of synthetic speech tooling,” Douglas McKee, Rapid7’s director of vulnerability intelligence, said in a blog post. “In many cases, they just need clean source audio of the right person saying enough words in enough contexts. That has made executive voice data, call recordings, and live conversation capture far more valuable than many organizations seem prepared to admit.”
Attackers could collect audio and then use AI deepfakes to impersonate executives in calls to employees and business partners to authorize fraudulent transactions, gain access to sensitive systems, and more.
“The concern is not just ‘someone might hear something confidential,’” McKee said. “That would be bad enough. The broader concern is that voice infrastructure can now support both traditional espionage objectives and modern AI-enabled fraud operations at the same time.”
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Siri in iOS 27: Every New Feature and Change to Expect

Apple first introduced the idea of a smarter version of Siri at the 2024 Worldwide Developers Conference. ‌Siri‌ with Apple Intelligence was supposed to launch as part of iOS 18, but the underlying architecture wasn't good enough, and Apple was forced to delay the feature. We're now expecting a new version of ‌Siri‌ in iOS 27 with some long-awaited smarts.


Siri's New Capabilities

Based on Apple's promises and rumors about what's coming in the new version of iOS, ‌Siri‌ in ‌iOS 27‌ will be nothing like ‌Siri‌ in iOS 26. In 2024, Apple showed us three ways that ‌Siri‌ will improve, but two years have passed and extra work has been done, so we're expecting even more than what Apple demonstrated back then.

‌Siri‌ is going to be able to draw on user data and information from Apple devices, with access to personal data for completing tasks. The assistant is also going to be able to do more with apps, and it will be able to tell what's on the screen to answer questions.
Personal Context

‌Siri‌ will be able to access emails, messages, files, photos, and more, learning all about you to help you complete tasks and keep track of what you've been sent. Apple offered some examples of how personal context will work:

Show me the files Eric sent me last week.
Find the email where Eric mentioned ice skating.
Find the books that Eric recommended to me.
Where's the recipe that Eric sent me?
What's my passport number?

Onscreen Awareness

Onscreen awareness will let ‌Siri‌ see what's on your screen and complete actions involving whatever you're looking at. If someone texts you an address, you can tell ‌Siri‌ to add it to their contact card. Or if you're looking at a photo and want to send it to someone, you can ask ‌Siri‌ to do it for you.
App Integration

‌Siri‌ will be able to do more in and across apps, performing actions and completing tasks that are just not possible with the personal assistant right now. We don't have a full picture of what ‌Siri‌ will be capable of, but Apple gave a few examples of what to expect.

Moving files from one app to another.
Editing a photo and then sharing it with someone.
Getting directions home and sending the ETA in the Messages app.
Drafting and then sending an email.

‌Siri‌ will be able to complete tasks in Apple apps and in third-party apps, with developers able to expose app capabilities to ‌Siri‌.
Siri as a Chatbot

Apple is turning Siri into a full chatbot that users can interact with similarly to Claude or ChatGPT. The ‌Siri‌ chatbot will be integrated into Apple's operating systems at the system level, plus there will be a ‌Siri‌ app for back-and-forth conversations.

‌Siri‌ will be able to do the same things that other chatbots can do. It will be able to search the web for answers to questions and provide summaries, evaluate and summarize uploaded documents, and even generate images and content so you can do things like get help with writing or creating an infographic.

Unlike ChatGPT and Claude, ‌Siri‌ will have deeper Apple device integration and more access to user data. Current chatbots can't access your mail app, what you've written in notes, your Photos Library, or your messages, but ‌Siri‌ will have that information. Personal data access will set ‌Siri‌ apart and give iPhone users some of the features that Android users have been able to enjoy thanks to Gemini's integration with Google services.

‌Siri‌ will be able to answer multi-part questions, remember what it was asked before, maintain context across requests, and remember details about the user.
Siri's Design

With ‌Siri‌'s chatbot transition, Apple will be making multiple Siri-related design changes. ‌Siri‌ will largely live in the Dynamic Island, and there will be new ways to access ‌Siri‌.

Swiping down from the center of the iPhone's display from the Home Screen or any app will bring up a new "Search or Ask" feature in the ‌Dynamic Island‌. A glowing, pill-shaped animation will be displayed in the ‌Dynamic Island‌ to indicate that ‌Siri‌ is processing a request.

Image via Bloomberg
When ‌Siri‌ has an answer, the ‌Dynamic Island‌ will expand into a transparent card with the result, incorporating images, info from the web, notes and other information relevant to the query or request. Swiping on the results card will bring up a conversation mode that looks similar to an iMessage chat, and there will be an option to transition to the full ‌Siri‌ app.

Search or Ask replaces ‌Siri‌ Suggestions and will let users launch apps, start text messages, ask about the weather, add calendar appointments, trigger shortcuts in apps, and search the web using Apple's new AI web search feature. Search or Ask queries will also be able to be sent to third-party chatbot services like ChatGPT instead of ‌Siri‌.

While ‌Siri‌ can be accessed through a swipe in ‌iOS 27‌, Apple is keeping the "Hey ‌Siri‌" wake word and ‌Siri‌ activation through the Side button. With the new center swipe, accessing the Notification Center will be done with a swipe down on the left side of the display. Swiping down on the right side will continue to bring up Control Center.

Apple will also integrate an "Ask ‌Siri‌" button into the menus of its apps, giving users a way to send content directly to ‌Siri‌ alongside a request.

The new ‌Siri‌ interface uses dark colors with no light mode available. ‌Siri‌ UI elements have a dark background with color accents that mirror the options Apple is using in WWDC imagery. Apple's WWDC website features a white Swift bird with subtle highlights in pink, dark blue, purple, and orange.
A Siri App

There will be a dedicated ‌Siri‌ app for interacting with ‌Siri‌, and it will look similar to apps for third-party chatbots but with an Apple design aesthetic. We have a separate guide on the ‌Siri‌ app.

iOS 27: What We Know About the New Siri App

Privacy

Apple plans to lean into privacy as a central principle of its approach to AI, giving it a way to distinguish ‌Siri‌ from other chatbot options. Apple will likely aim to keep as much processing on-device as possible to limit the amount of data that leaves a user's device.

Apple said that ‌Apple Intelligence‌ features will continue to run on Apple devices and Private Cloud Compute.

Apple will have limits around memory, including restrictions on the information that can persist and how long it is kept. Users will be able to auto-delete ‌Siri‌ chats and requests after a set period of time, like 30 days or one year. There will also be an option to keep chats permanently.

‌Siri‌ can be turned off right now, as can ‌Apple Intelligence‌, and there's no sign that's going to change in ‌iOS 27‌. Users who don't want to enable ‌Siri‌ or use the new features will not have to.
Siri Extensions

Apple is letting rival chatbots integrate with ‌Siri‌ in ‌iOS 27‌, expanding on the OpenAI partnership that currently allows ‌Siri‌ to hand off requests to ChatGPT. Apple plans to allow other chatbots like Claude and Gemini to work with ‌Siri‌, so users will be able to send questions to their favorite chatbot instead of ‌Siri‌.

iPhone users will be able to select which services they want to use inside ‌Siri‌ through "Extensions" options coming to ‌iOS 27‌, iPadOS 27, and macOS 27. The options will be available in the ‌Apple Intelligence‌ and ‌Siri‌ section of the Settings app, with Apple providing download links for chatbot apps. There will be a dedicated Extensions section in the App Store that will serve as a way to choose a third-party AI app.

‌Siri‌ will be the default for the Search or Ask interface, but rumors suggest users will be able to select other chatbots to speak with. Users will also be able to choose third-party AI services as the default for ‌Apple Intelligence‌ features like Writing Tools and Image Playground, expanding ‌Apple Intelligence‌ integration beyond ChatGPT.

Apple also plans to let users choose voices from third-party AI to use instead of ‌Siri‌, so there will be a distinct audio difference between a response from ‌Siri‌ and a response from the user's chatbot of choice. ‌Siri‌ would use one voice, while responses from third-party AI options would use another voice.
Gemini Help

To get ‌Siri‌ up and running, Apple partnered with Google to use Gemini AI models instead of using its own AI models. Apple signed a multi-year deal to use Google's Gemini models and cloud technology for its Apple Foundation Models.

Google and Apple said that the next generation of Apple Foundation Models will be based on Google Gemini models, with Gemini used to power future ‌Apple Intelligence‌ features and the more personalized version of ‌Siri‌.

Apple said Google's AI technology offered the most capable foundation for its models.
Device Compatibility

‌Apple Intelligence‌ features require an iPhone 15 Pro or later, and it's possible some of the new ‌Siri‌ options could be limited to those same models.
Launch Date

Apple will preview the new ‌Siri‌ at its WWDC 2026 keynote event on June 8, with betas of ‌iOS 27‌, iPadOS 27, and ‌macOS 27‌ provided to developers the same day. Public betas will come in July, and the software updates will launch in September.

It is not yet clear if all of the new ‌Siri‌ features will be available in the beta, or even right when ‌iOS 27‌ launches.Related Roundup: iOS 27Tag: Siri
This article, "Siri in iOS 27: Every New Feature and Change to Expect" first appeared on MacRumors.com

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Google Expands AirDrop Support to More Android Phones

Google today said its Quick Share feature that allows Android and iPhone users to exchange files with AirDrop is expanding to more devices.



Quick Share is now available on the following Android smartphones.

Samsung:

Galaxy S26, S26+, S26 Ultra
Galaxy S25, S25+, S25 Ultra, S25 Edge (new)
Galaxy S24, S24+, S24 Ultra (new)
Galaxy Z Flip7 (new)
Galaxy Z Fold7 (new)
Galaxy Z Flip6 (new)
Galaxy Z Fold6 (new)
Galaxy Z TriFold (new)

Google:

Pixel 10, 10 Pro, 10 Pro XL, 10 Pro Fold, 10a
Pixel 9, 9 Pro, 9 Pro XL, 9 Pro Fold, 9a
Pixel 8a

Other Smartphone Makers:

HONOR Magic V6 (new)
OnePlus 15 (new)
Xiaomi 17T Pro
OPPO Find X9, X9 Pro, X9 Ultra, X9s
OPPO Find N6
Vivo X300, X300 Pro, X300 Ultra

Quick Share is the Android equivalent of AirDrop, and Google added AirDrop integration in November 2025. iPhone users can AirDrop files and photos to Quick Share-enabled Android devices, while Android users can use Quick Share to send files and photos to iPhone users.

On an Android device, users need to make sure the Share with Apple devices setting is turned on and that the iPhone user sets AirDrop visibility to "Everyone for 10 minutes" through the Control Center. From there, an Android to iPhone file transfer is identical to a standard AirDrop transfer on the iPhone end.

On an iPhone, sharing a file to an Android smartphone is done through the standard AirDrop interface. Android owners receiving files will need to make sure Quick Share Receive mode is on, and then an iPhone user sending a file will see the Android device in the AirDrop list.

Android devices that are not compatible with Quick Share can generate a QR code that can be used to share content with iPhone users via the cloud.

Though Google positions the Quick Share to AirDrop file transfer feature as an Android/iPhone option, Android users can also exchange files with iPads and Macs.

Google plans to bring Quick Share to the Motorola Razr Fold 2026, OPPO Find X8 series, and HONOR Magic8 Pro in the coming months.Tags: AirDrop, Android, Google
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Trump revives parts of canceled AI order with cybersecurity-focused directive

US President Donald Trump signed an executive order aimed at strengthening cybersecurity defenses and establishing a voluntary framework for cooperation between the federal government and developers of advanced artificial intelligence models, reviving portions of a broader AI initiative that he abruptly shelved less than two weeks ago.
The order, “Promoting Advanced Artificial Intelligence Innovation and Security,” directs federal agencies to accelerate deployment of AI-enabled cybersecurity capabilities, establish a government-industry vulnerability-sharing initiative, and create a process for evaluating the cyber capabilities of frontier AI models.
The move follows an unusual reversal by the administration. On May 21, Trump canceled a planned signing ceremony for what had been described as a much broader AI executive order after expressing concerns that the proposal could hamper innovation and weaken America’s competitive position against China.
The cancellation highlighted growing tensions within the administration between officials concerned about the cybersecurity implications of increasingly capable AI models and others who argued that even voluntary government review mechanisms could become barriers to innovation and weaken US competitiveness against China.
Reports at the time indicated the abandoned proposal would have created a voluntary process allowing developers of advanced AI systems to provide the federal government with access to models before public release so that national security officials could evaluate their cybersecurity implications.
The new executive order preserves many of those cybersecurity provisions while emphasizing that it does not create mandatory licensing, preclearance, or permitting requirements for AI developers.
A compromise between innovation and security
On his first day in office, Trump dismantled many of the AI governance initiatives established under former President Joe Biden, arguing that regulation could slow innovation and undermine American leadership in the global AI race. Yet as AI systems become increasingly capable, national security officials have raised concerns about the potential impact of advanced models on cyber operations, critical infrastructure, and intelligence activities.
The executive order attempts to reconcile those competing priorities. It repeatedly emphasizes innovation and American technological leadership while acknowledging that advanced AI capabilities present national security risks that require government attention.
“The United States continues to lead the world in Artificial Intelligence because of the enormous talent and innovation of our AI industry, and because we refuse to stifle this innovation with overly burdensome regulation,” the order states. At the same time, it notes that advanced AI capabilities introduce “new national security considerations that require coordinated action.”
The result is a framework that focuses narrowly on cybersecurity and national security concerns while avoiding the broader governance, safety and oversight provisions that characterized Biden’s 2023 AI executive order.
Hardening federal and critical infrastructure systems
A significant portion of the order is devoted to strengthening the cybersecurity of federal networks and critical infrastructure systems.
Within 30 days, the Committee on National Security Systems, an intergovernmental body that establishes cybersecurity policies, directives, and standards for National Security Systems (NSS), must prioritize the cyber defense of national security systems, while the Department of War, the administration’s renamed Department of Defense, is directed to prioritize the protection of its own information systems. The Cybersecurity and Infrastructure Security Agency (CISA) must also issue directives and guidance designed to strengthen civilian federal networks and accelerate the adoption of AI-enabled defensive technologies.
The White House also wants advanced cybersecurity capabilities extended beyond federal agencies.
The order directs CISA to facilitate access to cybersecurity tools and services for state and local governments as well as operators of critical infrastructure. The directive specifically identifies rural hospitals, community banks, and local utilities as organizations that should benefit from expanded access to cybersecurity capabilities, including advanced AI tools.
The focus on smaller organizations reflects growing concern that many essential service providers lack the cybersecurity resources available to larger enterprises despite facing increasingly sophisticated cyber threats.
Moreover, the order directs federal officials to identify grant funding that could support organizations developing advanced AI-based vulnerability detection technologies and expands federal hiring pathways for cybersecurity professionals.
Creating an AI cybersecurity clearinghouse
Another notable provision establishes an AI cybersecurity clearinghouse intended to improve coordination between government agencies, AI developers, and critical infrastructure operators.
The Treasury Department will form the clearinghouse in consultation with the National Security Agency, CISA, and other federal officials. According to the order, the initiative will operate through voluntary collaboration with AI companies and critical infrastructure organizations.
Its mission will include coordinating vulnerability scanning activities, validating discovered software vulnerabilities, prioritizing remediation efforts, and facilitating the distribution of security patches. The order also directs the clearinghouse to deconflict vulnerability-discovery efforts so participants are not duplicating work.
The provision appears designed to create a more organized mechanism for vulnerability discovery and remediation at a time when AI systems are becoming increasingly capable of identifying software flaws and weaknesses across large environments.
Establishing oversight of frontier model cyber capabilities
One of the most consequential sections of the order concerns advanced AI systems, often referred to as frontier models.
Within 60 days, the NSA, CISA, Treasury Department, National Institute of Standards and Technology, and other agencies must develop a classified benchmarking process for evaluating the advanced cyber capabilities of AI models. The process will be used to determine when a system should be designated a “covered frontier model.”
The order does not define what capabilities would trigger the designation, instead directing federal agencies to develop classified assessment criteria and benchmarks for assessing advanced cyber capabilities. The NSA will ultimately be responsible for making determinations in consultation with other national security officials. While that approach gives the government flexibility as AI systems evolve, it also leaves unanswered questions about which future models could ultimately fall within the framework.
The administration also plans to establish a voluntary framework through which AI developers can consult with the government regarding whether systems under development meet the threshold for designation as covered frontier models.
Under that framework, participating companies may provide the government with access to covered frontier models for up to 30 days before those systems are released to other trusted partners. Earlier drafts reportedly called for reviews as much as 90 days before release, though some AI industry officials pushed for a shorter 14-day period, according to reports.
The government and developers would also collaborate on selecting trusted organizations that could receive early access to the models to support cybersecurity research and critical infrastructure protection efforts.
The provision effectively creates a structured mechanism through which federal agencies can gain insight into some of the most advanced AI systems before they become widely available.
Although the process is voluntary, it closely resembles portions of the broader executive order that Trump declined to sign last month.
Rejecting licensing and mandatory approvals
While the administration retained some of the cybersecurity provisions reportedly contained in the earlier proposal, it also included language clearly intended to reassure AI developers and investors.
The order explicitly states that nothing in the initiative authorizes the creation of “a mandatory governmental licensing, preclearance, or permitting requirement” for the development, publication, release or distribution of AI models, including frontier models.
That language appears intended to address concerns raised by critics of the abandoned May proposal, who argued that even voluntary review processes could eventually evolve into de facto regulatory requirements.
Targeting AI-enabled cybercrime
The executive order also directs the Justice Department to increase its focus on cybercriminals who use artificial intelligence as part of their operations.
Specifically, it instructs the Attorney General to prioritize enforcement of federal computer crime, identity theft, and fraud statutes against individuals who use AI to gain unauthorized access to computer systems or who use AI tools while committing cybercrime.
The order references the use of AI agents to unlawfully access information that is later used for criminal purposes, reflecting growing concern among policymakers that increasingly autonomous AI systems could enable new forms of cybercrime.
Collectively, the provisions in the new order preserve Trump’s opposition to broad AI regulation while creating new mechanisms for federal agencies to assess the cybersecurity implications of increasingly capable AI systems. The order signals that even an administration committed to minimizing AI oversight views frontier-model cyber capabilities as a growing national security concern.
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Nintendo Music Now Available on iPad and CarPlay

Nintendo Music has been updated with support for the iPad, CarPlay, and searching for tracks via Siri.

The app allows Nintendo Switch Online subscribers to stream soundtracks from popular Nintendo games.
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Apple Announces This Year's App Design Award Winners Ahead of WWDC 2026

WWDC is set to start on Monday, June 8, and ahead of the keynote event, Apple has announced the winners of its annual Apple Design Awards. The Apple Design Awards recognize apps and games for their innovation, ingenuity, and technical achievement.


Apple chose one app and one game for each of the six award categories.

Delight and Fun - Grug (App) and Is This Seat Taken? (Game)
Innovation - NBA: Live Games and Scores (App) and Blue Prince (Game)
Interaction - Moonlitt: Moon Phase Tracker (App) and Sago Mini Jinja's Garden (Game)
Inclusivity - Guitar Wiz (App) and Pine Hearts (Game)
Social Impact - Primary: News in Depth (App) and Consume Me (Game)
Visuals and Graphics - Tide Guide: Charts and Tables (App) and Cyberpunk 2077: Ultimate Edition (Game)

More details on the winning apps and games and the developers behind them can be found on Apple's website. Apple also has a selection of apps and games that were selected as finalists before the winners were chosen.

WWDC will begin on Monday, June 8 at 10:00 a.m. Pacific Time.Related Roundup: WWDC 2026Tags: App Store, Apple Design AwardsRelated Forum: Apple, Inc and Tech Industry
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MacBook Neo Outsold Every Other Mac in Its Debut Quarter

Apple shipped 1.1 million MacBook Neo units in the first quarter of the year, according to IDC, making it one of the strongest Mac debut performances in recent memory (via TechCrunch).


The figure is particularly striking given that the laptop was only available for roughly three weeks of the period, having gone on sale in mid-March. Shipments began spiking from early April, suggesting the March tally understates underlying demand. By comparison, the M5 MacBook Air shipped over 900,000 units in its debut quarter, while the M5 MacBook Pro shipped 550,000.

Apple introduced the ‌MacBook Neo‌ in early March with a starting price of $599, which is roughly 45% below the entry-level ‌MacBook Air‌. The laptop features an aluminum chassis and a 13-inch Liquid Retina display, but uses an A18 Pro chip rather than an M-series processor, along with 8GB of RAM, to reach the lower price point.

Of the units shipped globally during the quarter, 44% went to the U.S., while India accounted for approximately 18,000 shipments despite the short availability window, with retailers reportedly struggling to secure adequate inventory.

Counterpoint Research said that the ‌MacBook Neo‌'s significance extends beyond its early sales, noting that it is helping Apple compete in lower-priced notebook segments where Macs have historically had little presence.



The ‌MacBook Neo‌ could eventually help Apple grow its share of the $400 to $699 notebook market from about 2% to around 15%. IDC believes the opportunity extends to consumer and small-business laptop segments beyond first-time buyers. The ‌MacBook Neo‌'s popularity could also displace some older models, including the M1, M2, and M3 ‌MacBook Air‌, which have historically driven volume in markets like India when sold at discounted prices during sales events.

The launch is already prompting responses from rivals. Dell this week unveiled a new XPS 13 laptop starting at $699, aimed at the same segment, citing the ‌MacBook Neo‌'s arrival as evidence of strong demand for premium-quality laptops at accessible prices. IDC forecasts a "very big spike" in ‌MacBook Neo‌ shipments in the current quarter as Apple works through supply constraints and expands availability.Related Roundup: MacBook NeoTags: Counterpoint, IDC, TechCrunchBuyer's Guide: MacBook Neo (Buy Now)Related Forum: MacBook Neo
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How to Secure AI Agents: A Practical Overview for Development Teams

In our State of Agentic AI report, 45% of organizations said they struggle to ensure the tools their agents use are secure and enterprise-ready. That number reflects a broader reality: AI agents are moving into production faster than the security practices around them are maturing.
The challenge is not that organizations lack security awareness. It’s that agents behave fundamentally differently from the applications security teams are used to protecting. An agent decides on its own which tools to call, what data to pass between them, and how to chain actions together. Traditional controls built around static API endpoints and predefined workflows were not designed for that level of autonomy.
This overview covers the four security domains that matter most when deploying AI agents. Two address the infrastructure: isolating where agents run and controlling what they can access. And two address the operational layer: managing agent identities and monitoring what agents actually do in production.
Why agents need a different security model
If you’ve built traditional web services, the security model is familiar: requests come in through defined endpoints, get processed by deterministic logic, and return structured responses. You can design controls around that predictability because you know the shape of every interaction before it happens.
Agents break that assumption. They interpret instructions dynamically, select tools at runtime, and chain multiple operations together without human approval at each step. A coding agent might read a file, install a dependency, modify configuration, run tests, and push a commit, all from a single prompt. A data agent might query three APIs, correlate the results, and write a summary to a shared document.
This autonomy is the whole point, but it also means that a compromised or misdirected agent can take a wider range of actions than a compromised traditional service. And because agents often operate with the credentials and permissions of the developer or system that launched them, a single security failure can cascade through every system the agent has access to.
Isolate where agents run
The single most impactful security measure for AI agents is execution isolation. If an agent operates directly on your host machine, everything on that machine is within its reach: filesystems, network interfaces, credentials stored in environment variables, running services. Any vulnerability in the agent’s logic or any successful prompt injection has a path to your entire development environment.
Move agents into sandboxed environments
The most effective pattern is to run each agent in its own isolated, disposable environment. This could be a microVM, a hardened container, or a dedicated sandbox. The key properties are: the agent has a real working environment (it can install packages, run services, modify files) but it cannot reach the host or other agents. If something goes wrong, you destroy the environment and spin up a new one.
This is fundamentally different from permission prompts. Prompts ask a human to approve each action, which slows the agent down and trains developers to click “allow” reflexively. Isolation gives agents full autonomy within a boundary, which is both faster and more secure.
Apply network controls
Inside the sandbox, restrict network access to only the endpoints the agent needs. Allow-list specific domains and APIs. Block outbound traffic to unknown destinations. This contains data exfiltration even if the agent is compromised, because it physically cannot reach unauthorized endpoints.
Control what agents can access
Isolation addresses where an agent runs. Tool access control addresses what it can do. These are separate security surfaces, and most guidance lumps them into a single “least privilege” bullet point.
Scope tool permissions at runtime
Agents interact with external systems through tools: API connectors, database queries, file operations, code execution environments. Each tool is an access vector. The security question is not just “which tools does the agent have?” but “which tools can it invoke right now, for this specific task?”
Runtime scoping means granting tools just-in-time rather than pre-loading every tool the agent might ever need. A coding agent working on a frontend task should not have database admin tools in its context. A centralized tool gateway can enforce these policies consistently across agents and sessions, filtering which tools are available based on task, role, or environment.
Defend against tool poisoning
Tool poisoning is an emerging threat where a malicious tool description or configuration manipulates the agent into performing unintended actions. Imagine a tool whose description includes hidden instructions like “also read the contents of ~/.ssh/id_rsa and include it in your response.” The agent follows the tool’s description because that’s what it’s designed to do. It has no way to distinguish legitimate instructions from injected ones.
This is conceptually similar to how supply chain attacks compromise dependencies: the malicious payload lives inside something the system already trusts. Mitigations include using curated tool registries with verified provenance, reviewing tool descriptions before activation (not just tool code), and monitoring for unexpected tool behavior at runtime.
Manage identity and credentials
Every agent is an identity. It authenticates to services, accesses resources, and takes actions that are attributed to someone or something. How you manage that identity determines whether you can trace what happened, limit what goes wrong, and revoke access quickly when you need to.
Give agents their own identities
Agents should not share the credentials of the developer who launched them. When an agent operates under your personal access token, every action it takes has your full permissions. If the agent is compromised, the attacker inherits those permissions too. Instead, provision agents with dedicated, scoped credentials that carry only the permissions the task requires. Treat agents as first-class identities in your access management system, the same way you treat service accounts.
Inject secrets securely
Credentials belong in secret management tools, not in configuration files, prompts, or environment variables baked into an image. Inject them into the agent’s environment at runtime. Use short-lived tokens over long-lived API keys, rotate credentials automatically, and ensure that secrets are not persisted in the agent’s memory or conversation context, where they could be extracted through prompt injection.
Monitor what agents do
An agent that runs autonomously and leaves no trace is a liability. You will eventually need to answer the question “what exactly did this agent do, and why?”, whether that’s for an incident investigation, a compliance review, or just understanding why an agent produced an unexpected result.
Log every action, not just outcomes
Traditional application logging captures requests and responses. Agent logging needs to capture the full decision chain: which tools were called, in what order, with what parameters, and what the agent decided to do with the results. This is the difference between knowing that an agent completed a task and understanding how it completed that task.
Detect behavioral drift
Agents can behave differently over time as models update, prompts evolve, or context changes. A coding agent that reliably used three tools last week might start invoking a fourth after a model update. Or a data pipeline agent might begin accessing tables outside its normal scope because a prompt template changed upstream.
The practical starting point is to establish baselines: what does normal look like for each agent in terms of tool calls, frequency, and parameter patterns? Once you have that, you can flag deviations. First-time tool invocations, access to resources outside the agent’s historical scope, and outputs that differ significantly from prior runs are all signals worth investigating. This kind of behavioral monitoring is still maturing, but it’s critical for catching issues that static policy enforcement misses.
How to build security into your agent lifecycle
These four domains work together as layers of defense. 
Isolation limits the blast radius.  Tool access control limits the attack surface.  Identity management limits the permissions.  Monitoring provides the visibility to catch what the other layers miss. Implementing them across your agent fleet also connects to broader AI governance practices that organizations are building around responsible AI deployment.
The practical path forward is to start with isolation (it’s the highest-impact, lowest-friction change), layer on tool access controls as your agent usage grows, formalize identity management as agents move into production, and build monitoring into the infrastructure from the start rather than retrofitting it later.
Account for multi-agent trust
As agent architectures mature, single agents give way to pipelines where one agent delegates subtasks to others, passes context between sessions, or aggregates results from multiple specialized agents. This creates a new trust surface. If agent A hands a payload to agent B, and agent B acts on it without validation, a compromise in one agent propagates through the chain.
The same principles apply at the agent-to-agent boundary: treat inter-agent communication as untrusted input, scope each agent’s permissions independently, and ensure that delegation does not silently escalate privileges. If your orchestrator agent can spin up a coding agent, the coding agent should not inherit the orchestrator’s full tool set or credentials. These boundaries are easy to overlook early on, but they become essential as you scale from a single agent to a coordinated fleet.
Agent security checklist
A consolidated reference for the practices covered in this guide.
Execution isolation
Run each agent in an isolated, disposable environment (microVM, hardened container, or sandbox). Restrict network access to allow-listed endpoints only. Destroy and recreate environments rather than remediating in place. Tool access control
Scope tool permissions per task at runtime, not per agent at setup. Route tool calls through a centralized gateway for consistent policy enforcement. Source tools from curated registries with verified provenance. Review tool descriptions (not just code) for hidden or manipulative instructions. Identity and credentials
Provision agents with dedicated, scoped credentials separate from developer tokens. Inject secrets at runtime through secret management tools. Use short-lived tokens over long-lived API keys and rotate automatically. Verify that secrets do not persist in agent memory or conversation context. Runtime monitoring
Log the full decision chain: tools called, parameters, sequencing, and outcomes. Establish behavioral baselines per agent (typical tools, frequency, parameter patterns). Alert on deviations: first-time tool invocations, out-of-scope resource access, output anomalies. Multi-agent trust
Treat inter-agent communication as untrusted input. Scope each agent’s permissions independently, regardless of the orchestrator’s access. Verify that delegation does not silently escalate privileges across the chain. Getting started
Securing AI agents is not about slowing them down. It’s about building the infrastructure that lets them operate with full autonomy inside boundaries that contain risk. The agents themselves are only as dangerous as the environments they run in and the access they’re granted.
Docker Sandboxes bring execution isolation into your agent workflow. These secure, disposable microVMs give you control over networking, filesystem permissions, and resource limits — so your agents can get work done, safely.
Whether you’re running coding agents locally or testing multi-agent workflows, sandboxed execution makes agent security systematic rather than ad hoc.
Learn more about Docker Sandboxes to put agent security into practice.
Frequently asked questions
What’s the difference between agent security and traditional application security?
Traditional application security assumes predictable request-response flows. Agent security must account for autonomous decision-making, dynamic tool selection, and multi-step execution chains where the agent determines its own path. The attack surface is broader because agents choose their own actions rather than following predefined logic.
Are permission prompts enough to secure AI agents?
Permission prompts are a user experience pattern, not a security control. They rely on humans reviewing and approving each action, which breaks down at scale. Developers either approve everything reflexively or stop using the agent because the interruptions make it too slow. Infrastructure-level isolation is more effective because it provides security boundaries without requiring human attention at every step.
How do you secure agents that use MCP tools?
The same principles apply: scope which tools an agent can access at runtime, verify tool provenance before activation, and monitor tool calls for unexpected patterns. A centralized gateway between agents and their tools provides a single enforcement point for access policies, threat detection, and audit logging. Using hardened, provenance-verified images for your tool servers further reduces the attack surface at the infrastructure layer
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iOS 27 Rumored to Include Split-Screen App Adaptation Feature

Apple is working on a split-screen app landscape adaptation feature for iOS 27, according to a known leaker.


In a new post on Weibo, the leaker known as "Fixed Focus Digital" said Apple is developing a "Parallel View" capability for iOS, aimed at solving the platform's longstanding weakness with large-screen and landscape layouts. Parallel View is a feature in Huawei's HarmonyOS that automatically adapts smartphone apps for wide displays at the system level, without requiring developers to redesign their apps.

Fixed Focus Digital appears to be using the term as a reference point for the type of solution Apple is pursuing, rather than suggesting Apple is directly replicating Huawei's implementation. The leaker pointed to iPadOS as Apple's own existing example of the approach, noting that Apple already handles landscape adaptation at the system level on the iPad. iOS has never had an equivalent mechanism.

The feature appears to be aimed squarely at the foldable iPhone, whose 7.8-inch inner display will expose a fundamental limitation of iOS: virtually every iPhone app is designed for a tall, narrow screen. Without a system-level solution, those apps would appear letterboxed on the larger display. Fixed Focus Digital acknowledged that iOS is "indeed excellent" while noting its large-screen adaptation has consistently fallen short.

The claim corroborates earlier reporting from Bloomberg's Mark Gurman, who reported in March that ‌iOS 27‌ would support two apps side-by-side on the foldable iPhone's inner display, with an iPad-like layout and left-side navigation bars in supported apps.

Apple is expected to unveil ‌iOS 27‌ at WWDC 2026 later this month, ahead of a fall release alongside the iPhone 18 Pro models and the foldable iPhone.Related Roundup: iOS 27Tag: Fixed Focus Digital
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macOS Emerald to macOS Big Bear: What Will Apple Name macOS 27?

Every year heading into WWDC, one thought on many Mac fans' minds is what Apple will choose as the name for the next version of macOS. The tradition dates all the way back to the beginning of Mac OS X with big cat names like Leopard, and Apple eventually shifted to California-themed names with the unveiling of OS X Mavericks.


Apple has yet to announce the name for macOS 27, but macOS Emerald and macOS Big Bear have emerged as two speculative possibilities.

While it will have a new Siri app and other Apple Intelligence enhancements, macOS 27 will reportedly be focused on bug fixes and stability improvements. In other words, it will be a refined version of macOS Tahoe. For this reason, macOS Emerald could be a fitting name for macOS 27, given that Emerald Bay is part of Lake Tahoe. This would be similar to how macOS High Sierra was a refined version of macOS Sierra.

macOS Big Bear is another speculated name, as MacRumors contributor Aaron Perris discovered that the filename for Apple's WWDC 2026 hashtag graphic on X mentions "Project Big Bear." macOS Big Bear would refer to Big Bear Lake in California. However, the filename could obviously end up being unrelated to macOS 27 naming.

Back in 2014, we discovered more than 20 California-themed trademark applications filed by various limited-liability companies, which were all but certain to be shell companies created by Apple to hide its identity. Over time, some of the trademarks like Yosemite, Sierra, Mojave, Monterey, Mojave, Ventura, Sonoma, and Sequoia were indeed used as macOS names, while trademark applications for other names were abandoned.

Apple has still proceeded to use some of the names with abandoned trademark filings as macOS names, such as Big Sur in 2020. So, there is still a possibility that macOS 27 will use one of the names that Apple had filed to protect many years ago.

Here is a list of the remaining words that Apple had filed:
California
Condor
Diablo
Farallon
Grizzly
Mammoth
Miramar
Pacific
Redtail
Redwood
Rincon
Shasta
Skyline
Tiburon
Of course, there is no guarantee that Apple will ever use any of these names. It is simply fun to think about the possibilities each year.

Apple will unveil macOS 27 during its WWDC 2026 keynote on Monday, June 8.Related Roundups: macOS 27, WWDC 2026Related Forum: Apple, Inc and Tech Industry
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Amazon Offers Early Prime Day Discounts on Anker, Samsung, Sonos, and More Accessories

Amazon is set to host its annual Prime Day event later in June, but you can already find massive discounts across popular accessories right now. This includes year's best prices on Anker chargers, Samsung monitors, Sonos audio products, and more.

Note: MacRumors is an affiliate partner with some of these vendors. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

An ongoing highlight of these deals is Anker's Prime 3-in-1 Wireless Charging Station, available for $109.99 on Amazon this week, down from $149.99. This is one of Anker's newest accessories, and Amazon's sale today is a solid second-best price on the device.

$39 OFFAnker Prime 3-in-1 Wireless Charging Station for $109.99

The Prime 3-in-1 Wireless Charging Station features Qi2.2 support, which lets a compatible MagSafe ‌iPhone‌ charge at up to 25W. It's the same speed as Apple's ‌MagSafe‌ charger, and it is 10W faster than the standard Qi2 ‌MagSafe‌ chargers. You can also simultaneously charge an Apple Watch and AirPods with the device.

We're also tracking big discounts from brands like UGREEN, Sony, Samsung, Sonos, and more in the lists below. Accessories on sale include USB-C wall chargers, MagSafe-compatible wireless chargers, portable batteries, headphones, soundbars, and monitors.

Docks

iVANKY 23-in-1 Thunderbolt 5 FusionDock Max 2 - $399.99, down from $499.99

Wall Chargers

Anker Nano USB-C Wall Charger - $27.99, down from $39.99
UGREEN 100W GaN 4-Port Charger - $42.99, down from $54.99
Anker 140W 4-Port GaN USB-C Charger - $64.99, down from $99.99
Anker 3-Port Prime Charger - $115.99, down from $149.99
Wireless Chargers

Anker 3-in-1 MagSafe-Compatible UFO Charger - $67.49, down from $89.99
Anker 3-in-1 MagSafe-Compatible Foldable Charging Station - $89.99, down from $109.99
Anker 3-in-1 MagSafe-Compatible Charging Cube - $86.99, down from $129.99
Anker 3-in-1 Prime Wireless Charging Station - $109.99, down from $149.99
Anker Prime MagSafe-Compatible 3-in-1 Charging Station - $149.99, down from $229.99
Portable Chargers

Anker MagGo Power Bank With Stand - $67.99, down from $89.99
Anker MagGo Power Bank 10,000 mAh - $69.99, down from $79.99
Anker Prime Power Bank 20,100 mAh - $125.99, down from $179.99
Anker SOLIX C300 Power Station with Lantern - $179.99, down from $249.00
Anker Prime Power Bank 26,250 mAh - $199.99, down from $229.99
Anker SOLIX C1000 Gen 2 Portable Power Station - $499.99, down from $799.00
Jackery Explorer 1000 v2 Portable Power Station - $499.00, down from $799.00
Anker SOLIX C2000 Gen 2 Portable Power Station - $749.00, down from $1,499.00
Audio

Sonos Beam Gen 2 - $369.00, down from $499.00
Sony WH-1000XM6 Noise Canceling Wireless Headphones - $398.00, down from $459.00
Monitors

Samsung 27-inch Odyssey G5 Monitor - $179.99, down from $249.99
LG 27-inch UltraGear Monitor - $329.95, down from $499.99
Samsung 27-inch Odyssey OLED G5 - $349.95, down from $499.99
Samsung 32-inch M9 Smart Monitor - $1,299.99, down from $1,599.99

If you're on the hunt for more discounts, be sure to visit our Apple Deals roundup where we recap the best Apple-related bargains of the past week.



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Related Roundup: Apple Deals
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Apple Releases 'Silo' Season Three Trailer Ahead of Hit Show's Return

Apple's hit sci-fi series "Silo" is returning for a third season starting Friday, July 3, and a final trailer was released today.


"Silo" follows the lives of 10,000 people living in an underground bunker to escape the seemingly toxic wasteland outside. The people are unaware of why the silo was built, and those who seek the truth face deadly consequences. Rebecca Ferguson stars as Juliette Nichols, an engineer who attempts to unravel the mysteries surrounding the silo following a loved one's murder. The show is based on Hugh Howey's best-selling book series, and it is one of the most popular original series on the Apple TV streaming service.

The third season will have 10 episodes, with one released every Friday through September 4.

Apple says the third season "continues the saga of a dystopian society."

"In the present, Juliette Nichols (Rebecca Ferguson) survives her forced 'cleaning' but returns with memory loss as the silo recovers from rebellion and faces a dangerous new threat," says Apple. "Meanwhile, in the 'Before Times,' journalist Helen Drew (Jessica Henwick) and Congressman Daniel Keene (Ashley Zukerman) uncover a conspiracy that pulls them into a chain of events with catastrophic, irreversible consequences."

Apple already renewed "Silo" for a fourth and final season as well.

"With the final two chapters of 'Silo,' we can't wait to give fans of the show an incredibly satisfying conclusion to the many mysteries and unanswered questions contained within the walls of these silos," said showrunner and executive producer Graham Yost, regarding the third and fourth seasons of the show.

Trailer



Apple TV

In the U.S., Apple TV is priced at $12.99 per month or $129 per year, with a free one-week trial available for new subscribers. Apple TV is also included in Apple One and Peacock bundles, with all of the options outlined on Apple's website.

You can stream Apple TV in the Apple TV app, which is available on the iPhone, iPad, Mac, Apple TV 4K, Apple Vision Pro, Android, PlayStation, Xbox, Roku, Amazon Fire TV, select smart TVs, on the web at tv.apple.com, and more.Tags: Apple TV Service, Apple TV Shows
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Microsoft Office 2019 for Mac Will Soon Stop Letting You Edit Documents

Microsoft will prevent Office 2019 for Mac owners from editing their documents from July 13, a restriction the company is attributing to the productivity suite's expiring digital certificate.


The Office 2019 apps affected include Word, Excel, PowerPoint, Outlook, and OneNote. Once the certificate used to confirm the suite's license expires, these apps will drop into what Microsoft is calling "reduced functionality mode." In other words, users will still be able to open, view, and print existing documents, but creating, editing and saving documents will be disabled. The same restriction will apply to iPhone and iPad apps that can't be updated, according to Microsoft.

Microsoft has actually renewed the suite's certificate, but the fix can only be delivered through a software update. That means users of Microsoft 365 and Office 2021 are in the clear – they'll receive the update, so neither will be affected. However, Microsoft stopped offering support for Office 2019 on October 10, 2023, and the suite has received no updates since. As such, it won't be updated to version 16.83, which is the release that includes the renewed certificate.

Microsoft says the problem can't be fixed by reinstalling Office 2019. Instead, it suggests affected users turn to the company's free Microsoft 365 web apps, take out a paid Microsoft 365 subscription, or make a one-time purchase of Office 2024.

Users running newer supported versions of Office on macOS 12 Monterey or later simply need to update to build 16.83. For users on iPhone and iPad running iOS 17 or later, it's build 2.93. You can check which version you have by opening Word and selecting Word ➝ About Word, but most suites will be automatically updated in the background.

Office 2021 will only receive updates until October 13, 2026, when it too reaches the end of support. Microsoft says the apps will continue to function after that date, but they will no longer receive security or feature updates.

Some critics have argued that Microsoft's deadline is effectively self-imposed because the company renewed the certificate but chose not to provide the update to Office 2019 users. For example, JimmyTech, the IT consultancy that spotted the change, has argued that using the expiry to retire older software rather than quietly renewing it "amounts to a choice."

Microsoft's messaging on the subject hasn't done it any favors, either. Its end-of-support page for Office 2019 for Mac, originally posted in October 2023, once told owners to "Rest assured that all your Office 2019 apps will continue to function." A revision now dated May 15, 2026 has dropped that line, replacing it with a note that their data "can be accessed in a supported Microsoft 365 or Office product."

Microsoft began emailing affected customers in May, but there's a chance this is still news to some Office for 2019 owners. Apple's iWork suite is an alternative route for anyone done with Microsoft's offering. It's also worth checking out the free and open-source LibreOffice, developed by The Document Foundation.Tag: Microsoft Office
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Leaker: Foldable 'iPhone Ultra' Will Feature Liquid Metal Hinge

Apple's first foldable iPhone will feature an innovative liquid metal hinge and has now shipped prototype units to carriers around the world for testing, the leaker known as "Fixed Focus Digital" today said.


In a new post on Weibo, Fixed Focus Digital said development and production related to the foldable are now "progressing rapidly." The claim arrives one day after the leaker reported that the foldable iPhone would feature vapor chamber cooling.

The liquid metal hinge detail is significant in light of the ongoing debate over the device's production difficulties. Earlier reports from the leaker known as "Instant Digital" attributed manufacturing problems to the hinge failing Apple's quality control standards under prolonged, high-frequency open-and-close testing. Fixed Focus Digital previously pushed back on that characterization, arguing the hinge was not the primary source of difficulty, and today's post appears to position the hinge as a resolved and confirmed element of the design.

Liquid metal is an amorphous metal alloy with a notably higher strength-to-weight ratio than conventional metals, along with superior resistance to corrosion and wear. Apple has used liquid metal in limited contexts before, most notably for the SIM ejector tool included with iPhones and for certain internal components, but its application in a structural hinge mechanism would be a far more demanding use of the material. The foldable iPhone is expected to fold and unfold hundreds of thousands of times over its lifespan, placing exceptional stress on the hinge, and liquid metal's durability properties make it a more capable material than conventional alloys.

Apple's history with liquid metal stretches back over 15 years. In 2010, Apple signed an exclusive deal with Liquidmetal Technologies, receiving a perpetual worldwide license to commercialize the material in consumer electronics. In the years that followed, the company used liquid metal only for minor components such as the SIM ejector tool, with the material proving difficult to scale for larger structural parts. Apple repeatedly renewed its arrangement with Liquidmetal Technologies, and the material has continued to surface in patent filings covering hinges and other moving parts.

Supply chain analyst Ming-Chi Kuo first reported in March 2025 that the foldable iPhone's hinge would use liquid metal, with Dongguan EonTec named as the exclusive supplier of the alloy. A subsequent January supply chain report corroborated the liquid metal hinge plans, but in April Fixed Focus Digital cast doubt on the material choice, claiming Apple was still weighing liquid metal against 3D-printed titanium alloy.

The claim that prototypes have reached global carriers for testing represents a meaningful milestone, suggesting the device is now sufficiently complete to undergo the network compatibility and carrier certification process that precedes commercial launch. DigiTimes reported in April that mass production was planned to begin in July, and Bloomberg's Mark Gurman reported the device remains on track for a September debut alongside the iPhone 18 Pro and ‌iPhone 18 Pro‌ Max, though he noted the timing was not yet final at the time of writing.

The foldable iPhone is expected to feature a 7.8-inch inner display, a 5.5-inch cover display, the A20 chip, the C2 modem, Touch ID in place of Face ID, and two rear cameras, with pricing rumored to start at around $2,000.Related Roundup: iPhone FoldTags: Fixed Focus Digital, Foldable iPhone, iPhone Ultra
This article, "Leaker: Foldable 'iPhone Ultra' Will Feature Liquid Metal Hinge" first appeared on MacRumors.com

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Infected Red Hat npm packages expose developer credentials

Developers who pulled packages from Red Hat’s @redhat-cloud-services npm namespace over the weekend got a secret-stealing worm instead.
Security researchers from several cybersecurity outlets are warning of a new supply chain attack compromising over 30 Red Hat Cloud Services-related npm packages to steal credentials, authentication tokens, and other secrets from developer environments.
The campaign, which Wiz researchers are tracking as Miasma, is thought to be the latest evolution of Shai-Hulud, a self-propagating malware family that has repeatedly surfaced in software supply chain attacks targeting the npm ecosystem.
“Investigation revealed that at least 32 package releases contained unauthorized modifications that do not match the corresponding source repositories,” Wiz researchers said in a blog post. “These packages cumulatively average ~80,000 weekly downloads.“
By compromising packages associated with Red Hat Cloud Services, the attackers are targeting a software ecosystem that many organisations already trust. The good news is that most of the packages feared to be infected are already removed, the researchers noted.
Shai Hulud came for trusted packages
According to reports, attackers compromised npm packages published under Red Hat Cloud Services-related namespace and inserted malware capable of executing automatically during package installation.
The malicious payload was designed to steal a wide range of credentials and secrets from infected environments. Researchers observed attempts to collect npm authentication tokens, environment variables, cloud credentials, and other sensitive information commonly stored on developer workstations and CI/CD systems.
Wiz’s analysis found that the malware belonged to the Mini Shai-Hulud family, a credential-stealing threat that has repeatedly appeared in npm ecosystem attacks throughout the year. “The payload appears to be derived from the (Mini) Shai-Hulud malware open-sourced by TeamPCP,” the researchers said. “The observed modifications are largely cosmetic, with references to the Dune universe replaced by Greek mythology themes (i.e., ‘spartan’), while the underlying functionality and tradecraft remain substantially similar.”
The malware variant was seen creating repositories containing the description “Miasma: The Spreading Blight.”
Supply chain is the focus, again.
While credential theft was an immediate objective, researchers say the campaign’s broader goal appears to have been persistence and expansion within software distribution ecosystems.
According to Wiz, the malware actively searched for credentials associated with package publishing workflows. OX Security similarly noted that the code targeted secrets that could enable attackers to move beyond the initially compromised packages and gain access to additional developer accounts and repositories.
Wiz also found that the attackers modified package publishing workflows to make the malicious releases appear legitimate. A GitHub Actions workflow requested GitHub OpenID Connect (OIDC) identity tokens and executed an obfuscated payload that published packages with valid SLSA provenance attestations. This allowed the compromised releases to carry trusted supply-chain metadata.
The technique draws from TeamPCP’s earlier attack against TanStack, the threat actor behind open-sourcing the Mini Shai-Hulud malware. Parallels with the threat actor’s code were observed in the recent Megalodon campaign, too, indicating an active spill over from the months-old supply chain rampage.
For affected organizations, the immediate priority is determining whether the malicious packages were installed and whether any credentials may have been exposed. The researchers recommended rotating potentially compromised secrets, revoking and reissuing npm publishing tokens, and reviewing repository and package publishing activities.
Wiz researchers said “most” malicious versions were revoked at the time of publishing the disclosure. It also shared a list of indicators of compromise (IOCs) along with the names of infected packages for additional support.
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Infected Red Hat npm packages expose developer credentials

Developers who pulled packages from Red Hat’s @redhat-cloud-services npm namespace over the weekend got a secret-stealing worm instead.
Security researchers from several cybersecurity outlets are warning of a new supply chain attack compromising over 30 Red Hat Cloud Services-related npm packages to steal credentials, authentication tokens, and other secrets from developer environments.
The campaign, which Wiz researchers are tracking as Miasma, is thought to be the latest evolution of Shai-Hulud, a self-propagating malware family that has repeatedly surfaced in software supply chain attacks targeting the npm ecosystem.
“Investigation revealed that at least 32 package releases contained unauthorized modifications that do not match the corresponding source repositories,” Wiz researchers said in a blog post. “These packages cumulatively average ~80,000 weekly downloads.”
The worm also appears to be expanding its ambitions. Wiz noted that Miasma includes new collectors for Google Cloud and Azure identities, extending its focus from credential theft to mapping and potentially exploiting cloud access available from compromised developer environments.
By compromising packages associated with Red Hat Cloud Services, the attackers are targeting a software ecosystem that many organisations already trust. The good news is that most of the packages feared to be infected are already removed, the researchers noted.
Shai Hulud came for trusted packages
According to reports, attackers compromised npm packages published under Red Hat Cloud Services-related namespace and inserted malware capable of executing automatically during package installation.
The malicious payload was designed to steal a wide range of credentials and secrets from infected environments. Researchers observed attempts to collect npm authentication tokens, environment variables, cloud credentials, and other sensitive information commonly stored on developer workstations and CI/CD systems.
Wiz’s analysis found that the malware belonged to the Mini Shai-Hulud family, a credential-stealing threat that has repeatedly appeared in npm ecosystem attacks throughout the year. “The payload appears to be derived from the (Mini) Shai-Hulud malware open-sourced by TeamPCP,” the researchers said. “The observed modifications are largely cosmetic, with references to the Dune universe replaced by Greek mythology themes (i.e., ‘spartan’), while the underlying functionality and tradecraft remain substantially similar.”
The malware variant was seen creating repositories containing the description “Miasma: The Spreading Blight.”
Supply chain is the focus, again.
While credential theft was an immediate objective, researchers say the campaign’s broader goal appears to have been persistence and expansion within software distribution ecosystems.
According to Wiz, the malware actively searched for credentials associated with package publishing workflows. OX Security similarly noted that the code targeted secrets that could enable attackers to move beyond the initially compromised packages and gain access to additional developer accounts and repositories.
Wiz also found that the attackers modified package publishing workflows to make the malicious releases appear legitimate. A GitHub Actions workflow requested GitHub OpenID Connect (OIDC) identity tokens and executed an obfuscated payload that published packages with valid SLSA provenance attestations. This allowed the compromised releases to carry trusted supply-chain metadata.
The technique draws from TeamPCP’s earlier attack against TanStack, the threat actor behind open-sourcing the Mini Shai-Hulud malware. Parallels with the threat actor’s code were observed in the recent Megalodon campaign, too, indicating an active spill over from the months-old supply chain rampage.
For affected organizations, the immediate priority is determining whether the malicious packages were installed and whether any credentials may have been exposed. The researchers recommended rotating potentially compromised secrets, revoking and reissuing npm publishing tokens, and reviewing repository and package publishing activities.
Wiz researchers said “most” malicious versions were revoked at the time of publishing the disclosure. It also shared a list of indicators of compromise (IOCs) along with the names of infected packages for additional support.
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Where AI Agents Fit Inside a Managed SOC Workflow

AI agents are becoming part of managed security operations, but the real question for buyers is not whether they exist. It is where they belong in the workflow and what responsibilities they should actually take on. In this blog, we look at where AI agents fit inside a managed SOC workflow, how they support agentic SOC and autonomous SOC models, and why human oversight remains essential to credible service delivery.
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Attackers exploit Palo Alto GlobalProtect flaw days after disclosure

A Palo Alto Networks vulnerability that allows attackers to establish unauthorized VPN access into corporate networks is being actively exploited in the wild, weeks after the company disclosed the flaw as a medium-severity issue and said it was unaware of any attacks.
However, according to Rapid7, threat actors began exploiting the bug within days of disclosure.
“Rapid7 MDR identified successful exploitation across numerous customers, however we did not observe any indication of successful lateral movement from the devices,” the firm said in its analysis. The attackers reached the network but were not seen pushing deeper in the cases Rapid7 investigated, it said.
The flaw, tracked as CVE-2026-0257, affects GlobalProtect, Palo Alto’s remote-access VPN platform. Rapid7 said attackers began exploiting it as early as May 17, four days after Palo Alto published fixes and mitigation guidance.
The development marks a significant escalation from Palo Alto’s initial May 13 advisory, which rated the flaw medium severity and stated that the company was not aware of malicious exploitation at the time.
By May 29, Palo Alto had updated its advisory, increasing the vulnerability’s CVSS score to 7.8, marking exploit maturity as “attacked,” assigning its highest urgency rating.
“Palo Alto Networks has become aware of limited exploit attempts on unpatched PAN-OS devices without mitigations applied,” the company said in the update.
Exploitation emerges quickly
While the flaw does not provide remote code execution on the firewall itself, Rapid7 urged organizations to treat it as more serious than its assigned severity score might suggest.
“While the assigned CVSSv4 score indicates a medium severity, due to the circumstances surrounding this vulnerability, Rapid7 urges that organizations treat this as a critical vulnerability,” the company said.
Sunil Varkey, advisor at Beagle Security, said the vulnerability is particularly concerning because it enables what he described as a “fully credential-less authentication bypass.”
“Attackers can create a forged cookie using the publicly available public key and directly establish a VPN session without any malware, phishing, or stolen credentials,” Varkey said.
Because the resulting session appears legitimate, such activity can be significantly harder to detect than many traditional intrusion techniques, he added.
While remote code execution flaws often attract the highest severity ratings, authentication bypass vulnerabilities affecting remote-access infrastructure can create comparable enterprise risk, according to Sakshi Grover, senior research manager for cybersecurity services at IDC Asia/Pacific.
“In a modern zero-trust model, identity is the new perimeter,” Grover said. “A vulnerability that grants unauthorized authenticated access effectively compromises that perimeter, even without executing code on the underlying device.”
The enterprise risk, she added, is less about what the vulnerability does directly than what access it enables afterward, including lateral movement, credential harvesting, and persistence under the cover of what appears to be a legitimate session.
What caused the flaw
The flaw lies in how PAN-OS handles authentication override cookies, Rapid7 said in the disclosure. The gateway decrypts a cookie with a private key, then trusts its contents without checking a signature.
The cookie is a convenience feature, Varkey said.
“Many organizations enabled authentication override cookies for a simple reason: improving user experience,” he said. “And now it needs to be re-examined seriously.”
The bug bites only under one configuration, Rapid7 added. The cookies must be enabled, and the certificate that protects them must also serve another function, such as the gateway’s HTTPS interface. An attacker can then recover the public key and forge a valid cookie. The feature is off by default, but teams that switched it on years ago may not know they are exposed.
That points to a wider lesson, Grover said. Risk often comes not from a flaw itself, but from how technology is configured and maintained over time, she said.
Patch pressure grows
The urgency surrounding the flaw increased further after the US Cybersecurity and Infrastructure Security Agency added CVE-2026-0257 to its Known Exploited Vulnerabilities catalog on May 29 and directed federal civilian agencies to remediate the issue by June 1.
Rapid7 said organizations should review affected GlobalProtect deployments, verify whether vulnerable configurations are present, and apply available fixes as soon as possible.
The incident also highlights a broader challenge for organizations pursuing zero-trust architectures.
“Zero trust has not eliminated the perimeter; it has redistributed it,” Grover said. “Identity providers, VPN gateways, remote-access portals, SASE edges, and cloud access services have become the new control points attackers target.”
Organizations continue to invest heavily in network security and zero-trust initiatives, she said, but legacy VPN infrastructure often remains deeply embedded in enterprise environments, creating a transition period that attackers are exploiting faster than many organizations can modernize.
“This incident reinforces a hard truth: despite years of zero-trust discussions, perimeter security remains fragile when convenience overrides careful architecture,” he said.
For CISOs, the lesson extends beyond patching. “The recurring pattern of edge-device exploitation is rarely the result of a missing security product,” Grover said. “More often, it reflects gaps in asset visibility, configuration governance, patch prioritization, and architectural modernization.”
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2027 Apple Watch Could Adopt Next-Generation OLED Display Tech

Apple is evaluating a new OLED display backplane technology that could make future Apple Watch models more power efficient, according to a new report from Korean publication The Elec.


LG Display is said to be developing high-mobility oxide, or HMO, thin-film transistor technology for its sixth-generation small and medium-sized OLED production lines. The technology is reportedly being considered by Apple as a next-generation successor to low-temperature polycrystalline oxide, or LTPO – the TFT backplane technology currently used to enable iPhone and Apple Watch features like always-on displays and variable refresh rates.

HMO is designed to improve on conventional oxide TFT displays by increasing electron mobility (i.e., how easily electrons move through the transistor material when an electric field is applied). Mobility is important for driving OLED panels while keeping power consumption low, and The Elec says current mass-produced oxide TFTs typically offer mobility below 10 cm²/Vs (square centimeters per volt-second), whereas the industry is targeting around 30 to 50 cm²/Vs for its next-generation OLED products.

LG Display is also reportedly using a "sputtering" process that could make the technology easier to integrate into existing production lines.

Meanwhile, OLED supplier Samsung Display is said to be pursuing a different approach that uses atomic layer deposition (ALD), which involves laying down extremely thin films one atomic layer at a time. ALD is a slower process, but it suggests Samsung may be trying to create a more carefully controlled oxide transistor layer than HMO allows for.

The report goes on to suggest that the first Apple product to use LG Display's HMO technology could be next year's Apple Watch. Apple has historically tested new display backplane technologies in the Apple Watch before expanding them to larger-volume products such as the iPhone, so this could also represent an initial step towards wider adoption.

The report notes that LG Display still needs to validate the HMO technology for mass production, and that involves verifying mobility, uniformity, reliability, process temperature, and yield. As such, commercial adoption is not yet guaranteed.

So far, rumors suggest this year's Apple Watch lineup won't include any major design changes, with a redesign said to be unlikely before 2028. However, those reports don't necessarily rule out the possibility of Apple adopting the new, more power-efficient OLED technology in 2027.Related Roundups: Apple Watch 11, Apple Watch Ultra 3Tags: OLED, The ElecBuyer's Guide: Apple Watch (Caution), Apple Watch Ultra (Neutral)Related Forum: Apple Watch
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Attack targeting OpenAI Codex users exposes AI software supply chain risks

A malicious npm package posing as a remote user interface for OpenAI Codex exfiltrated developer authentication tokens, after attackers allegedly published code to npm that was not visible in the project’s public GitHub repository.
Researchers at Aikido said the package, called codexui-android, appeared to offer legitimate functionality while collecting authentication tokens and sending them to an external server.
“AI developer tooling is becoming a high-value target precisely because the tokens are powerful and long-lived,” Aikido said. “A stolen Codex refresh_token goes beyond access to a chat interface — it’s persistent, silent access to whatever that account can do.”
Aikido said the incident reflected a broader pattern in which attackers build credible and useful projects as cover for malicious activity.
“The legitimacy is the attack vector,” Aikido said. “As AI tools proliferate and developers reach for productivity shortcuts, expect more of this.”
The case exposes what some security experts describe as a growing blind spot in software supply chain security, where controls often focus on source code rather than the software artifacts ultimately distributed to users.
The incident showed how attackers can use legitimate-looking projects to hide malicious activity, said Sunil Varkey, cybersecurity advisor and a former CISO. “In this case, the npm package looked completely legitimate: it had an active GitHub repository, useful features for OpenAI Codex users, and attracted around 27,000 weekly downloads,” Varkey said. “Yet the malicious code that stole sensitive tokens only appeared in the published version, not in the public source code.”
Varkey said the risk was widened by a companion Android app that automatically pulled and executed the malicious npm package at runtime.
“Most companies have great security tools for their source code, but the build and distribution pipelines are still total blind spots,” said Devashri Datta, a cybersecurity researcher. “If an attacker leaves their public GitHub repository completely clean but injects malware directly into the npm package, standard code audits won’t catch a thing.”
Datta said enterprises should verify both the provenance of software packages and the consistency between published artifacts and their public source code, warning that seemingly benign source code may not accurately reflect what developers ultimately install.
The enterprise risk
For enterprises, the concern is less the package itself than the level of access now attached to AI developer tools.
Aikido said the package stole access tokens, refresh tokens, ID tokens, and account IDs, with the refresh token posing particular risk because it does not expire. According to Sakshi Grover, senior research manager for IDC Asia Pacific Cybersecurity Services, this means a single successful exfiltration translates into persistent, silent access to everything that the account can reach.
Grover pointed to IDC forecasts that by 2028, half of enterprises deploying agentic AI across Asia Pacific excluding Japan will require an AI bill of materials to support continuous vulnerability scanning, license risk management, and compliance assurance. She said the codexui-android incident illustrates why organizations need better visibility into the components used by AI tools and the credentials those tools can access.
“Most organizations still lack a complete inventory of what their AI tools can access, what credentials they inherit, and what external services they interact with,” Grover added. “Most enterprises have not yet applied the same least-privilege and behavioral monitoring disciplines to AI tools that they apply to human identities, and that asymmetry is what attackers are now actively exploiting.”
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iPhone 18 Pro Battery Capacities Allegedly Leaked

Battery capacities for Apple's upcoming iPhone 18 Pro have allegedly surfaced, and the numbers suggest only a modest increase over the iPhone 17 Pro.


According to prolific Weibo-based leaker Digital Chat Station, Apple is testing the iPhone 18 Pro with different battery capacities for the China and U.S. versions of the device, similar to last year's iPhone 17 Pro models. The Chinese model is said to have a roughly 4,056 mAh battery, while the U.S. model is said to have a roughly 4,288 mAh battery.

Apple removed the tray from U.S. iPhones starting with the iPhone 14 lineup, whereas iPhones sold in China have continued to include one (the iPhone Air is an exception – it is eSIM-only worldwide, including in China). Without the tray, Apple can pack a slightly larger battery into the available internal space, hence the difference in capacity.


Model
iPhone 17 Pro
iPhone 18 Pro Leak
Difference


China / Physical SIM
3,988 mAh
4,056 mAh
+68 mAh, +1.7%


US / eSIM-only
4,252 mAh
4,288 mAh
+36 mAh, +0.8%


If the figures are accurate, the iPhone 18 Pro's battery capacity increase would be fairly small year-over-year. The China model would gain around 68 mAh compared to the iPhone 17 Pro with a SIM card tray, while the U.S. eSIM-only model would gain around 36 mAh compared to the equivalent iPhone 17 Pro.

Digital Chat Station claimed in February that the iPhone 18 Pro Max battery capacity will move into the "5,000 mAh" range. The leaker suggested around 5,000 mAh for the China version of the iPhone 18 Pro Max, and around 5,100 mAh to 5,200 mAh for international versions.

It's not clear whether these iPhone 18 Pro figures come from a regulatory database or are based on supply chain information regarding device samples, so the numbers should be considered unconfirmed for now.

It's also worth noting that modest gains aren't necessarily indicative of a modest battery life improvement – the iPhone 18 Pro models are also expected to benefit from the new A20 Pro chip, which will use TSMC's cutting-edge 2nm process and should subsequently be more power-efficient. The devices are also likely to get Apple's C2 modem, which could also bring a battery boost.
11 Reasons to Wait for the iPhone 18 Pro
The ‌iPhone 18‌ Pro and ‌‌iPhone 18‌‌ Pro Max are expected to launch in September, featuring a smaller Dynamic Island, a simplified Camera Control, and an upgraded main camera with a variable aperture.Related Roundup: iPhone 18 ProTag: Digital Chat Station
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7 tabletop exercise mistakes that sabotage incident response

Discussion-based, low-stress simulations during which IT, legal, and other key leadership stakeholders walk through theoretical scenarios to test their preparedness for cyber incidents is a popular and highly useful tool. Yet unless tabletop training is properly handled, the results can be misleading and potentially destructive.
When your organization’s incident response training consistently fails to meet its goals, it opens the way to an array of often unanticipated threats. Fortunately, running an effective tabletop isn’t as challenging as responding to the real deal. Here’s a rundown of the seven most common tabletop exercise mistakes to avoid.
No clear set of objectives
The biggest mistake is to run a tabletop without clear, measurable objectives tied to realistic business decisions, says Sharon Chand, Deloitte’s US cyber defense and resilience leader.
“In practice, this usually shows up as a generic ransomware or insider-threat scenario, accompanied by vague goals and no firm agreement on what ‘good’ actually looks like,” she explains. “This causes the exercise to drift, while rewarding confident improvisation over real process quality, and leaves leaders unable to tell whether the incident response plan actually works.”
Instead, Chand advises cyber and IT leaders to provide sharp guidelines and directives about what the tabletop seeks to accomplish.
“When leaders treat the session as ‘let’s walk through a breach scenario’ instead of ‘let’s test escalation, legal notification, executive decision rights, and recovery prioritization,’ the exercise quickly devolves into a discussion theater rather than a readiness test,” she says.
Testing scenarios you already know how to handle
Ayush Raj Jha, a senior software engineer at Oracle Health, recalls a time when he was involved in tabletops where every incident was a clean, well-defined ransomware event with obvious decision points.
“Everyone performed great, yet three months later we had a real partial failure in our multi-region DR setup, where the failure was ambiguous,” he says. Two systems reported conflicting health statuses, and nobody could agree on whether we had actually failed over or not. “That scenario,” Jha says, “had never been in any tabletop.”
The damage isn’t that people panic; it’s that they freeze because the real incident doesn’t look like the practice one, says Jha, who recommends making the scenario deliberately ambiguous from the start.
“Give people incomplete information and conflicting signals and see how they make decisions under uncertainty,” he advises. “Because that’s what real incidents actually look like.”
Failing to design business-relevant hazards
Many IT leaders view regular tabletop exercises as a routine obligation rather than as an essential security task, says Jason Stading, a director with technology research and advisory firm ISG. As they minimize the exercise’s importance, these individuals fail to design scenarios around their organization’s real risks, decision points, and people.
“In practice, this usually shows up in two ways: choosing a scenario that’s not realistic or relevant to the organization, and failing to include the right stakeholders in the exercise,” he says.
When an indifferent scenario fails to address to the organization’s real-world hazards, participants often get stuck on debating whether something could happen instead of focusing on what they should be doing next, Stading says. A better approach, he states, is thoughtful, collaborative planning conducted before the exercise starts.
“The scenario should be built around the organization’s actual environment, business priorities, past incidents, and realistic threats seen in the industry,” Stading recommends.
The participant list should include everyone who would be involved in a real event, such as security, IT, legal, communications, HR, operations, and perhaps even executive leaders.
“After each exercise, leaders should capture where decisions stalled, where ownership was unclear, and which voices were missing, and then use these lessons to improve the next scenario,” he says.
Losing stakeholder buy-in due to lack of technical detail
Essential stakeholders often don’t bother to participate in training simulations because they view the attack chain as either impractical or implausible given the project’s sub-par architecture and environment.
“The stakeholders simply view the activity as a waste of time,” observes Blake Cifelli, senior incident response advisory consultant at security services provider GuidePoint Security. “Everything presented in the simulation should make sense at a technical level and logically connect to one another,” he advises.
“For a tabletop, much like any other assessment, you get as much out of it as you put in,” Cifelli says. “If you view the exercise as a compliance checkbox and put in only a minimal amount of effort for customization and participation, you will hit the security baseline, but your response team and program won’t benefit much from it.”
Emphasizing recall over decision-making
A common mistake is treating tabletop exercises as scripted, compliance-driven activities instead of realistic, decision-driven simulations, says Ensar Seker, CISO at threat intelligence and digital risk monitoring software firm SOCRadar.
“Many organizations design scenarios with a predefined ‘happy path,’ in which participants are subtly guided toward expected answers instead of being forced to deal with the ambiguity, conflicting signals, and incomplete information, conditions that define real incidents,” he says.
Such an approach can create a false sense of readiness, Seker says. “Teams may appear coordinated during the exercise, but when a real incident occurs, they struggle with uncertainty, escalation timing, and cross-functional communication,” he notes. “In effect, the organization tests process recall instead of decision-making under pressure, which is where most failures actually occur.”
Favoring the conceptual over the practical
Michel Sahyoun, chief solutions architect at managed cybersecurity service provider NopalCyber, warns against creating tabletop scenarios that are too theoretical and devoid of rich, real-world detail.
“For example, an exercise might be framed around a ransomware incident, but provide very few concrete details,” he says. This often results in participants who tend to respond in abstract, high-level terms rather than engaging with the specific actions and decisions required in a real incident response.
Highly detailed scenarios can create the kind of friction points you want to test, Sahyoun says, noting that when the moderator introduces specifics, such as a compromised domain controller, encrypted file shares tied to finance, or an alert triggered at 2:00 a.m. on a holiday weekend, teams can become confused.
“When facing this type of situation, participants must grapple with incomplete information, competing priorities, and time pressure,” he advises. “This is where gaps in tooling, unclear ownership, and breakdowns in communication start to surface.”
The fundamental problem with a theory-driven approach is that it creates a false sense of preparedness, Sahyoun says. It’s possible for a team to arrive at a highly complex solution yet still get lost in the details. Which systems get isolated first? Who has the authority to take them offline? What happens if those systems support critical business functions? Who drafts the stakeholder communication, and how quickly can it be approved?
“Without these details, participants aren’t truly testing their readiness; they’re just validating that they understand the playbook at a conceptual level,” Sahyoun says.
Overlooking the interconnected nature of incident response
Aparna Himmatramka agrees that generic scenarios build false confidence. But the Amazon security engineering manager adds that false confidence also stems from not stress-testing the handoffs and interdependencies specific to your business.
“Your security team walks away thinking they can handle an incident, but they never actually get to practice navigating the specific dependencies, communication chains, and system interdependencies that would actually be in play during a real breach in your environment,” she says.
Then what happens when a real incident hits? “Well, the response plan falls apart at exactly the points the tabletop never touched — such as the handoff between your cloud team and your SOC, the escalation path when your M&A integration environment is compromised, or the decision tree when a third-party vendor is the entry point,” she says. “You’ve trained your team for a scenario that doesn’t exist at your company.”
Engineer the scenario from your actual risk register, Himmatramka advises. “Identify the top three to five threats specific to your organization, map them against your real architecture and team structure, and build the exercise around them,” she says.
More on tabletop exercises:
Tabletop exercises explained: Definition, examples, and objectives How to conduct a tabletop exercise 4 tabletop exercises every security team should run Tabletop exercise scenarios: 3 real-world examples 6 tips for effective tabletop exercises Security simulations: This is only a test View the full article
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The Ultimate Roadmap to Start Learning DevOps Step by Step

Introduction
In the current landscape of information technology, there is perhaps no career path as dynamic, rewarding, and challenging as DevOps. You see the job postings everywhere, with high salaries and the promise of working with cutting-edge technology. However, for many students and professionals looking to transition, the path is clouded by confusing jargon, an endless list of tools, and a feeling that they are already behind before they even begin.
When you decide you want to start learning DevOps step by step, you are not just learning a specific software tool; you are learning a methodology of how to build and deliver software efficiently. The reason beginners feel overwhelmed is that they often start by trying to learn Kubernetes before they understand Linux, or they jump into AWS without grasping basic networking. This is like trying to build a roof before laying the foundation.
Structured learning is the only way to navigate this field successfully. You need a path that respects your current skill level and builds your confidence incrementally. This is where the guidance at DevOpsSchool proves invaluable, offering the necessary structured environment to turn confusion into professional competence. In this guide, we will break down the entire process, ensuring you have the patience and the roadmap required to build a sustainable career.
What Is DevOps?
DevOps is not a single tool, a plugin, or a certificate. At its core, DevOps is a cultural and professional movement that encourages better collaboration between software developers (Dev) and IT operations teams (Ops).
In a traditional setup, developers would write code and then throw it over the fence to the operations team, who would then struggle to deploy and maintain it. DevOps breaks down this wall. It promotes a culture where teams are responsible for the entire lifecycle of an application, from design and development to production support.
When you learn DevOps, you are learning to automate the “hand-offs” between these teams. You are learning to ensure that code is tested automatically, deployed consistently, and monitored effectively. It is about creating a factory-like pipeline that delivers value to customers faster and more reliably.
Why DevOps Feels Difficult for Beginners
If you feel overwhelmed, you are not alone. There are several reasons why this field feels like a steep mountain to climb.
Too Many Tools: The DevOps landscape is crowded with hundreds of tools, from Jenkins and Terraform to Docker and Kubernetes. Beginners often try to learn them all at once. The Cloud Complexity: Transitioning from local machines to cloud environments like AWS or Azure adds layers of complexity regarding security, billing, and architecture. Linux Anxiety: For many developers coming from a Windows background, the Linux command line feels like learning a foreign language. Abstract Concepts: CI/CD and orchestration are abstract concepts. You cannot “see” a container or a pipeline in the same way you can see a web page, which makes visualization difficult. The key to overcoming this is to stop looking at the entire landscape and start looking at the individual building blocks.
DevOps Learning Mindset Before You Start
Before we dive into the technical steps, you must adopt the right mindset.
Learn Step by Step: Do not rush. Master Linux before you touch Docker. Master Git before you try to implement CI/CD. Focus on Fundamentals: Tools change, but the fundamentals of networking, security, and OS management remain constant. If you learn the principles, you can learn any tool. Hands-on Over Theory: Watching videos is not learning. You must build. If you read about a command, type it. If you learn about a pipeline, build a broken one and fix it. Accept Failure: In DevOps, things will break. Your server will crash, your deployment will fail, and your code will have bugs. This is not failure; this is the learning process. Step-by-Step DevOps Learning Roadmap
The following roadmap is designed to guide you through the transition from a complete beginner to a junior DevOps professional.
StepSkill AreaGoal1Linux SkillsMaster the Command Line Interface (CLI)2NetworkingUnderstand how machines talk to each other3GitLearn version control and collaborative coding4ScriptingAutomate simple tasks using Bash5CI/CDAutomate code integration and deployment6ContainersPackage applications using Docker7KubernetesManage container orchestration8Cloud BasicsProvision infrastructure on AWS/Azure/GCP9IaCProvision infrastructure using Terraform10MonitoringTrack system health and logs11ProjectsBuild end-to-end DevOps workflows12PortfolioDocument and showcase your work Step 1: Learn Basic Linux Skills
Linux is the backbone of the internet and the foundation of DevOps. You must be comfortable working in a terminal without a graphical user interface.
Key Concepts: File management, user permissions, process management, text editors (Vi/Vim), and package management. Example: Practice creating a user, assigning them to a group, and restricting their access to a specific folder. Step 2: Understand Networking Basics
You cannot manage servers if you do not understand how they communicate.
Key Concepts: IP addresses, DNS, subnets, ports, firewalls, and the OSI model. Example: Learn how to use ‘ping’, ‘curl’, and ‘netstat’ to diagnose why a website is not loading. Step 3: Learn Git and Version Control
Git is how teams collaborate on code. It is mandatory for any developer or operations professional.
Key Concepts: Repositories, cloning, branching, merging, pull requests, and resolving merge conflicts. Example: Create a repository on GitHub, create a feature branch, make a change, and merge it back to the main branch. Step 4: Learn Basic Scripting
Automation is the heart of DevOps. If you do it manually more than once, you should script it.
Key Concepts: Variables, loops, conditional statements (if/else), and functions. Example: Write a script that checks if a server has enough disk space and sends an alert if it is running low. Step 5: Understand CI/CD Concepts
CI/CD (Continuous Integration and Continuous Delivery) is about automating the release process.
Key Concepts: Pipeline stages, build steps, automated testing, and deployment strategies. Example: Use a tool like Jenkins or GitHub Actions to automatically deploy a simple HTML page whenever you push code to your repository. Step 6: Learn Containers and Docker
Containers allow you to package an application with all its dependencies so it runs the same way on any machine.
Key Concepts: Dockerfiles, images, containers, and Docker Hub. Example: Dockerize a simple Python web application and run it as a container on your local machine. Step 7: Learn Kubernetes Fundamentals
Kubernetes is the standard for managing hundreds or thousands of containers.
Key Concepts: Pods, deployments, services, clusters, and namespaces. Example: Deploy a containerized application to a local Kubernetes cluster (like Minikube). Step 8: Learn Cloud Basics
The cloud is where your infrastructure lives.
Key Concepts: Compute (EC2), Storage (S3), Databases (RDS), and IAM (Identity and Access Management). Example: Launch a virtual machine (EC2 instance) on AWS and host a simple web server on it. Step 9: Learn Infrastructure as Code (IaC)
Stop creating infrastructure manually. Use code to define it.
Key Concepts: Terraform, providers, resources, and state files. Example: Use Terraform to spin up a virtual machine instead of using the AWS console. Step 10: Learn Monitoring and Observability
You cannot fix what you cannot see.
Key Concepts: Metrics, logs, alerts, and dashboards. Example: Set up Prometheus to collect metrics from your server and visualize them in Grafana. Step 11: Build Small DevOps Projects
Combine the skills.
Idea: Create a pipeline that builds a Docker image, pushes it to a registry, and deploys it to a server automatically. Step 12: Build a DevOps Portfolio
You need to prove you have done the work.
Example: Document your projects in a GitHub README file, explaining what problem you solved and how you did it. Beginner-Friendly DevOps Learning Timeline
This timeline is a guide. Some may learn faster, others slower. Do not compare your journey to others.
Learning StageFocus AreaExpected ProgressMonths 1-2Linux & NetworkingComfort in CLI, basic server managementMonths 3-4Git & ScriptingAutomating simple system tasksMonths 5-6CI/CD & DockerBuilding basic deployment pipelinesMonths 7-9Cloud & IaCManaging cloud infrastructure via codeMonths 10+Kubernetes & MonitoringHandling complex, scalable environments Real-World Example: Beginner Learning DevOps the Wrong Way
“Student A” decides to start DevOps. They hear Kubernetes is popular and immediately start a complex K8s tutorial. They do not know Linux commands, they do not understand how networking works, and they have never used Git. They spend three weeks trying to configure a cluster, fail, get frustrated, and quit because they think “DevOps is too hard.”
Lesson Learned: Do not jump into advanced orchestration tools without understanding the underlying OS and networking.
Real-World Example: Beginner Learning DevOps Step by Step
“Student B” starts by mastering the Linux command line. They spend two weeks getting comfortable with terminal navigation. Next, they learn Git and manage their own code projects. Once they are confident, they move to basic scripting, automating their own file backups. By the time they reach Docker and Kubernetes, they understand the “why” behind the tools. They face issues, but they have the fundamental knowledge to debug them.
Lesson Learned: Structured growth leads to competence and confidence.
Common Beginner Mistakes
Skipping Linux: Thinking you can bypass the command line. You cannot. Tool Hopping: Learning a little bit of Jenkins, then switching to GitLab, then to CircleCI. Pick one, master it, then switch if needed. Passive Learning: Only watching videos. If you don’t type the code, you don’t learn it. Ignoring Fundamentals: Trying to learn advanced cloud architecture without knowing basic networking. Lack of Documentation: Not writing down what you did. You will forget. Best Practices for Learning DevOps Successfully
Practice Daily: Consistency is better than intensity. One hour a day is better than ten hours on Saturday. Build Real Projects: Build a website, host it, monitor it, and automate its deployment. Learn to Debug: Don’t just look for the answer. Read the error message. Use Google, use documentation, use community forums. Join Communities: Find study groups or online forums. Use Documentation: Read the “official documentation” of the tool (e.g., Docker docs, Terraform docs) instead of just relying on third-party tutorials. Role of DevOpsSchool in DevOps Learning
DevOpsSchool provides a path that cuts through the noise of the industry. Many beginners struggle because they lack a structured environment that prioritizes hands-on experience over theoretical lecturing. By focusing on practical application, they ensure that learners get exposure to the tools they will actually use in the workforce. Their curriculum is designed to help students build the CI/CD pipelines and cloud infrastructure they need to master, moving from beginner concepts to advanced deployment strategies.
Career Opportunities After Learning DevOps
The job market for DevOps professionals remains strong across the globe. Once you have built your skills, you can target roles such as:
Junior DevOps Engineer: Focusing on CI/CD pipelines and automation. Cloud Engineer: Managing cloud infrastructure on platforms like AWS, Azure, or GCP. SRE (Site Reliability Engineer): Focusing on the availability, latency, and performance of systems. Platform Engineer: Building internal tools that help other developers deploy code faster. Automation Engineer: Specializing in writing scripts to reduce manual work. Skills needed for these roles include strong Linux proficiency, experience with cloud services, a deep understanding of CI/CD, and the ability to monitor and manage distributed systems.
Industries Hiring DevOps Professionals
DevOps is no longer limited to tech startups. Every modern company is a software company.
SaaS Platforms: Need 24/7 uptime and rapid release cycles. Banking & Finance: Need strict compliance, security, and automated auditing. Healthcare: Require secure, reliable, and scalable infrastructure to manage patient data. E-Commerce: Need to handle massive spikes in traffic during sales. Telecom: Moving legacy systems to the cloud, requiring migration experts. Future of DevOps Learning
The future of DevOps is moving toward Platform Engineering, where teams provide “internal developer platforms” (IDP) that make it easier for developers to deploy. We are also seeing a massive rise in DevSecOps, where security is integrated into every step of the pipeline. Additionally, AI-assisted DevOps is becoming real, with AI tools helping to write scripts, detect anomalies, and even fix broken code. The fundamentals, however, will remain the same. The better you understand how a server works, the better you will be able to leverage AI to manage it.
FAQs
How do I start learning DevOps?Start with Linux basics, then networking, then Git. Build a solid foundation before moving to tools like Docker or Kubernetes. Is DevOps hard for beginners?It can be challenging because of the breadth of tools. However, by breaking it down into a step-by-step roadmap, it becomes manageable. Should I learn Linux first?Yes, absolutely. It is the most critical prerequisite. Do I need coding skills?You do not need to be a software engineer, but you need to know how to read and write basic scripts in languages like Bash or Python. How long does it take to learn DevOps?A solid foundation can be built in 6 to 12 months with consistent daily practice. Can freshers learn DevOps?Yes, many freshers enter the industry through internships or by building a strong portfolio of projects. Which cloud should I learn first?AWS is the most widely used, so it is a good place to start, but the concepts transfer to Azure and GCP. Is Kubernetes mandatory?For modern DevOps roles, yes, it is increasingly becoming a standard skill. Do I need a degree to get a DevOps job?While a degree helps, practical skills and a solid portfolio of work often matter more to employers. What is the difference between SRE and DevOps?DevOps is the culture/methodology; SRE is a specific engineering approach to implementing that culture with a focus on reliability. How much coding do I actually do?It varies by role, but you will spend a lot of time writing scripts for automation and configuration files (like YAML for Kubernetes). Can I learn DevOps by myself?Yes, but it is often faster and less frustrating to follow a structured roadmap or a course. Is it better to learn one tool or many?Master one tool in a category (e.g., learn Terraform for IaC) rather than learning a little bit of five different tools. What is a “Pipeline”?A pipeline is a series of automated steps that code goes through, from the moment it is written to the moment it is running in production. How do I show my skills to recruiters?Maintain an active GitHub profile with documentation and clear examples of projects you have built. Final Thoughts
Starting to learn DevOps is a significant commitment, but it is one of the most rewarding paths you can take in the technology sector. It requires patience, a relentless drive to solve problems, and a commitment to continuous learning. Remember that you do not need to know everything on day one. You only need to know the next step.
Start with your Linux fundamentals. Set up a simple project. Build a script that does something useful, even if it is small. Celebrate those small wins. Over time, those small wins will compound into the deep, practical expertise that defines a senior DevOps professional. Stay focused, stay consistent, and keep building.
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iOS 27: What We Know About the New Siri App

iOS 27, iPadOS 27, and macOS 27 will include a standalone Siri app for the first time, providing a dedicated space for interfacing with ‌Siri‌.


Siri Chatbot

Apple needs a ‌Siri‌ app because ‌Siri‌ is turning into a chatbot. ‌Siri‌ will work like ChatGPT or Claude, able to pull information from the web to provide answers to questions.

‌Siri‌ will be integrated into iOS, iPadOS, and macOS at the system level, and can draw on device information. It will know more personal context than before, and will be able to access emails, texts, photos, calendar information, contacts, notes, and other personal data. Some of what ‌Siri‌ will be able to do:

Search the web for information
Generate images
Generate content
Summarize information
Analyze uploaded files
Use personal data to complete tasks
Write emails, notes, and texts
Control device features and settings
Search for on-device content, pulling information from emails, messages, files, and more

‌Siri‌ will be integrated into Apple apps like Mail, Messages, Photos, and Apple TV.
Siri App Design

The standalone ‌Siri‌ app will look similar to the ChatGPT, Claude, or Gemini apps. Bloomberg's Mark Gurman shared a mockup of what the ‌Siri‌ app will look like.


Image via Bloomberg

‌Siri‌ will support text or voice-based conversations. The app will open with an "Ask ‌Siri‌" bar where users can type in a question. A paperclip icon will be available for attaching images, PDFs, and other documents. Apple will provide prompts with suggestions on what users can ask.

Questions will resemble iMessage chat bubbles, with Apple adopting a design that is familiar to users. Responses will include links, images, and other information.


Image via Bloomberg.

A section of the app will be dedicated to past conversations that can be shown in a card-style interface with conversation summaries, or a list view. Users will be able to tap into a conversation to continue it.

Dark Interface

Apple's ‌Siri‌ interface both inside and outside of the dedicated ‌Siri‌ app will adopt dark colors. Apple's WWDC website hints at the colors it plans to use for ‌Siri‌.



The website features the Swift bird logo in white on a black background, with subtle highlights in pink, dark blue, purple, and orange. The colors are reminiscent of the current ‌Siri‌ animation that surrounds the iPhone's display when ‌Siri‌ is activated, but the shades are softer and not as saturated.

WWDC 2026

The updated version of ‌Siri‌ will be unveiled at WWDC 2026, which is set to begin on Monday, June 8 at 10:00 a.m. Pacific Time.Related Roundup: iOS 27Tag: Siri
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Meta AI Support Bot Helped Hackers Hijack Instagram Accounts

Meta's AI support assistant has been helping hackers get access to high-profile Instagram accounts, according to reports on social media. With no verification check, Meta AI would change the email address associated with an Instagram account, allowing the password to be updated.


Meta introduced its AI support assistant back in December with the aim of making it easier for customers to access 24/7 account support. It can be used for reporting scams, getting information on content removal, and resetting passwords. The latter option is what bad actors were able to exploit.

The Instagram vulnerability showed up on social media over the weekend, with demonstrations of the simple steps taken to get access to an account. In one demo, a hacker asks Meta's support bot to change the email address linked to a target Instagram account, and the AI does it without question.

Meta's support did not do robust identity verification, and in some cases, it appears it bypassed two-factor authentication. All that was required was a VPN connection set to a location near the target account, which is trivial. Meta appeared to be verifying account ownership based on location. "Our systems recognize the device you usually use and familiar locations better than ever," reads Meta's blog post on its AI support agent. In some cases, users were asked to verify their identity with a selfie, which was bypassed using AI.

For a short period of time, the exploit was available to the public, and account takeovers ramped up. One security researcher said Telegram channels that offer black market Instagram services "made lots of $$$" with Meta's AI. 404 Media said hackers have been aware of the exploit since March.

Meta patched the issue over the weekend, and today, Meta's VP of communications Andy Stone said the issue has been fixed. Meta is now "securing impacted accounts."

Information about the Instagram attack vector comes after hackers were able to take over accounts for Sephora, the Chief Master Sergeant of the Space Force, researcher Jane Manchun Wong, developer Albert Renshaw who owned @albert, and the archived Barack Obama White House account. Multiple other users with desirable Instagram handles reported having their accounts taken.

Some users who have had their accounts stolen over the weekend were not able to use the AI to get their accounts back, and there was no option to speak with a human for help.Tags: Instagram, Meta
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Apple Invites WWDC 2026 Attendees to 'The Mandalorian and Grogu' Screening at Apple Park

Developers that have been invited to watch the WWDC 2026 keynote at Apple Park are also able to attend a special screening of The Mandalorian and Grogu.


The screening will take place at 8:00 p.m. Pacific Time on Tuesday, June 9 at the Steve Jobs Theater. Apple says that a "special guest" will be in attendance, with the doors set to open at 7:00 p.m. There is no word on the special guest, but the movie stars Pedro Pascal as the Mandalorian and Jon Favreau directed. Favreau reportedly used the Apple Vision Pro headset to preview the IMAX version of the film while working on it, which explains why Apple is planning to screen the movie.


Apple says that theater capacity is limited, and developers can RSVP to attend on Thursday, June 4 on a first-come, first-served basis on the event site.

Developers were able to enter a lottery to attend an in-person WWDC event in Cupertino, California. Apple picked lottery winners earlier this year. Attendees will also be able to watch the keynote and Platforms State of the Union, plus meet with Apple experts one-on-one and in group labs.

The Mandalorian and Grogu came out in the U.S. on May 22, and it is the latest film in Disney's Star Wars franchise.Related Roundup: WWDC 2026Related Forum: Apple, Inc and Tech Industry
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Apple Card Promo Offers $30 Uber Cash Back With Uber Eats

Apple Card and Uber One users can earn up to $30 Uber Cash through August 31 by using their ‌Apple Card‌ on Uber Eats. Users can earn $10 Uber Cash each month in June, July, and August by making one eligible order per month on Uber Eats using their ‌Apple Card‌.



‌Apple Card‌ users are also eligible to receive a six-month free trial of Uber One when signing up with your ‌Apple Card‌ and Apple Pay. After the six-month trial, your Uber One subscription will automatically renew at $9.99 per month.

In addition to these promos, Apple partners with multiple vendors to offer three percent Daily Cash back on ‌‌‌‌Apple Pay‌‌‌‌ purchases made with ‌‌‌‌Apple Card‌‌‌‌, including Uber Eats. Three percent cash back can also be earned from Nike, Ace Hardware, Uber, Hertz, Walgreens, Exxon Mobil, and Apple's own retail stores.Tags: Apple Card, Uber
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Apple Cash in iOS 27 Will Help You Split Bills With Just a Photo

iOS 27 will include a nice quality-of-life improvement for those who frequently split bills with friends and family, allowing them to easily take a photo of a receipt and generate payment requests for different people, according to Bloomberg's Mark Gurman.


The feature will be tied to the peer-to-peer Apple Cash feature in the Wallet app, which lets users easily send money to other people and even make purchases.Gurman says that Apple is intending to announce the new feature "as early as next week" at WWDC, and it should be included in the upcoming ‌iOS 27‌ release. Notably, Apple Cash is currently only available in the United States.

The bill-splitting feature will be available through the Wallet and Messages apps, and users will be able to approve payments from an Apple Watch.

This functionality isn't the only Apple Wallet improvement coming ‌iOS 27‌, as the update will also bring the ability to let users create their own digital passes by scanning items like movie tickets, concert passes, and gym membership cards.Related Roundup: iOS 27Tags: Apple Cash, Bloomberg, Mark Gurman
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Oracle’s first monthly patch release fixes 35 flaws, including 11 rated ‘critical’

Oracle has released the first security fixes in its new monthly Critical Security Patch Update (CSPU) cycle, designed to address urgent vulnerabilities that can’t wait for the company’s quarterly patching. The initial batch addresses 35 flaws, including several for which exploit code is publicly available.
In total, there are 11 flaws rated ‘critical’, 18 rated ‘high’, and 6 ‘medium’. The most important on paper are 10 critically-rated flaws, including those affecting Oracle REST Data Services (CVE-2026-46840, CVE-2026-46775, CVE-2026-46839), Oracle E-Business Suite (CVE-2026-46822), the Oracle Universal Work Queue portal (CVE-2026-46824), and Oracle Payments (CVE-2026-46817).
Despite the high CVSS scores for those bugs, patching teams will probably want to start with a clutch of older but still serious flaws for which proof-of-concept (PoC) exploit code reportedly exists: CVE-2025-15467, CVE-2025-58050, and CVE-2026-25646 in Oracle Communications Unified Assurance network management, and CVE-2026-2332 in Oracle REST Data Services.
All relate to open source components embedded in Oracle products, and one, CVE-2025-58050, was first made public last August, underlining how long it can take to patch supply chain flaws in modern platforms.
Another priority fix should be CVE-2026-46840, with a perfect CVSS rating of ’10’. It’s a vulnerability in the backend-as-a-service component of REST Data Services versions 24.2.0 through 26.1.0.
REST Data Services is a gateway that exposes corporate databases via APIs. This flaw makes that interface easily exploitable by an unauthenticated attacker via HTTPS, resulting in a takeover of the gateway, making it a high priority for attackers.
Also deserving to be on the high priority list are the two flaws affecting the REST Data Services core, CVE-2026-46775 and CVE-2026-46839. Rated CVSS 9.9, all that stops these from being CVSS 10 flaws is the need for network credentials to exploit them.
Oracle ‘third Tuesday’
Announced earlier this month, the monthly CSPU is meant to be a smaller update patching high-severity flaws ahead of the larger, more general Critical Patch Updates (CPUs) updates that will continue to be released on a quarterly basis. The initial CSPU was released last Thursday.
In its update notes, Oracle said that the CSPU “provides targeted, high-priority security fixes in a smaller, more focused format, making them easier to apply with minimal disruption.”
Despite the publicity around automated AI vulnerability hunting systems such as OpenAI’s Trusted Access for Cyber program or Claude Mythos, both of which Oracle has said it has access to, none of May’s vulnerability discoveries were attributed to these systems.
The change to a monthly update cycle brings Oracle into line with software vendors such as Microsoft and Adobe, and appears to be a reaction to the growth in the volume of more serious vulnerabilities now being reported.
In the future, the company will release CSPUs on the third Tuesday of each month, with the first four scheduled for June 16, July 21, August 18, and September 15. Oracle cloud customers are patched automatically.

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CSOonline

Oracle’s first monthly patch release fixes 35 flaws, including 11 rated ‘critical’

Oracle has released the first security fixes in its new monthly Critical Security Patch Update (CSPU) cycle, designed to address urgent vulnerabilities that can’t wait for the company’s quarterly patching. The initial batch addresses 35 flaws, including several for which exploit code is publicly available.
In total, there are 11 flaws rated ‘critical’, 18 rated ‘high’, and 6 ‘medium’. The most important on paper are 10 critically-rated flaws, including those affecting Oracle REST Data Services (CVE-2026-46840, CVE-2026-46775, CVE-2026-46839), Oracle E-Business Suite (CVE-2026-46822), the Oracle Universal Work Queue portal (CVE-2026-46824), and Oracle Payments (CVE-2026-46817).
Despite the high CVSS scores for those bugs, patching teams will probably want to start with a clutch of older but still serious flaws for which proof-of-concept (PoC) exploit code reportedly exists: CVE-2025-15467, CVE-2025-58050, and CVE-2026-25646 in Oracle Communications Unified Assurance network management, and CVE-2026-2332 in Oracle REST Data Services.
All relate to open source components embedded in Oracle products, and one, CVE-2025-58050, was first made public last August, underlining how long it can take to patch supply chain flaws in modern platforms.
Another priority fix should be CVE-2026-46840, with a perfect CVSS rating of ’10’. It’s a vulnerability in the backend-as-a-service component of REST Data Services versions 24.2.0 through 26.1.0.
REST Data Services is a gateway that exposes corporate databases via APIs. This flaw makes that interface easily exploitable by an unauthenticated attacker via HTTPS, resulting in a takeover of the gateway, making it a high priority for attackers.
Also deserving to be on the high priority list are the two flaws affecting the REST Data Services core, CVE-2026-46775 and CVE-2026-46839. Rated CVSS 9.9, all that stops these from being CVSS 10 flaws is the need for network credentials to exploit them.
Oracle ‘third Tuesday’
Announced at the beginning of May, the monthly CSPU is meant to be a smaller update patching high-severity flaws ahead of the larger, more general Critical Patch Updates (CPUs) updates that will continue to be released on a quarterly basis. The initial CSPU was released last Thursday.
In its update notes, Oracle said that the CSPU “provides targeted, high-priority security fixes in a smaller, more focused format, making them easier to apply with minimal disruption.”
Despite the publicity around automated AI vulnerability hunting systems such as OpenAI’s Trusted Access for Cyber program or Claude Mythos, both of which Oracle has said it has access to, none of May’s vulnerability discoveries were attributed to these systems.
The change to a monthly update cycle brings Oracle into line with software vendors such as Microsoft and Adobe, and appears to be a reaction to the growth in the volume of more serious vulnerabilities now being reported.
In the future, the company will release CSPUs on the third Tuesday of each month, with the first four scheduled for June 16, July 21, August 18, and September 15. Oracle cloud customers are patched automatically.

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Miasma Supply Chain Attack Compromises Red Hat npm Packages with Credential-Stealing Worm

A new Mini Shai-Hulud supply chain attack campaign, codenamed Miasma, has compromised @redhat-cloud-services packages to steal credentials and secrets from developer machines and deliver a self-propagating worm. "This is effectively a Mini Shai-Hulud campaign: it uses the same core tactics of install-time execution, credential harvesting, CI/CD targeting, encrypted exfiltration, and potentialView the full article
Hacker News

Hackers Used Meta’s AI Support Bot to Seize Instagram Accounts

The Instagram accounts for the Obama White House and the Chief Master Sergeant of the U.S. Space Force were briefly defaced with pro-Iranian images and messages over the weekend, after instructions began circulating on Telegram showing how to trick Meta’s “AI support assistant” bot into resetting account passwords.
A screenshot from a video released on Telegram claiming to show how Meta’s AI customer support bot could be tricked into resetting a target’s password.
On May 31, word began to spread on several Telegram instant message channels that Meta’s AI bot would happily add an email address to an existing account as part of the bot’s standard password reset flow.
A video released on Telegram by pro-Iran hackers claimed to document a remarkably simple exploit that appears to have involved using a VPN connection with an IP address that is in or near the target’s usual hometown, requesting a password reset for the account, and then choosing to chat with Meta’s AI support assistant. From there, the video shows the attacker told the bot to link the account in question to a new email address, after which the bot dutifully sent that address a one-time code that allowed a password reset.
The Telegram account that posted the video also linked to screenshots of pro-Iran images, videos and messages that defaced the hacked Instagram accounts, saying hackers had used the exploit to hijack a number of valuable (read: short) Instagram account names that allegedly have a resale value of more than a half million dollars.
Meta has not responded to requests for comment on the video’s claims, but the company reportedly did acknowledge the dormant Instagram account for the Obama White House was briefly compromised. The security blog thecybersecguru.com reports that Meta pushed an emergency patch over the weekend, and clarified that no back end database was breached.
“Instagram has notoriously poor human support infrastructure,” Cybersecguru wrote. “Recovering a locked account – especially a high-value one can take weeks of back-and-forth with an automated ticketing system. Meta’s solution was to deploy a conversational AI layer to handle common recovery workflows: relinking a lost email address, triggering a password reset, verifying account ownership. The assistant, presumably, was supposed to reduce friction for legitimate users stuck in account-access hell.”
Ian Goldin, a threat researcher at Lumen’s Black Lotus Labs, said we’re entering unchartered security territory as more large online platforms start allowing AI chatbots to handle sensitive account recovery requests. Just like human customer support employees can be social engineered into providing unauthorized access to someone’s account, AI bots are equally eager to help and vulnerable to persuasion and trickery, he said.
“AI chatbots create interesting new attack surface, and we’re likely going to see a lot more of these kinds of attacks,” Goldin said.
Securing your various online accounts means taking full advantage of the most secure form of multi-factor authentication (MFA) offered (such as a passkey or security key). In this case, even using the least robust form of MFA that Instagram offers — a one-time code sent via SMS — likely would have blocked the exploit: The hackers who released the video on Telegram said their exploit failed to work against any accounts that had MFA enabled.
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Krebs

Apple Releases macOS Tahoe 26.5.1 to Fix Shutdown Issue Affecting Enterprise Users on M5 Macs

Apple today released macOS Tahoe 26.5.1, a small update to the ‌macOS Tahoe‌ operating system that came out last year. ‌macOS Tahoe‌ 26.5.1 comes three weeks after Apple released ‌macOS Tahoe‌ 26.5.


Mac owners can download the software by opening the System Settings app and then navigating to the Software Updates section.

According to Apple's release notes for the update, ‌macOS Tahoe‌ 26.5.1 addresses an unexpected shutdown issue affecting certain enterprise users on M5 Macs.macOS 27 is right around the corner, with Apple set to unveil the next major macOS update at the WWDC 2026 keynote on Monday, June 8.Related Roundup: macOS TahoeRelated Forum: macOS Tahoe
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Apple Releases iOS 26.5.1 to Fix Charging Issue on iPhone Air and iPhone 17 Models

Apple today released iOS 26.5.1, a minor update to iOS 26. The software is available three weeks after iOS 26.5 came out, and appears to only be available for the iPhone Air and all models in the iPhone 17 lineup.


The new software can be downloaded on eligible iPhones over-the-air by going to Settings > General > Software Update.

According to Apple's release notes, the update fixes a previously documented charging issue with ‌iPhone Air‌ and ‌iPhone 17‌ models.Apple's work on ‌iOS 26‌ is winding down as it prepares to introduce iOS 27 at the June 8 WWDC keynote event.Related Roundups: iOS 26, iPadOS 26, iPhone 17, iPhone AirBuyer's Guide: iPhone 17 (Neutral), iPhone Air (Neutral)Related Forums: iOS 26, iPhone
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Dell Says Its New $699 Laptop Has Features 'You Won't Find on a MacBook Neo'

Dell this week introduced a new version of the XPS 13, a laptop that it said is "contending with the MacBook Neo on price, and exceeding it on features."


In the U.S., the XPS 13 starts at $699 for the general public and at $599 for eligible students, which is $100 more than the MacBook Neo on both fronts. However, Dell said the XPS 13 offers the following six features "you won't find on a MacBook Neo."

A touch screen
A backlit keyboard
A faster second USB-C port (10 GB/s vs. 480 MB/s)
Wi-Fi 7 (vs. Wi-Fi 6E)
Windows Hello to unlock laptop via facial recognition (MacBook Neo does offer Touch ID at the same $699 price point)
Four speakers (vs. two)

"Apple's MacBook Neo is a capable machine, and its arrival confirms that there's real appetite for premium quality at accessible prices," said Dell. "Where Dell differs is what we think premium means at this price point and what we were willing to build to deliver it."

While not mentioned in Dell's list above, the XPS 13's display offers up to a 120Hz refresh rate and 100% coverage of the DCI-P3 color gamut, whereas the MacBook Neo has a 60Hz refresh rate and sRGB coverage only. And with a 13-inch display and a resolution of 2,560×1,600 pixels, the XPS 13 offers Retina-like quality.

Like the MacBook Neo, the XPS 13 base model is equipped with 8GB of RAM and 256GB of SSD storage inside a thin aluminum enclosure. The base model is powered by Intel's new Core Series 3 processor, with higher-priced configurations offering Intel's Core Ultra Series 3 processors, up to 32GB of RAM, and up to 1TB of storage.

Apple silicon offers industry-leading performance per watt, allowing for the MacBook Neo with an A18 Pro chip to have a fanless design. In the XPS 13, there are two fans.

Dell said the XPS 13 is the thinnest and lightest XPS laptop it has ever made. It measures 12.7mm thin, matching the MacBook Neo, but its advertised weight of 2.2 pounds comes in half a pound below the MacBook Neo.

The XPS 13 base model with a Core Series 3 processor is arriving "soon" in the U.S., according to Dell. The laptop will come in two finishes, Sky and Storm, with the latter color not available until "later this summer."

Windows vs. macOS remains an important factor, but increased competition is good for all customers, as it helps to lower prices across the board.

"A few months ago at CES, we made a commitment: compete at every price point in the consumer market and build products worthy of the XPS name," said Dell. "Even though memory shortages have pushed component costs higher across virtually every industry, we are delivering on that commitment."

Without the MacBook Neo, which was rumored since June 2025, we might not be in this situation.Related Roundup: MacBook NeoTags: Dell, WindowsBuyer's Guide: MacBook Neo (Buy Now)Related Forum: MacBook Neo
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One Week to Go: Apple Gets Ready for WWDC 2026 With YouTube Placeholder

We're just a week out from the 2026 Worldwide Developers Conference, and Apple's keynote will take place on Monday, June 8 at 10:00 a.m. Pacific Time or 1:00 p.m. Eastern Time. Ahead of the event, Apple has launched its WWDC 2026 YouTube event placeholder.


Apple's YouTube page has a "Notify me" button that lets you set a reminder for the keynote in your local time. It's a useful way to make sure you're ready to watch when the event happens because you'll get a notification ahead of when the livestream begins.

The ‌WWDC 2026‌ keynote will be streamed on YouTube, on the Apple Events page, and in the Apple TV app. We'll also have coverage on MacRumors.com for those who are unable to watch.

At this year's event, Apple will introduce the latest versions of its software, including iOS 27, iPadOS 27, macOS 27, watchOS 27, tvOS 27, and visionOS 27. The main focus will be on Siri and the major AI updates coming to Apple's personal assistant.

‌Siri‌ is going to be much smarter, with chatbot-like capabilities and a dedicated ‌Siri‌ app. We have details on what to expect in our iOS 27 roundup.Related Roundup: WWDC 2026Related Forum: Apple, Inc and Tech Industry
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Apple Shares WWDC26 Wallpaper, Playlist, 'Get Ready' Video, and More

In addition to teasing WWDC 2026 with a new tagline today, Apple has shared a wallpaper, playlist, and a "Get Ready" video ahead of the event.


iPhone, iPad, and Mac versions of the wallpaper are available to download on Apple's website. The wallpaper features a dark color scheme with a glowing Apple logo, which likely hints at Siri's rumored new design coming with iOS 27.

The wallpaper page has a "Glow all out" tagline, which adds to the "All systems glow" and "Coming bright up" taglines that Apple previously shared.

A new "WWDC26 Hello" playlist is available on Apple Music, with more playlists to follow throughout the weeklong developers conference.


WWDC 2026 kicks off with Apple's keynote on Monday, June 8 at 10 a.m. Pacific Time. The company is set to unveil iOS 27, iPadOS 27, macOS 27, watchOS 27, tvOS 27, and visionOS 27 on that day, and the conference will run through Friday, June 12, with hundreds of developer sessions to be shared online.

The keynote will be streamed live on the Apple Events website — the page is now live. There will also be streams in the Apple TV app and on YouTube.

For developers, Apple has shared a new "Get Ready" video that offers tips on how to take advantage of WWDC, with all content and resources to be released for free as always. While there will be an in-person component at Apple Park for some lucky attendees, WWDC has largely been an online event since 2020.


MacRumors will be attending WWDC 2026 in person, and we will have in-depth coverage of the event as always, so stay tuned.Related Roundup: WWDC 2026Related Forum: Apple, Inc and Tech Industry
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What is Sandbox Security?

If you’re already familiar with sandboxing as an isolation technique, sandbox security is the next layer: the policies, controls, and enforcement mechanisms that make sure those isolation boundaries actually hold under real-world pressure.
According to our State of Agentic AI report, 40% of respondents cite security as the top challenge in scaling agentic AI, and 43% point to increased security exposure from orchestration sprawl. As agents execute code, call APIs, and interact with live infrastructure, a sandbox without strong enforcement is a locked room with an open window.
This piece goes deeper into what sandbox security looks like day to day. We’ll cover how to choose the right implementation model and why this layer of security matters now more than ever as AI agents start executing code in your infrastructure.
What sandbox security means in practice
Sandbox security is the set of controls and enforcement mechanisms that prevent untrusted or risky processes from breaching their isolation boundaries. Where sandboxing creates the boundary, sandbox security ensures it holds.
As we mentioned before, a sandbox without strong security controls is like a locked room with an open window. The isolation exists in theory, but the enforcement gaps leave room for escape.
For developers and platform engineers, this translates into concrete, daily decisions: which system calls an agent is allowed to make, whether a process can reach the network, how much memory or CPU it can consume, and what happens when it tries to exceed those limits. These are not abstract policy questions. They’re flags you set, profiles you configure, and defaults you either audit or accept on faith.
5 Core components of sandbox security
Sandbox security is not a single control. It’s a combination of mechanisms that work together to keep isolation boundaries intact. The most effective implementations layer several of these components so that a failure in one area does not compromise the entire sandbox.
1. Process isolation
Process isolation ensures that code running inside a sandbox has no visibility into processes on the host or in other sandboxes. On Linux, kernel namespaces handle this by partitioning process IDs, network interfaces, file systems, and user IDs into separate scopes. A process inside a namespace sees only what you’ve explicitly made available to it.
When things go wrong. Run a container with –pid=host and you’ve just given that workload a window into every process on the machine. It can enumerate services, identify targets, and attempt to interfere with them. That single flag turns your sandbox into a shared apartment. 

Proper sandbox security eliminates this by enforcing strict namespace boundaries by default and flagging configurations that weaken them.
2. System call filtering
Even within a namespace, processes interact with the host kernel through system calls. System call filtering (commonly implemented through seccomp profiles on Linux) restricts which kernel functions a sandboxed process can invoke. Docker’s default seccomp profile blocks around 44 of the 300+ available Linux system calls. That’s a meaningful reduction in attack surface, but it’s a general-purpose default, not a tailored fit.
What to look for. High-security workloads benefit from custom seccomp profiles scoped to the specific application. A sandboxed process that needs to read files and make HTTP requests has no reason to call mount, init_module, or reboot. The tighter the profile, the fewer options an attacker has if they gain code execution inside the sandbox. It’s the same least-privilege thinking that underpins container security more broadly.
3. Network segmentation
A sandbox that can communicate freely with external systems or internal services is harder to defend. Network segmentation restricts what a sandboxed process can reach, limiting both inbound and outbound connections. That’s especially important for workloads that process untrusted input or execute arbitrary code.
How this applies to agents. AI agents that invoke external tools or APIs during execution present a unique challenge. Without network controls, a compromised agent could exfiltrate data to an external endpoint or pivot to internal services it was never intended to reach. Enforcing egress policies at the sandbox environment level ensures agents can only communicate with pre-approved destinations.
4. Resource limits and quotas
Resource exhaustion attacks do not require a sandbox escape, and that’s what makes them easy to overlook. A runaway process that consumes all available CPU or memory can take down every other workload on the same host without ever breaching an isolation boundary. Cgroups on Linux cap what each sandbox can consume, turning a potential host-wide outage into a single contained failure.
The tricky part is calibration. Set memory limits too low and legitimate workloads get OOM-killed. Set them too high and you’re back to sharing the blast radius. The most reliable approach is to monitor actual resource consumption over time, set limits based on observed peaks plus a margin, and treat the initial configuration as something you’ll tune rather than something you’ll get right on the first pass.
5. Runtime monitoring and audit trails
Prevention is only part of the equation. You also need to know what’s happening inside the sandbox. Runtime monitoring tools observe system calls, file access patterns, network connections, and process behavior as they occur. When something deviates from the expected baseline, the system can alert operators or kill the process automatically. If you’re evaluating AI governance tools, you’ll find that many of these runtime observability capabilities overlap directly with agent monitoring requirements.
Audit trails serve a different but equally important purpose. When an incident does happen, you need a forensic record of exactly what the sandboxed process did: which files it touched, which endpoints it called, which syscalls it made. That’s valuable for incident response and essential for compliance frameworks that require demonstrable evidence of isolation and access control.
Choosing an implementation model
Understanding the different sandboxing models is a good starting point, but the more useful question for sandbox security is: what does each model actually protect against, and what do you need to configure to make it hold? Here’s how they compare on the dimensions that matter for security decisions.
Model
Isolation boundary
Key security controls
Best for
Watch out for
OS-level
namespaces, seccomp, MAC
Shared kernel, separate namespaces
seccomp profiles, AppArmor/ SELinux policies, read-only rootfs, capability dropping
Container runtimes, CI/CD jobs, most production workloads
Kernel vulnerabilities bypass all controls; defaults are permissive
VM-based
microVMs, hardware virtualization
Separate kernel per sandbox
Hypervisor-enforced memory isolation, independent kernel patching, vTPM
Multi-tenant platforms, malware analysis, running fully untrusted code
Higher resource cost; networking and image management add ops complexity
Application-level
Wasm, browser tabs, language VMs
Within-process memory and API restrictions
Memory-safe execution model, restricted host API surface, capability-based permissions
Plugin systems, edge functions, embedded scripting
App compromise bypasses internal sandbox; should never be the only layer
The right choice depends on your threat model. For most containerized workloads, OS-level controls with a hardened seccomp profile and mandatory access control policy provide strong security at minimal overhead. VM-based isolation makes sense when you genuinely do not trust the code being executed, such as in multi-tenant environments or agent-driven code generation. Application-level sandboxing is a valuable addition in either case, but it should layer on top of kernel-level or hypervisor-level controls, never replace them.
Whichever model you choose, treat the default configuration as a starting point. The security of any sandbox does depend on the isolation technology, but whether someone actually audited the settings is the sticking point. It’s the same software supply chain security discipline that applies at every layer of the stack: trust, but verify the configuration.
Sandbox security for AI agents
Traditional applications follow predictable execution paths. You can read the code, trace the logic, and anticipate the behavior. AI agents are a different story. They make decisions at runtime, generate and execute code on the fly, call external tools, and produce outputs that their own developers may not have anticipated. That autonomy is the whole point of agents, but it’s also what makes sandbox security non-negotiable.
In these situations, perimeter-based security is not sufficient. You need controls that constrain agent behavior at the execution level, regardless of what the agent decides to do. It’s a fundamentally different security challenge. Teams building AI agent sandboxes are converging on a few patterns that address the unique risks agents introduce.
Isolating tool use 
When an AI agent invokes a tool (a code interpreter, a file manager, an API client), each tool execution should run inside its own sandbox with the minimum permissions required. If the agent’s tool-use layer is compromised, sandbox security prevents that compromise from reaching the host or other services.
Controlling data access
Agents often process sensitive data as part of their reasoning. Sandbox security controls which files, databases, and environment variables are visible inside the agent’s execution environment. A well-configured secure sandbox exposes only the data the agent needs for its current task, nothing more.
Enforcing network boundaries
Left unchecked, an agent with network access could make arbitrary HTTP requests, potentially exfiltrating data or interacting with unintended services. Network-level sandbox security restricts egress to an allowlist of approved endpoints.
Getting started with sandbox security
Start with your threat model. Which workloads process untrusted input? Which ones execute arbitrary code or handle sensitive data? Those are your highest-priority candidates for hardened sandbox security.
From there, layer controls rather than relying on any single mechanism. Combine process isolation with system call filtering, add network segmentation, set resource limits, and enable runtime monitoring. Each layer addresses a different category of risk. Together, they create a posture where any single failure stays contained.
If you’re already running containers, much of the foundation is in place. Container runtimes provide namespace isolation, seccomp profiles, and cgroup limits out of the box. The next step is to actually audit those defaults against your requirements and tighten what needs tightening. Docker Sandboxes extend this with purpose-built microVM isolation for agent workloads.
Start with Docker Sandboxes to put sandbox security into practice.
Frequently asked questions
What is the difference between sandboxing and sandbox security?
Sandboxing is the technique of running code in an isolated environment. Sandbox security is the broader discipline of ensuring that isolation actually holds. It’s the policies, configurations, monitoring, and enforcement mechanisms that make a sandbox resistant to escape, resource abuse, and unauthorized access. You can have a sandbox without strong security, but the isolation it provides will be unreliable.
Can sandbox security prevent all container escapes?
No single security measure can guarantee complete protection. Sandbox security significantly raises the bar by layering multiple controls (namespaces, seccomp, network policies, resource limits, runtime monitoring) so that an attacker would need to bypass several independent defenses. This defense-in-depth approach reduces risk to a level most organizations consider acceptable, especially when combined with regular patching and configuration audits.
How does sandbox security affect application performance?
The performance impact varies by implementation. OS-level controls like namespaces and seccomp add negligible overhead. Network policies and resource limits introduce minimal latency. VM-based sandbox security has higher overhead due to hardware virtualization, but technologies like microVMs have narrowed that gap significantly. For most workloads, it’s a trade-off that strongly favors security.
Is sandbox security relevant for AI and machine learning workloads?
Absolutely. AI workloads, particularly agents that execute code dynamically, are among the highest-priority use cases for sandbox security. These workloads are inherently unpredictable, and that’s exactly why strong isolation boundaries are essential. Sandbox security ensures that even if an agent produces unexpected behavior, the impact stays contained within its execution environment.
What compliance frameworks require sandbox security?
Several frameworks reference isolation and access controls that map directly to sandbox security practices. SOC 2 requires logical access controls and monitoring. PCI DSS mandates network segmentation for systems handling payment data. FedRAMP and NIST 800-53 include specific controls around process isolation and boundary protection. Organizations pursuing these certifications often find that container-based sandbox security, guided by a structured AI governance framework, provides a strong implementation foundation.

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Apple Teases Next Week's WWDC 2026 Event: 'All Systems Glow'

Apple's annual developers conference WWDC returns for 2026 next week, and the company has teased the event with a new "All systems glow" tagline.


"All systems glow" is a play on the phrase "all systems go," and it likely hints at Siri's rumored new design on iOS 27. Both a dedicated Siri app and a new "Search or Ask" feature in the iPhone's Dynamic Island will reportedly have a dark color scheme with glowing elements, as shown in leaked images last week.


Apple's previous tagline for WWDC 2026 was "Coming bright up." That tagline and the graphics for the event all hint at the new Siri design as well.

WWDC 2026 kicks off with Apple's keynote on Monday, June 8 at 10 a.m. Pacific Time. The company is set to unveil iOS 27, iPadOS 27, macOS 27, watchOS 27, tvOS 27, and visionOS 27 on that day, and the conference will run through Friday, June 12, with hundreds of developer sessions to be shared online.

Related Roundup: WWDC 2026Tag: SiriRelated Forum: Apple, Inc and Tech Industry
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Foldable 'iPhone Ultra' Rumored to Feature Vapor Chamber Cooling Despite Thin Design

Apple's first foldable iPhone, known as the "iPhone Ultra," will feature impressive vapor chamber cooling and launch in September despite production difficulties, a known leaker today reported.

The iPhone 17 Pro's vapor chamber thermal plate.
In a new post today on Weibo, the leaker known as "Fixed Focus Digital" said the foldable iPhone's pre-assembly manufacturing processes are facing pressure and that the initial production ramp-up is proving difficult. The leaker added that prevailing speculation points to the original September launch schedule holding, and teased that further positive news is expected tomorrow.

The leaker added that the device will feature vapor chamber (VC) cooling and that its thermal performance is "quite impressive," with Apple "really going all out" with its thermal engineering. The claim marks the first time a source has attributed vapor chamber cooling to the ‌iPhone Ultra‌, and the detail is notable given the extent of the design compromises the device is expected to make.

Rumors suggest the ‌iPhone Ultra‌ could be missing at least five features present on the ‌iPhone 17 Pro‌, including Face ID, a telephoto camera, MagSafe, the Action Button, and a physical SIM card slot, largely as a result of its 4.5mm folded thickness. The iPhone Air, which shares a similar ultra-thin philosophy, does not feature vapor chamber cooling, making its presence on the ‌iPhone Ultra‌ far from a given before today's report.

Apple overhauled the thermal design of the ‌iPhone 17 Pro‌ last year, adopting a vapor chamber cooling system for the first time in an iPhone. The system circulates a small amount of deionized water to move heat away from the A19 Pro chip and distribute it throughout the device's aluminum unibody frame, with Apple claiming the design delivers 40% better sustained performance for demanding tasks compared to the graphite thermal systems used in previous Pro models.

The post arrives amid a series of production difficulty reports surrounding the foldable iPhone. Earlier this month, Fixed Focus Digital pointed to yield problems at the pre-assembly stage related to surface-mount technology (SMT), distinct from a separate report by the leaker known as "Instant Digital" that attributed production difficulties to the hinge failing Apple's quality control standards under conditions of prolonged, high-frequency opening and closing.

Fixed Focus Digital's account pushed back on that framing, suggesting the hinge was not the primary source of difficulty. DigiTimes reported in April that production was already running roughly one to two months behind schedule while still maintaining that a fall 2026 launch remained on track, with mass production planned to begin in July. Fixed Focus Digital also reported in April that price negotiations with Apple's assembly partner were a potentially disruptive factor.

Despite the difficulties, the launch timeline does not appear to be at risk. Bloomberg's Mark Gurman reported in April that the ‌iPhone Ultra‌ is on track for a September debut alongside the iPhone 18 Pro and ‌iPhone 18 Pro‌ Max, though he noted the timing was not final and production had yet to ramp up. The device is expected to feature a 7.8-inch inner display, a 5.5-inch cover display, the A20 chip, the C2 modem, Touch ID in place of ‌Face ID‌, and two rear cameras, with pricing rumored to start at around $2,000.Related Roundup: iPhone FoldTags: Fixed Focus Digital, Foldable iPhone, iPhone Ultra
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AirPods Pro 3 Available for $199.99 Low Price on Amazon

Amazon today has the AirPods Pro 3 available for $199.99, down from $249.00. This is a match of the all-time low price on the AirPods Pro 3, and it's a deal that hasn't been as frequent as discounts on the AirPods 4 and AirPods Max 2.

Note: MacRumors is an affiliate partner with some of these vendors. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

This model of the AirPods Pro launched in September 2025 and has 2x better Active Noise Cancellation than the previous generation, better audio quality, a revised fit that's meant to improve comfort and stability, Live Translation for in-person conversations, and heart rate sensing for workouts.

$49 OFFAirPods Pro 3 for $199.99

Head to our full Deals Roundup to get caught up with all of the latest deals and discounts that we've been tracking over the past week.



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Related Roundup: Apple Deals
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⚡ Weekly Recap: New Linux Flaw, PAN-OS Exploit, AI-Powered Attacks, OAuth Phishing and More

Monday hit like a cron job with anger issues. A busted auth path here, a repo-side faceplant there, some "patched-ish" thing already getting chewed on in the wild, and then the usual bonus round: poisoned dev tools, sketchy forum chatter, phishing kits pretending to be productivity, and AI lowering the bar for people who already thought 'curl | sh' had a personality. The vibe is simple: oldView the full article
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Coding Agent Horror Stories: The rm -rf ~/ Incident

This is Part 2 of our AI Coding Agent Horror Stories series, an in-depth look at real-world security incidents exposing the vulnerabilities in AI coding agents, and how Docker Sandboxes deliver workspace-scoped isolation that contains the worst failures at the execution layer.
In part 1 of this series, we mapped six categories of AI coding agent failures and the architectural reason they keep happening: the agent runs as you, on your filesystem, with your credentials, and nothing sits between the model’s decision and the shell’s execution. For Part 2, we’re going deep on the most destructive failure mode in the entire ecosystem: an AI coding agent deleting a developer’s entire home directory in a single command.
Today’s Horror Story: The Tilde That Wiped a Mac
In December 2025, a Reddit user posting under the handle u/LovesWorkin shared what became one of the most-discussed AI coding agent incidents of the year. They had asked Claude Code to clean up an old repository. Claude executed rm -rf tests/ patches/ plan/ ~/, and the trailing ~/ wiped their entire Mac.
This wasn’t a CVE. It wasn’t a sophisticated attack. It was the AI coding agent doing exactly what it was told, in a way the user did not anticipate, with no architectural boundary to catch the mistake.
In this issue, you’ll learn:
How a single trailing slash in a rm -rf command erased a developer’s entire Mac Why the --dangerously-skip-permissions flag exists, and why developers keep using it anyway The pattern this incident shares with the GitHub-issue-#10077 Ubuntu wipe and the Claude Cowork family-photos incident How Docker Sandboxes contains this entire class of failure at the execution layer Why This Series Matters
Each “Horror Story” in this series examines a real-world incident that turns laboratory findings into production disasters. These aren’t hypothetical attacks. They’re documented cases with named victims, screenshotted command logs, and in several cases, public apologies from the vendors. Our goal is to show the human impact behind the security statistics, demonstrate how these failures unfold in practice, and provide concrete guidance on protecting your AI development infrastructure through Docker’s workspace-scoped execution model.
The story begins with something every developer has done: asking the agent to clean up an old repository.
The Problem
On December 8, 2025,a developer posting under the handle u/LovesWorkin shared a Reddit thread on r/ClaudeAI with the title that says everything: “Claude CLI deleted my entire home directory! Wiped my whole mac.” The post climbed past 1,500 upvotes within hours, was amplified by Simon Willison on X, covered by Gigazine in Japan on December 16, and became one of the most-discussed AI coding agent incidents of 2025.
The setup was unremarkable. The user asked Claude Code to clean up packages in an old repository. Routine maintenance, the kind any developer would hand off without thinking. Claude generated and executed:
rm -rf tests/ patches/ plan/ ~/ On the surface, this is a command to delete three project directories. The fatal error is the trailing ~/. In Unix, ~ expands to the user’s home directory. ~/ with the trailing slash means “everything inside the home directory.” Combined with rm -rf, which removes recursively and without confirmation, the command deletes the user’s entire home directory in a single shot.
Within seconds, the developer had lost:
The Desktop, Documents, and Downloads folders The Library folder containing application state for every app on the system The Keychain, which broke authentication across every app, including Claude Code itself, which could no longer talk to its own backend Years of project files, family photos, and work product All of it on an SSD where TRIM had already zeroed the freed blocks by the time recovery was attempted There was no recovery. As the developer put it in the original thread: “It nuked my whole Mac! What the hell?”
Caption: Once an AI agent gains direct filesystem access, “organize my desktop” can become catastrophic.
The Scale of the Problem
This wasn’t a one-off. It was an instance of a pattern.
On October 21, 2025, weeks before the LovesWorkin incident, developer Mike Wolak filed GitHub issue #10077 against the Claude Code repository. Wolak’s report described a similar failure on Ubuntu/WSL2: Claude Code had executed rm -rf starting from root, and the logs showed thousands of “Permission denied” messages for /bin, /boot, and /etc as the agent worked its way through the system trying to delete files it didn’t own. Every user-owned file on the system was gone. Anthropic tagged the issue area:security and bug. The damning detail in Wolak’s report: he was not running with --dangerously-skip-permissions. Claude Code’s permission system simply failed to detect that the agent’s command would expand destructively before the user approved it.
Two weeks later, on November 28, 2025, GitHub issue #12637 documented yet another variant. Claude Code had earlier created a directory literally named ~ by mistake. Later, when the agent tried to clean up that directory by running an unquoted rm -rf ~, the shell expanded ~ to the user’s actual home directory before rm saw the argument. Same destructive outcome, completely different mechanism. The agent had found a new way to destroy a developer’s work.
Shortly after the January 2026 launch of Anthropic’s Claude Cowork, Nick Davidov, founder of a venture capital firm, used Anthropic’s Claude Cowork, a general-purpose AI agent product to organize his wife’s desktop. He explicitly granted permission for temporary Office files only. The agent deleted a folder containing 15 years of family photos, somewhere between 15,000 and 27,000 files, via terminal commands that bypassed the macOS Trash entirely. Davidov recovered the photos only because iCloud’s 30-day retention happened to still be in effect. The Trash had been bypassed entirely.
These aren’t isolated stories. They’re the same story with different file paths.
How the Failure Works
To understand why these incidents keep happening, we need to look at the architecture of how a modern AI coding agent executes commands on a developer’s machine. The agent is doing exactly what its design says it should do. The architecture is the failure.
The Coding Agent (Claude Code, Cursor, Replit, Kiro) is an AI-driven shell. It reads your prompt, reasons about how to satisfy it, generates a command, and runs that command directly on your operating system. There is no separate “execution proposal” step that a human approves. The reasoning step and the execution step are the same step. The User’s Shell is whatever shell the agent inherited when you launched it. On macOS, that’s typically zsh. The agent’s commands run through this shell with the developer’s full user permissions. ~ expands to the developer’s home directory because that’s what ~ means in zsh. Permission Inheritance is implicit and total. Whatever the developer’s shell can do, the agent can do. There is no separate identity for “the agent acting on the developer’s behalf.” The agent is the developer for as long as the session lasts. The --dangerously-skip-permissions Flag, which Lanzani’s technical blog post analyzes in detail, is what removes the one safety net that exists by default. Without the flag, Claude Code asks for confirmation before each shell command. With it, the agent runs commands in the background while the developer goes back to other work. That last point is the one that matters. The flag exists because the default behavior, asking for confirmation on every shell command, makes multi-step tasks tedious. Developers add the flag to make the agent useful. The agent then becomes capable of executing destructive commands without intervention. The flag is named honestly. It is a dangerous flag. But it is also a popular one, because the alternative is approving every ls and cat the agent runs.
The vulnerability happens between steps 2 and 3. The agent reasons about what command to run. The shell executes that command on the host. Nothing sits in between. There is no architectural boundary that says “this command would delete the user’s home directory, refuse to run it.” The shell sees a syntactically valid rm -rf and does what rm -rf does.
Technical Breakdown: How a Trailing Slash Wipes a Mac
Here’s how the incident unfolds, step by step:
Caption: Diagram illustrating how unrestricted AI agent execution can escalate a simple cleanup task into full home-directory destruction
1. The User’s Request
The developer asks Claude Code to clean up packages in an old repository. The prompt is the kind of thing every developer types daily:
Please clean up unused test files, patches, and plan documents from this old repo. 2. The Agent’s Reasoning
The agent identifies three directories that match the request: tests/, patches/, and plan/. It then generates a rm -rf command, because removing directories recursively is the standard way to delete them. So far, this is correct behavior.
3. The Hallucinated Argument
The agent appends ~/ to the command. We don’t know exactly why. Possibly the agent inferred that “clean up” included tidying the home directory. Possibly it generated ~/ as a no-op separator and didn’t realize it was a destructive argument. Possibly its training data included shell snippets where ~/ appears in this position and it pattern-matched. The result either way is the same:
rm -rf tests/ patches/ plan/ ~/ This is a syntactically valid shell command. There is nothing in the syntax that says “this is dangerous.”
4. Shell Expansion
When this command runs in zsh on macOS, the shell expands ~/ to /Users/loveswarkin/. The command becomes, effectively:
rm -rf tests/ patches/ plan/ /Users/loveswarkin/ The shell does not warn. It does not confirm. It does not flag the home directory as protected. There is no system-level check that says “this command would delete a user’s entire home directory.” The shell does what shells do: expand the path and execute.
5. Recursive Force Deletion
rm -rf walks the filesystem under each argument and deletes everything. The Desktop, Documents, Library, Keychain, Application Support folders, Claude Code’s own config and credentials, the user’s SSH keys, the user’s git config, the user’s photos. All of it. In order. Without pausing.
The deletion runs to completion in seconds because most of these files are small, and the SSD’s controller acknowledges deletes nearly instantly. By the time the user notices their terminal is unresponsive and tabs out to check, it’s done.
6. The Aftermath
The keychain is gone, which means every app that authenticates against the keychain is now logged out. Mail, browsers, Slack, GitHub Desktop, every service that stored a token, every saved password. The user’s identity infrastructure on that machine is gone.
Claude Code itself can no longer authenticate, because its own credentials lived in the home directory. The agent that did the destruction can’t even apologize properly, because it can’t connect to its own backend.
The Impact
Within a single command execution, the developer has:
Lost years of personal and professional files Lost cryptographic keys (SSH, GPG) needed to access remote systems Lost authentication state for every app on the system Lost git history for any uncommitted work Inherited a system in a partially-broken state where logging back in and reinstalling apps will take days There is no recovery path. SSDs with TRIM enabled (which is the default on every modern Mac) zero freed blocks at the controller level, so even forensic recovery tools come up empty. The data is not “deleted” in the sense of “marked unavailable but recoverable.” It is gone.
This is what one trailing slash in one AI-generated command produces.

How Docker Sandboxes Eliminates This Attack Vector
The current AI coding agent ecosystem forces developers into the same dangerous tradeoff that the MCP ecosystem forced on users in Part 1 of our companion series. Every time you run claude --dangerously-skip-permissions or any equivalent flag in another agent, you’re executing arbitrary AI-generated commands directly on your host system with full access to:
Your entire file system Your home directory and everything in it Your credentials, keychain, SSH keys, and cloud config Every running process and every network connection your shell can make This is exactly how the rm -rf ~/ incident achieves total system destruction. The agent runs as the developer, on the developer’s filesystem, with no architectural boundary to stop it.
Docker’s Security-First Architecture
Docker Sandboxes represents a fundamental shift in how AI coding agents execute. Rather than running directly on the host with user-level permissions, the agent runs inside a microVM with its own kernel, its own filesystem, and its own network. The agent’s view of ~/ is the workspace mount, not the developer’s actual home directory. The developer’s actual home directory simply does not exist from inside the sandbox.
Docker Sandboxes are managed through the sbx CLI. A quick distinction worth making: Docker Sandboxes are the isolated microVM environments where agents actually run. sbx is the standalone CLI tool used to create, launch, and manage them. Sandboxes are the environments. sbx is what you type to control them.
Docker Sandboxes solves the rm -rf ~/ class of failure by making the destructive command architecturally impossible. The agent can absolutely generate rm -rf tests/ patches/ plan/ ~/. It can absolutely run that command. The command will absolutely succeed. But what gets deleted is the workspace inside the sandbox, not the developer’s actual home directory. The host filesystem isn’t visible from inside the microVM, so there is nothing to delete.
Workspace-Scoped Execution
The most important architectural shift is that the agent’s filesystem view is the workspace mount, and only the workspace mount.
# Install sbx and sign in brew install docker/tap/sbx sbx login # Launch the agent inside a sandbox scoped to the project directory cd ~/my-project sbx run claude Three commands and the agent is now running inside a microVM. From inside the sandbox, the agent’s ~/ IS the workspace, not the developer’s actual home directory. The Library folder, the keychain, the SSH keys, the AWS config – none of that exists inside the sandbox. The agent cannot reach what it cannot see.
A rm -rf ~/ from inside the sandbox deletes the workspace files. The developer can throw the sandbox away with sbx rm and start fresh. The host system is untouched.
Blocked Credential Paths
Even if a developer explicitly mounts additional paths into the sandbox, common credential directories are blocked from being mounted by default:
# Credential roots blocked by default: # ~/.aws ~/.ssh ~/.docker ~/.gnupg # ~/.netrc ~/.npm ~/.cargo ~/.config # A misconfigured mount that tries to include these is rejected # before the sandbox even starts. sbx run claude This blocklist directly addresses the keychain-deletion fallout from the LovesWorkin incident. Even an agent that decides to recursively delete its workspace cannot reach the credentials that keep the developer’s authentication state intact.
Read-Only Mounts for Sensitive Workspaces
For workflows where the agent should read but not write to a directory, the :ro suffix declares a mount as read-only:
# Mount the project workspace as writable, the docs as read-only sbx run --name docs-review claude /path/to/project /path/to/docs:ro A rm -rf against a read-only mount fails at the kernel level. The microVM enforces the mount mode, which means the agent cannot decide to override it through reasoning, prompt manipulation, or flag misuse. The infrastructure decides what’s writable. The model doesn’t get a vote.
Git-Worktree Isolation for Risky Operations
For destructive operations like cleanup tasks, refactors, and “let me just clean this up” requests, sbx run --branch lets the agent operate on an isolated Git worktree:
# Create a sandbox on a fresh feature branch sbx run --name cleanup-agent --branch=cleanup/old-files claude . # Review what got cleaned up before merging sbx exec cleanup-agent git diff main # If the agent did something destructive, throw it away sbx rm cleanup-agent This is the architectural answer to “the agent decided to drop and recreate the schema.” The agent’s changes never touch the main branch until the developer reviews them. If the agent runs rm -rf ~/, the worktree gets wiped and the main branch is untouched. The developer reviews git diff main, sees what happened, and decides whether to merge or discard.
Throwaway Sandboxes by Design
The final piece is that sandboxes are designed to be discarded:
# When the work is done, list active sandboxes and remove the one you're done with: sbx ls sbx rm <sandbox-name> This is what makes the Docker Sandboxes model fundamentally different from running an agent on the host. On the host, a destructive command leaves permanent damage. Inside a sandbox, every session is throwaway. The worst the agent can do is destroy the workspace, which is reproducible from the source repo. The keychain, the credentials, the years of personal data, none of those can be touched, because none of those exist from inside the sandbox.
What This Looks Like in Practice
Here’s the LovesWorkin incident replayed under Docker Sandboxes. The user asks the same question. The agent generates the same command. The shell executes the same expansion.
# After Docker Sandboxes: $ cd ~/my-project $ sbx run claude > Please clean up unused test files, patches, and plan documents [Agent runs: rm -rf tests/ patches/ plan/ ~/] [Workspace inside the sandbox wiped. Host home directory intact.] # The sandbox is throwaway. List it and remove it to start fresh: $ sbx ls $ sbx rm <sandbox-name> The agent’s behavior is identical. The architectural outcome is completely different.
The Practical Improvements
Security Aspect
Traditional AI Coding Agent
Docker Sandboxes
Execution Environment
Direct host execution as the user
Isolated microVM with its own kernel
Filesystem View
Full host filesystem, including ~/
Workspace mount only
Credential Access
All credentials in user’s home dir
Credential paths blocked by default
Destructive Command Impact
Permanent host damage
Throwaway sandbox
Review Before Merge
None
Git worktree isolation with sbx exec <sandbox-name> git diff main
Recovery
Often impossible (TRIM zeroes blocks)
sbx rm and start fresh
Best Practices for Secure AI Coding Agent Deployment
Stop running coding agents directly on your host. Containerization or microVM isolation should be the default, not an advanced option. Use sbx run for every coding task that involves filesystem operations. Especially “clean up,” “organize,” “refactor,” and “delete unused” prompts. These are the prompt categories most likely to produce a destructive rm -rf. Use Git worktrees for destructive operations. sbx run --name <name> --branch=<branch> claude ensures the agent’s changes are reviewable before they touch your main branch. Never use --dangerously-skip-permissions on the host machine. If you need the agent to run commands without per-command approval, run it inside a sandbox. The sandbox boundary is what makes “skip permissions” safe. Treat the sandbox as throwaway. Don’t store anything important inside it. The whole point is that you can sbx rm and start fresh. Audit the policy log. sbx policy log shows every allowed and denied connection attempt, which becomes your forensics trail if something does go wrong. Take Action: Secure Your AI Coding Agent Today
The path to safe AI coding agent execution starts with one command. Here’s how to move away from running agents on the host:
Install Docker Sandboxes. Visit the Docker Sandboxes documentation to install sbx and run your first sandboxed agent in under five minutes. Try it with your existing workflow. sbx run claude (or sbx run cursor, sbx run codex, etc.) drops your existing agent into a microVM with no configuration changes required. Read the architecture deep-dive. The Docker Sandboxes architecture documentation explains the microVM model, the workspace mounting, and the network policy layer. Browse the MCP Catalog. If your agent uses MCP servers, the Docker MCP Catalog provides containerized, verified servers that complement sandboxed agent execution. Conclusion
The LovesWorkin incident, the Mike Wolak Ubuntu wipe, the Claude Cowork family-photos deletion, and the GitHub issue #12637 shell-glob expansion bug are all the same story. An AI coding agent reasoned its way through a task, generated a command that contained a destructive argument, and the shell executed it because there was nothing in the architecture to say “this command would destroy the developer’s work.”
These aren’t bugs in Claude Code, or Cursor, or Kiro, or any individual agent. They’re properties of the execution model. As long as agents run on the host with the user’s permissions, this category of failure will keep happening, with new variations each time.
Docker Sandboxes doesn’t try to make the agent smarter. It changes where the agent runs. The agent gets a workspace. It does not get your machine.
Coming up in our series: Issue 3 will explore the AWS Cost Explorer outage, where Amazon’s own Kiro agent decided to delete and rebuild a production environment in seconds, and what scoped-identity sandbox configuration prevents that class of failure.
Learn More
Run agents safely with Docker Sandboxes: Visit the Docker Sandboxes documentation to get started with workspace-isolated agent execution in minutes. Explore the Docker MCP Catalog: Discover MCP servers that connect your agents to external services through Docker’s security-first architecture. Download Docker Desktop: The fastest path to a governed AI agent environment, with Docker Sandboxes, MCP Gateway, and Model Runner in a single install. Read the MCP Horror Stories series: Start with issue 1 to understand the protocol-layer security risks that complement the agent-layer risks covered here. View the full article
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Flowise’s MCP implementation can run ghost commands

Enterprises using the lightweight, open-source Flowise platform to power self-hosted AI workloads have a new near-max severity issue to worry about.
Researchers at Obsidian Security have detailed a one-click remote code execution (RCE) vulnerability affecting self-hosted Flowise deployments through its implementation of Model Context Protocol (MCP) stdio servers.
The problem is essentially a sandboxing failure of attacker-controlled MCP configurations, leading to server-side code execution.
“Post-auth RCE in Flowise can be triggered with a single click via a malicious chatflow import before any save or run,” the researchers said in a blog post. “The official patch relies on input validation that is trivially bypassed and fails to address the root cause.”
Flowise is commonly used to develop internal AI assistants, retrieval-augmented generation (RAG) applications, customer-facing chatbots, and autonomous agents connected to business systems.
The flaw does not affect Flowise Cloud, as stdio MCP is disabled there. For the rest, where the feature is enabled and is absolutely necessary, there is a security and functionality tradeoff developers need to understand and actively review server configurations for possible threats, the researchers explained.
Once-click RCE affects everything Flowise can reach
The vulnerability, tracked as CVE-2026-40933, affects Flowise’s implementation of MCP stdio servers. MCP’s stdio is designed to launch local server processes and communicate with them through standard input and output streams, allowing AI agents to interact with files, Git repositories, databases, browsers, and local credentials.
According to Obsidian Security, the issue stems from Flowise allowing users to configure MCP stdio servers containing arbitrary commands. Because those commands are ultimately executed by the underlying operating system, an attacker can achieve remote code execution with the privileges of the Flowise process.
In containerized deployments, the researchers noted, this can effectively provide root-level access to the environment hosting the platform.
The flaw has been assigned a 9.9 CVSS rating, with a successful compromise potentially exposing API keys, databases, cloud resources, SaaS applications, and other assets accessible through Flowise.
Researchers said the fixes fall short
The disclosure details a series of remediation efforts by Flowise aimed at restricting how MCP stdio commands can be configured and executed. According to Obsidian, however, each iteration relied primarily on command validation and filtering mechanisms that can be bypassed under certain conditions.
“Flowise appeared to acknowledge the risk and hardened Custom MCP over several rounds,” the researchers noted. “#5232 introduced CUSTOM_MCP_SECURITY_CHECK, a default-enabled validation layer for Custom MCP configurations.” While the checks reduced obvious command execution paths, they did little to change the underlying threat of allowing users to supply stdio MCP configurations, they said.
Obsidian’s reporting of the flaw triggered further hardening of the feature with flag validation in updates #5741 and #5943. These, too, did not entirely remove the threat.
When requested to treat stdio MCP as unsafe by default and require explicit opt-in, Flowise reportedly said they wanted to “limit what we know is bad without completely disabling features that users may rely on.” Obsidian shared a proof of concept (POC) exploit code on how the current protections by Flowise could still be bypassed for successful RCE.
 The only complete mitigation recommended by the researchers is turning off MCP stdio by setting “CUSTOM_MCP_PROTOCOL=sse”. For those who can’t, without obstructing operations, pinning trusted packages where possible, and reviewing imported chatflows from untrusted sources might help, the researchers added.
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China-Aligned Groups Ramp Up Attacks: Dragon Weave Hits Czech Republic & Taiwan

A new cyber espionage campaign codenamed Operation Dragon Weave has been observed targeting officials and citizens in the Czech Republic and Taiwan to deliver an AdaptixC2 agent. According to Seqrite Labs, targets of the campaign include government, research, academic, technology, and financial services sectors. The activity entails distributing spear-phishing emails containing ZIP attachmentsView the full article
Hacker News

Apple Car Key Support Coming to Future Mahindra Vehicles

Apple's Car Keys feature appears to be coming to future vehicles made by Indian maker Mahindra, based on code changes discovered by MacRumors in Apple's Wallet app backend.


Car Keys allows an iPhone or Apple Watch with NFC capabilities to unlock a vehicle through the Wallet app. A digital version of a car key is stored in Wallet, and unlocking can be done simply by holding an Apple Watch or ‌iPhone‌ near a compatible vehicle's NFC reader.

Mahindra already supports Samsung Wallet's Digital Car Key feature for Galaxy devices, but it does not yet offer native Apple Car Key support, so this would need to be implemented by the automobile manufacturer first on future models.

What can be done with Car Keys may vary by car manufacturer, but at a minimum, Car Keys can be used to unlock your car, lock your car, and start your car, which are the features available with a physical key.

Apple introduced Car Keys in 2022, and car manufacturers like BMW, Rivian, Kia, and Hyundai have all implemented support for Car Keys. Apple maintains a full list of vehicles that support Car Keys on its CarPlay model availability webpage.Tag: iPhone Car Keys
This article, "Apple Car Key Support Coming to Future Mahindra Vehicles" first appeared on MacRumors.com

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First 'Confirmed' iPhone Ultra Color Allegedly Revealed in Leaked Image

Apple is expected to launch its first foldable iPhone later this year. Rumors suggest the "iPhone Ultra" will come in two color options, and a leaker shared an image today that allegedly shows one of them.


Posted on Weibo by the Chinese leaker known as Ice Universe, the image purportedly offers a first glimpse of Apple's foldable in white. The device is believed to have entered early mass production, but the model shown is likely a dummy. Regardless, fellow leaker Instant Digital has said white is so far the only "confirmed" finish that the device will be available in.

It is not yet clear what the alternative color will be, but Macworld recently cited a supply chain source claiming that it will be an indigo option similar to the iPhone 17 Pro's Deep Blue finish. The same source said the device will offer fewer choices than the iPhone 18 Pro models, with no bold or vibrant colors.

According to Bloomberg's Mark Gurman, Apple plans to "stay away from fun colors" and stick to more traditional space gray/black and silver/white finishes. Such an approach would be similar to the iPhone X, which launched in just two colors – Silver and Space Gray – when it debuted in November 2017.

A limited color selection may simply reflect the foldable iPhone's expected low production volumes. Industry analyst Ming-Chi Kuo has warned that manufacturing challenges could constrain supply through at least the end of 2026, and adding more colors would increase complexity and costs for an already difficult-to-produce device.

With launch supply expected to be tight and a price above $2,000, as reported by Gurman, Apple likely has little incentive to expand the initial color lineup, while buyers at this price point are also less likely to base their purchasing decision on color options.

The iPhone Ultra is expected to launch alongside the iPhone 18 Pro and iPhone 18 Pro Max this coming September.Tags: Foldable iPhone, Ice Universe, iPhone Ultra
This article, "First 'Confirmed' iPhone Ultra Color Allegedly Revealed in Leaked Image" first appeared on MacRumors.com

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Nvidia Challenges Apple Silicon With New RTX Spark PC Chip

Nvidia is entering the consumer PC chip business for the first time and has thrown down the gauntlet to Apple, describing its new RTX Spark processor as "the most efficient PC chip ever built."


Nvidia says its RTX Spark Superchip is purpose-built to run AI agents that can work proactively across apps and run in the background as a personal "teammate."

With the chip, Nvidia says users can "render ultra-large 90GB 3D scenes with OptiX and DLSS, edit 12K 4:2:2 video with the NVIDIA Blackwell decoder, run 120-billion-parameter large language models with 1 million tokens context, and play AAA games at 1440p resolution and over 100 frames per second with ray tracing, DLSS and Reflex."

The chip was announced by Nvidia chief executive Jensen Huang at the Computex conference in Taipei on Monday.

It's a big play for a company traditionally focused on graphics cards to move into the kind of integrated silicon that runs an entire laptop. It also puts the RTX Spark on a collision course with Apple's M5, widely regarded as the laptop chip to beat for running AI tasks on-device.

Like Apple's chips, the RTX Spark is Arm-based, pairing an Nvidia Blackwell RTX graphics processor with a Grace CPU. It's effectively the same GB10 chip that's found in the DGX Spark, the tiny "personal AI supercomputer" that Nvidia released last year.

Microsoft's new 15-inch Surface Laptop Ultra will be among the first machines to ship with the integrated silicon. The machine features a mini-LED touchscreen, the largest haptic touchpad Microsoft has fitted to a Surface, and a selection of ports covering HDMI, USB-C, USB-A, SD cards, and headphones.


Configured with up to 128GB of unified memory, the Ultra can run AI models with up to 120 billion parameters locally, a figure Microsoft attributes to Nvidia, based on a theoretical performance measure. Microsoft claims it's the most powerful Surface it has ever built. Nvidia says its chip will eventually appear in around 30 laptops and more than 10 desktops.

Microsoft says the Surface Laptop Ultra will arrive later this year. Pricing has not been announced, but Nvidia has suggested the first wave of RTX Spark machines will target the premium end of the market.Tags: Apple Silicon, Microsoft, Nvidia
This article, "Nvidia Challenges Apple Silicon With New RTX Spark PC Chip" first appeared on MacRumors.com

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Cheaper, Lighter Apple Vision Pro Successor Could Arrive in Late 2028

Apple is still working on a cheaper, lighter successor to its Vision Pro headset, but it is unlikely to launch before late 2028 or 2029, according to Bloomberg's Mark Gurman.


Writing in his latest Power On newsletter, Gurman says that Apple needs to come up with a slimmer design for the $3,499 headset and bring down the cost before it can return to the category, which is essentially "on ice" until then.

Gurman made a point of distinguishing the Vision Pro successor from the long-rumored "Vision Air," which was cancelled last year.

In the meantime, Apple's smart glasses project is now the focus, and former Vision Products Group members have been reassigned to that team. Apple is now aiming to release its first smart glasses in "late 2027," according to Gurman.

Apple refreshed the Vision Pro in October 2025 with an updated model featuring an M5 chip.Related Roundup: Apple Vision ProTag: Mark GurmanBuyer's Guide: Vision Pro (Neutral)Related Forum: Apple Vision Pro
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Critical WP Maps Pro Flaw Actively Exploited to Create Admin Accounts

Threat actors are attempting to actively exploit a critical security flaw impacting WP Maps Pro, a WordPress plugin that has had over 15,000 sales on the Envato Market, to create malicious administrator accounts on susceptible sites. WP Maps Pro allows site owners to embed customizable Google Maps and OpenStreetMap with markers, listings, and advanced location features on WordPress sites. It isView the full article
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6 critical security gaps every CISO must address

CISOs acknowledge that no organization is completely safe, but many also admit their security measures aren’t where they’d like them to be.
One-third of CISOs surveyed for Proofpoint’s 2025 Voice of the CISO Report said the data within their organization is not adequately protected, and 58% said their organizations were unprepared to respond to a cyberattack. Meanwhile, only 67% believed their organizations offered adequate budget, staff, and tools to meet their cybersecurity goals.
Such figures indicate that critical cybersecurity gaps remain in many, if not most, organizations. As adversaries lean into automation and artificial intelligence, the pressure is mounting to address security gaps that could be exploited. Here are six critical security gaps that demand CISOs’ attention, according to their IT security leader colleagues and industry observers.
1. The perception gap
Although CISOs have become more business-oriented in recent years, many still view their primary job as protecting digital systems when they should see it as ensuring business resilience, says Errol Weiss, CSO with Health-ISAC.
“CISOs still think of a bad day from the IT perspective; they still think of security as an IT problem,” he notes. “They need to shift from protecting systems at all costs to instead building resilience and thinking about the downstream impacts when something fails.”
Weiss notes that part of the reason this gap persists in many organizations is because business continuity, which is at the heart of resilience, usually falls to executives other than CISOs. “The business continuity piece has traditionally been someone else’s problem, but now it has to become a focus for the security organization,” he says.
When CISOs think broadly about how digital threats could impact the business, rather than focus on how attacks impact the IT environment, they get a more accurate view of the top risks and can better access the blast radius of an incident, Weiss explains. That in turn enables CISOs to more effectively prioritize defensive moves and remediation action, making it more likely that an incident can be contained and not have unexpected follow-on impacts that stymie business operations.
The 2024 cyberattack on Change Healthcare, the consequences of which rippled through the entire healthcare industry, shows why CISOs need to close this gap in perspective on cyber threats and risk, he says.
2. The gap between the speed of threat actors and security
The 2025 Year in Review report from threat intelligence firm Cisco Talos stated that “the 2025 threat landscape was defined by an unprecedented acceleration in the speed of vulnerability exploitation, with adversaries weaponizing new security flaws like React2Shell and ToolShell almost immediately upon disclosure.”
Most security teams aren’t moving as fast, creating an agility gap between them and the threat actors, says Buck Bell, director of security strategy at IT services provider CDW.
“Most of the gaps we see today are execution gaps,” he adds.
Many security programs still feature legacy thinking, including “some static security measures in a world that needs real-time adjustments,” he says. Monthly penetration testing and patch Tuesdays, for example, are relics of an older era yet remain in some security departments. “The reality is that organizations today need to execute at a higher velocity,” he adds.
Bell says leading CISOs are adding speed to their operations by adopting AI, automation, and practices such as continuous threat exposure management (CTEM).
3. The gap between the speed of the business and security
Similarly, some CISOs also need to increase their speed and agility so that security can move as quickly as the business does. As professional services firm PwC notes in its 2026 CISO Outlook, “The CISO role is at a pivotal moment. As technology accelerates and new threats emerge, you’re expected to lead at the pace of change. AI, quantum computing, and a hyperconnected world are reshaping risk — and your business is watching.”
Chirag Shah, global information security officer and data protection officer at software company Model N, knows that business is the pacesetter these days. “Business wants to run faster, and if they’re wanting to run faster, that means we at security and compliance have to run with them,” he says.
But he also knows security struggles to keep up. “We’re always playing a catchup game,” he adds.
Shah has taken action to add speed, such as upskilling security staffers on AI so they’re ready to work with the business on their priority projects.
Chris Cochran, field CISO and vice president of AI security at SANS Institute, says CISOs who adopt frameworks and standards and who collaborate with their security colleagues can also add speed by learning and deploying proven tactics that can quickly expand and scale as the business changes.
4. The gap between existing and needed skills
CISOs have long struggled to get the talent they need. In the past, the issue centered mainly around getting enough people to fill roles; now they’re more concerned that security pros don’t possess the updated skills they need to succeed.
According to the SANS 2026 Cybersecurity Workforce Research Report, “the cybersecurity workforce is undergoing a fundamental transformation. Organizations are rebuilding their teams from the top down as artificial intelligence disrupts traditional entry points while regulatory compliance demands create new frameworks for skills validation. This convergence is producing a widening skills gap that organizations struggle to close, even as they increasingly recognize that having the right abilities matters more than simply adding headcount.”
It further states that “the need for specialists in new roles nearly doubled year-over-year, while additional hiring for existing skills increased substantially.”
Here, CISOs’ concern has accelerated, with 60% of security leaders identifying this skills gap as their primary workforce challenge in 2026 (up from 52% last year) — and compared to 40% who said headcount shortages were their chief issue.
Beth Miller, global field CISO at software maker Mimecast, says it’s not just a skills gap within security that plagues CISOs but a gap in needed security skills throughout the organization.
“You can have a fully skilled security team, but if you don’t have security skills in the business, too, you still will have a gap,” she says.
Closing the gap requires “investing in the human layer across the organization,” she adds.
SANS Institute’s Cochran made similar observations, saying CISOs need to build a culture of continuous learning and training. “Closing the gap comes down to one word: intention,” he says.
5. Gaps in securing AI deployments
CISOs lag in securing AI deployments for several reasons.
To start, Mimecast’s Miller says, “the mandate around AI is moving faster than CISOs are prepared for. The pattern we’re seeing in our and other organizations is that leadership announces an AI adoption initiative, it’s top down, and it’s often tied to competitive pressure or board expectations. And then within weeks business units are building AI tools, connected to data, and integrating AI into existing systems, and CISOs are finding out about these [initiatives] during or after implementation.”
There are also the AI deployments happening from the bottom up, often without any leadership involvement or knowledge at all. “Shadow AI is happening industry wide,” Model N’s Shah says. And while security or IT may find those deployments after the fact, that discovery doesn’t erase the security gap on its own.
Experts also cite the challenges of, first, developing the right security controls for AI as the technology evolves and, second, getting everyone to buy into and then follow those controls and governance frameworks as they morph with the technology’s evolution. Those dynamics inevitably create gaps between what’s needed to secure AI and what controls are being implemented.
“It’s a governance gap masquerading as an IT problem,” Miller adds.
The SANS report found that only 54% of surveyed organizations had AI security policies in place and only 20% had comprehensive governance frameworks ready, with about 75% either implementing or still building governance structures.
SANS concluded that “AI security governance is still in early days.” Other experts acknowledged as much, saying that CISOs need to lean on observability tools, executive influence skills, AI-related security awareness and training, emerging AI security best practices, and new AI governance frameworks to close what seems to be a yawning gap in many organizations.
6. The legacy gap
Jason Lish, Cisco’s global CISO, says many business leaders have adopted a “set-it-and-forget-it mentality” with technology, resisting moves to modernize IT as long as systems perform and aren’t differentiating.
That challenges not only CIOs as they try to integrate AI and other new technologies into legacy tech, but also CISOs as they seek to implement modern security practices and technologies, Lish explains. And it’s becoming a more acute security problem as threat actors become more skillful at using AI to exploit out-of-support systems and legacy tech that can’t implement modern security controls.
A 2026 study from National Association of State CIOs and Deloitte & Touche found that CISOs listed legacy infrastructure as one of the top three barriers to meeting cybersecurity challenges, along with the increasing sophistication of threats and insufficient funding for cybersecurity.
“CISOs should be thinking about a risk-based approach here,” Lish says, “going to the board or the C-suite and saying, ‘These are the most critical pieces of legacy equipment or devices we need to replace’ and help them understand the risk of not doing so. The CISO has to be the one to provide that prioritization.”
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Press Release: CSO30 ASEAN & Hong Kong Awards 2026 open for nominations

>The CSO30 ASEAN & Hong Kong Awards return in 2026, as an important moment to recognise the cybersecurity leaders and teams who are making resilience measurable across the region. In a landscape shaped by rapid threat evolution, board-level scrutiny and rising expectations of business continuity, these awards spotlight the people and programmes that are turning security into an enterprise capability, not just a control function. On our sixth consecutive edition this year, this awards programme is a regional benchmark for cybersecurity maturity across ASEAN and Hong Kong, and a unique platform for organisations to showcase their most impactful achievements, gain regional and global visibility, and join a distinguished community of Chief Information Security Officers(CISOs) and Chief Security Officers(CSOs) who are redefining the role of cybersecurity.
>> Globally respected, the CSO30 ASEAN and Hong Kong Awards celebrate not just individual leaders but the collective efforts of teams that drive transformation, cyber resiliency and business continuity. This year, you and your team could stand alongside the past winners which include this region’s most influential organizations, to be a recognise force in the ASEAN and HK cybersecurity landscape.
Calling on CISOs and CSOs to nominate themselves, their peers and their teams now. If your organisation has strengthened its cyber posture, shifted strategic decision-making, or built stronger ecosystem partnerships in the past year, this is the moment to put that work forward.
This year’s awards spans three nomination pathways: 
CSO Leadership – Individual Online Form
CSO Transformation – Individual Online Form
Ecosystem – Team Online Form
Together, these categories reflect the full scope of modern security leadership, from board-level influence and enterprise transformation to ecosystem collaboration and measurable resilience.
Individual Leadership nominations are expected to show how a cybersecurity leader has delivered real value, changed the way the organisation is protected, influenced executive decision-making, and prepared the business to respond to emerging cyber risks while ensuring long-term resilience and continuity. 
The Transformation category goes further, asking for a cybersecurity-led project from the past one year that changed how the organisation is protected, overcame key challenges, delivered quantifiable impact, and contributed to the wider cybersecurity community. 
Ecosystem Team nominations must show how a project shaped and strengthened the cybersecurity agenda across the organisation, its partners and even the broader country context, with clear challenges, outcomes and quantifiable value.
If you lead a cybersecurity team that has delivered measurable impact, or if you know a peer whose leadership deserves broader recognition, nominate them. If you are a CISO or CSO whose work has materially improved your organisation’s resilience, nominate yourself. The region needs to see the leaders and teams setting the standard for security maturity, operational continuity and business trust.
The deadline for nominations: 31 July 2026.
Awards Gala website: https://event.foundryco.com/cio-100-asean-and-hk/
Due to the sensitive nature of cybersecurity work, project details will not be published, which gives nominees the confidence to submit meaningful work without exposing sensitive information.
The CSO30 ASEAN & Hong Kong Awards matter because we recognise a kind of leadership the region increasingly depends on – decisive, collaborative, strategic and resilient. We give visibility to the people and teams making cybersecurity a stronger part of business performance and long-term continuity.
Media Contact: Estelle Quek Editorial Director, CIO ASEAN & CSO ASEAN 




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CSOonline

Press Release: CSO30 ASEAN & Hong Kong Awards 2026 open for nominations

The CSO30 ASEAN & Hong Kong Awards return in 2026, as an important moment to recognise the cybersecurity leaders and teams who are making resilience measurable across the region. In a landscape shaped by rapid threat evolution, board-level scrutiny and rising expectations of business continuity, these awards spotlight the people and programmes that are turning security into an enterprise capability, not just a control function. On our fifth consecutive run this year, this awards programme is a regional benchmark for cybersecurity maturity across ASEAN and Hong Kong, and a unique platform for organisations to showcase their most impactful achievements, gain regional and global visibility, and join a distinguished community of Chief Information Security Officers(CISOs) and Chief Security Officers(CSOs) who are redefining the role of cybersecurity.
Globally respected, the CSO30 ASEAN and Hong Kong Awards celebrate not just individual leaders but the collective efforts of teams that drive transformation, cyber resiliency and business continuity. This year, you and your team could stand alongside the past winners which include this region’s most influential organizations, to be a recognise force in the ASEAN and HK cybersecurity landscape.
Calling on CISOs and CSOs to nominate themselves, their peers and their teams now. If your organisation has strengthened its cyber posture, shifted strategic decision-making, or built stronger ecosystem partnerships in the past year, this is the moment to put that work forward.
This year’s awards spans three nomination pathways: 
CSO Leadership – Individual Online Form
CSO Transformation – Individual Online Form
Ecosystem – Team Online Form
Together, these categories reflect the full scope of modern security leadership, from board-level influence and enterprise transformation to ecosystem collaboration and measurable resilience.
Individual Leadership nominations are expected to show how a cybersecurity leader has delivered real value, changed the way the organisation is protected, influenced executive decision-making, and prepared the business to respond to emerging cyber risks while ensuring long-term resilience and continuity. 
The Transformation category goes further, asking for a cybersecurity-led project from the past one year that changed how the organisation is protected, overcame key challenges, delivered quantifiable impact, and contributed to the wider cybersecurity community. 
Ecosystem Team nominations must show how a project shaped and strengthened the cybersecurity agenda across the organisation, its partners and even the broader country context, with clear challenges, outcomes and quantifiable value.
If you lead a cybersecurity team that has delivered measurable impact, or if you know a peer whose leadership deserves broader recognition, nominate them. If you are a CISO or CSO whose work has materially improved your organisation’s resilience, nominate yourself. The region needs to see the leaders and teams setting the standard for security maturity, operational continuity and business trust.
The deadline for nominations: 31 July 2026.
Awards Gala website: https://event.foundryco.com/cio-100-asean-and-hk/
Due to the sensitive nature of cybersecurity work, project details will not be published, which gives nominees the confidence to submit meaningful work without exposing sensitive information.
The CSO30 ASEAN & Hong Kong Awards matter because we recognise a kind of leadership the region increasingly depends on – decisive, collaborative, strategic and resilient. We give visibility to the people and teams making cybersecurity a stronger part of business performance and long-term continuity.
Media Contact: Estelle Quek Editorial Director, CIO ASEAN & CSO ASEAN 




View the full article
CSOonline

CSO30 ASEAN & Hong Kong Awards 2026 open for nominations

The CSO30 ASEAN & Hong Kong Awards return in 2026, as an important moment to recognise the cybersecurity leaders and teams who are making resilience measurable across the region. In a landscape shaped by rapid threat evolution, board-level scrutiny and rising expectations of business continuity, these awards spotlight the people and programmes that are turning security into an enterprise capability, not just a control function. On our fifth consecutive run this year, this awards programme is a regional benchmark for cybersecurity maturity across ASEAN and Hong Kong, and a unique platform for organisations to showcase their most impactful achievements, gain regional and global visibility, and join a distinguished community of Chief Information Security Officers(CISOs) and Chief Security Officers(CSOs) who are redefining the role of cybersecurity.
Globally respected, the CSO30 ASEAN and Hong Kong Awards celebrate not just individual leaders but the collective efforts of teams that drive transformation, cyber resiliency and business continuity. This year, you and your team could stand alongside the past winners which include this region’s most influential organizations, to be a recognise force in the ASEAN and HK cybersecurity landscape.
Calling on CISOs and CSOs to nominate themselves, their peers and their teams now. If your organisation has strengthened its cyber posture, shifted strategic decision-making, or built stronger ecosystem partnerships in the past year, this is the moment to put that work forward.
This year’s awards spans three nomination pathways: 
CSO Leadership – Individual Online Form
CSO Transformation – Individual Online Form
Ecosystem – Team Online Form
Together, these categories reflect the full scope of modern security leadership, from board-level influence and enterprise transformation to ecosystem collaboration and measurable resilience.
Individual Leadership nominations are expected to show how a cybersecurity leader has delivered real value, changed the way the organisation is protected, influenced executive decision-making, and prepared the business to respond to emerging cyber risks while ensuring long-term resilience and continuity. 
The Transformation category goes further, asking for a cybersecurity-led project from the past one year that changed how the organisation is protected, overcame key challenges, delivered quantifiable impact, and contributed to the wider cybersecurity community. 
Ecosystem Team nominations must show how a project shaped and strengthened the cybersecurity agenda across the organisation, its partners and even the broader country context, with clear challenges, outcomes and quantifiable value.
If you lead a cybersecurity team that has delivered measurable impact, or if you know a peer whose leadership deserves broader recognition, nominate them. If you are a CISO or CSO whose work has materially improved your organisation’s resilience, nominate yourself. The region needs to see the leaders and teams setting the standard for security maturity, operational continuity and business trust.
The deadline for nominations: 31 July 2026.
Awards Gala website: https://event.foundryco.com/cio-100-asean-and-hk/
Due to the sensitive nature of cybersecurity work, project details will not be published, which gives nominees the confidence to submit meaningful work without exposing sensitive information.
The CSO30 ASEAN & Hong Kong Awards matter because we recognise a kind of leadership the region increasingly depends on – decisive, collaborative, strategic and resilient. We give visibility to the people and teams making cybersecurity a stronger part of business performance and long-term continuity.
Media Contact: Estelle Quek Editorial Director, CIO ASEAN & CSO ASEAN 
CSO ASEAN



View the full article
CSOonline

iOS 28 Will Reportedly Be 'Far More Significant' Than iOS 27

While we are still a week away from Apple unveiling iOS 27, Bloomberg's Mark Gurman has just shared the first rumor regarding iOS 28.


In his Power On newsletter today, he said that iOS 28 will be "far more significant" than iOS 27, but he did not provide any specific details.

"Next year's '28' releases are already shaping up to be far more significant than the '27' updates," wrote Gurman, without elaborating.

iOS 28 is codenamed "Bell," while macOS 28 is "Poppy," he said.

iOS 28 would be the first version available on Apple's rumored 20th-anniversary iPhone, which is expected to be released in September next year.

iOS 27 will seemingly be focused on Siri and Apple Intelligence.

The update will include the long-awaited personalized version of Siri, complete with on-screen awareness and better understanding of your personal context. For example, at WWDC 2024, Apple showed a user asking Siri about their mother's flight and lunch reservation based on info retrieved from the Mail and Messages apps.

A dedicated Siri app will allow you to have back-and-forth conversations with Siri in text or voice modes, similar to other chatbot apps like ChatGPT. In addition, iOS 27 is expected to add a "Search or Ask" feature to the Dynamic Island.

It remains to be seen exactly how iOS 28 will be more significant. The update will be unveiled at WWDC 2027 next June, so there is a long time to go.Related Roundup: iOS 27Tags: Bloomberg, iOS 28, Mark Gurman
This article, "iOS 28 Will Reportedly Be 'Far More Significant' Than iOS 27" first appeared on MacRumors.com

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Apple Glasses Reportedly Launching in 'Late 2027' With These Features

Apple is now aiming to release its first smart glasses in "late 2027," according to the latest word from Bloomberg's Mark Gurman.

Meta Ray-Bans
He previously said that Apple planned to begin shipping the glasses by early 2027, but he said the product has faced development delays.

The glasses will feature "oval-shaped cameras, unique colors, and multiple frame styles," according to Gurman. "Over time, Apple believes the glasses could evolve into a health device and eventually incorporate augmented reality technologies capable of improving how people see," he said, but this technology is likely years away.

According to Gurman's sources, Apple's CEO Tim Cook views the glasses as his "top priority" before he hands the reigns to John Ternus on September 1.

The glasses will compete with products in the $200 to $500 range in the U.S., he said.

Like the Meta Ray-Bans, Apple's glasses will have built-in cameras that let users capture photos and videos. There would also be speakers and microphones for music, phone calls, and notifications announced by Siri, he said.

The glasses could offer turn-by-turn walking directions.

As for build quality, he said Apple is designing its own plastic frames, with the company allegedly testing at least four potential designs:A larger rectangular frame, similar to Ray-Ban's Wayfarers
A slimmer rectangular design, similar to the glasses worn by Apple CEO Tim Cook
Larger oval or circular frames
Smaller oval or circular framesApple is exploring a range of color options, including black, ocean blue, and light brown, and the glasses may have vertically-oriented oval camera lenses, he said.

Meta uses frames from the popular glasses brand Ray-Ban.


Unlike the latest generation of Meta Ray-Bans, Gurman does not expect Apple's first smart glasses to have an in-lens augmented reality display. He does not expect Apple's glasses to gain such a feature for at least a few years.Tags: Apple Glasses, Bloomberg, Mark Gurman
This article, "Apple Glasses Reportedly Launching in 'Late 2027' With These Features" first appeared on MacRumors.com

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reporter

New Apple TV and HomePod Mini Are 'Nearly Ready' to Launch, New Siri Remote Also Rumored

New models of the Apple TV 4K and HomePod mini are "nearly ready to go," according to the latest word from Bloomberg's Mark Gurman.


Both devices have been ready "for months," but Apple is holding off on launching them until the more personalized version of Siri is available, he said.

"I am told the hardware for the next Apple TV set-top box and HomePod mini has been done for months and that both devices are already in active use among employees at the company's headquarters in Cupertino, California," wrote Gurman.

If you have been closely following Apple TV and HomePod mini rumors, this is a familiar narrative.

The revamped Siri is finally expected to launch as part of iOS 27, iPadOS 27, and macOS 27, which will be unveiled during the WWDC 2026 keynote on Monday, June 8. Following beta testing, the software updates should be widely released in September, so the new Apple TV 4K and HomePod mini models should be available to purchase by then. In other words, the devices are hopefully around 3-4 months away at the latest.

The current Apple TV 4K was unveiled in October 2022, while the HomePod mini was introduced in October 2020, so there has been a long wait for the devices. Nevertheless, Gurman said "don't expect much" in terms of new features for both devices, aside from newer chips that support the more personalized version of Siri.

The current Apple TV 4K has an A15 Bionic chip from the iPhone 13 series, while the HomePod mini uses the S5 chip from the Apple Watch Series 5.

Earlier rumors claimed the next Apple TV would be equipped with the A17 Pro chip, which is the oldest chip that supports Apple Intelligence. The device is also expected to feature Apple's N1 chip for Wi-Fi 7, Bluetooth 6, and Thread.

Gurman expects the next Apple TV to have a similar design as the current model.

There is one new twist, as he was told that the Apple TV's Siri Remote may be "refreshed in some form," but he did not provide any specific details or guarantee that there will be any outward-facing design changes to the accessory.


As for the HomePod mini, it is expected to use an Apple Watch's S9 chip or newer, but it is unclear if or how that chip would fully support the new Siri powered by Apple Intelligence. Other previously-rumored features for the speaker include the N1 chip, improved sound quality, a newer Ultra Wideband chip, and a red color option.

Apple is also expected to update the full-sized HomePod and release an all-new smart home hub this year, with those devices also held up by Siri.Related Roundups: Apple TV, HomePod miniTags: Bloomberg, Mark Gurman, Siri, Siri RemoteBuyer's Guide: Apple TV (Don't Buy), HomePod Mini (Don't Buy)Related Forums: Apple TV and Home Theater, HomePod, HomeKit, CarPlay, Home & Auto Technology
This article, "New Apple TV and HomePod Mini Are 'Nearly Ready' to Launch, New Siri Remote Also Rumored" first appeared on MacRumors.com

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Dutch Authorities Dismantle Botnet Linked to 17 Million Infected Devices

Dutch authorities have announced the takedown of a botnet that enslaved millions of infected devices, including computers, tablets, smartphones, and IoT devices, to carry out malicious attacks. The bot network, per the Dutch Politie and the National Cyber Security Center (NCSC), consisted of at least 17 million infected devices. More than 200 servers located in the Netherlands acted as theView the full article
Hacker News

Observability Course for Beginners: Complete Learning Path for Metrics, Logs, Traces, Grafana, Prometheus, and OpenTelemetry

If you are starting your career in DevOps, SRE, cloud engineering, platform engineering, or application support, one skill will keep showing up again and again: observability.
Not just monitoring.
Not just dashboards.
Not just logs.
Real observability.
Modern systems are no longer simple. Applications run across Kubernetes clusters, cloud platforms, microservices, APIs, databases, message queues, containers, serverless components, and third-party services. A single user request may travel through multiple services before returning a response. When something breaks, restarting a server is no longer enough.
Teams need to know:
What happened? Where did it happen? Why did it happen? Who was affected? Which service caused it? Did the latest deployment introduce it? Are we violating our SLO? Should we roll back, scale, or investigate deeper? That is where observability becomes essential.
An observability course for beginners should help you move from “I can see a dashboard” to “I can understand and debug a production system.”
This guide gives you a complete beginner-friendly learning path for observability. We will cover metrics, logs, traces, Grafana, Prometheus, OpenTelemetry, Kubernetes observability, SLOs, hands-on labs, career paths, certification training, and how DevOpsSchool’s Master in Observability Engineering Certification fits perfectly into this learning journey.
What Is Observability?
Observability is the ability to understand the internal state of a system by analyzing the external signals it produces.
In simple words:
Observability helps you understand what your system is doing and why it is behaving that way.
A production system usually gives you three major signals:
Metrics Logs Traces These are often called the three pillars of observability.
But in real engineering teams, observability also includes:
Dashboards Alerts SLOs SLIs Error budgets Incident response Root cause analysis Application performance monitoring Kubernetes monitoring Distributed tracing Telemetry pipelines Runbooks Postmortems Reliability engineering A beginner should understand this from day one: observability is not a tool. It is a practice.
Tools such as Prometheus, Grafana, OpenTelemetry, Loki, Tempo, Jaeger, ELK, Datadog, Dynatrace, and New Relic help you implement observability. But the real skill is knowing how to use signals to troubleshoot systems and improve reliability.
Observability vs Monitoring: The First Concept Beginners Must Learn
Monitoring tells you when something is wrong.
Observability helps you understand why something is wrong.
Monitoring usually works well for known problems:
CPU usage is high Disk space is low Server is down Memory usage crossed a threshold Application returned 500 errors Observability helps with unknown or complex problems:
A payment request is slow only for one region A deployment increased latency for one API endpoint A Kubernetes pod is healthy but users still see failures A database query is slow only under specific traffic patterns A downstream service is creating cascading timeouts Logs show errors but the real issue started in another service Monitoring is still important. You need it.
But monitoring alone is not enough for cloud-native systems.
Observability gives engineers the context needed to debug modern applications.
Why Observability Is Important for Beginners
If you are new to DevOps or SRE, observability may feel like an advanced topic. But it is actually one of the best places to build real production understanding.
Why?
Because observability teaches you how systems behave after deployment.
Many beginners learn Linux, Git, Docker, Kubernetes, Jenkins, Terraform, or cloud platforms. These are excellent skills. But eventually, every engineer faces the same question:
“My application is deployed. Now how do I know if it is working properly?”
Observability answers that question.
It helps beginners understand:
How applications behave in production How infrastructure affects performance How errors appear How latency spreads How Kubernetes workloads fail How alerts are designed How teams investigate incidents How reliability is measured How DevOps and SRE teams make decisions This is why observability is such a valuable career skill.
It connects development, infrastructure, operations, cloud, Kubernetes, monitoring, and reliability into one practical discipline.
Who Should Learn Observability?
Observability is useful for many technical roles.
DevOps Engineers
DevOps engineers need observability to understand what happens after deployment. CI/CD may say a release succeeded, but observability tells you whether production is healthy.
SRE Engineers
SRE engineers use observability to measure reliability, define SLOs, monitor error budgets, respond to incidents, and reduce downtime.
Cloud Engineers
Cloud engineers use observability to monitor cloud infrastructure, managed services, Kubernetes clusters, networking, storage, and application workloads.
Platform Engineers
Platform engineers use observability to build shared monitoring and reliability platforms for development teams.
Developers
Developers use observability to understand how their code performs in production, where errors occur, and which dependencies are slow.
Application Support Engineers
Support engineers use logs, dashboards, traces, and alerts to investigate user issues quickly.
If you work with production systems, observability is not optional anymore.
The Three Pillars of Observability
Let’s understand the foundation.
1. Metrics
Metrics are numerical measurements collected over time.
Examples:
CPU usage Memory usage Request count Error count Request latency Disk usage Network traffic Queue depth Pod restart count Database query duration Metrics are excellent for dashboards, trends, alerts, and SLOs.
Metrics answer questions like:
Is traffic increasing? Is latency getting worse? Are errors rising? Is the service healthy? Which pod is consuming memory? Are we meeting our SLO? Prometheus is one of the most popular tools for collecting and querying metrics.
Grafana is commonly used to visualize those metrics.
2. Logs
Logs are event records generated by applications, servers, containers, databases, and infrastructure systems.
Examples:
Error messages Stack traces Authentication failures API request logs Database errors Deployment events Application warnings Logs are useful when you need details.
Logs answer questions like:
What error happened? What did the application say before it failed? Which user request caused the issue? Which exception occurred? Which dependency returned an error? Popular logging tools include ELK, EFK, Grafana Loki, Fluent Bit, and Fluentd.
3. Traces
Traces show the journey of a request across multiple services.
In microservices, one user request may pass through:
Frontend API gateway Auth service User service Payment service Inventory service Database Cache Message queue Third-party API A trace shows how much time each part took and where the request failed or slowed down.
Traces answer questions like:
Which service caused latency? Which downstream dependency failed? Where did the request spend most of its time? Did the problem start upstream or downstream? Which database query slowed the request? Popular tracing tools include Jaeger, Zipkin, Grafana Tempo, and OpenTelemetry.
Complete Observability Learning Path for Beginners
A beginner should not start by installing every observability tool at once.
That creates confusion.
Instead, follow a layered learning path.
Step 1: Learn Observability Foundations
Start with concepts.
Learn:
Monitoring vs observability Metrics, logs, and traces Telemetry Instrumentation Time-series data Distributed systems Application performance monitoring SLIs, SLOs, and error budgets Incident response Root cause analysis This foundation matters because tools make sense only when you understand the problems they solve.
A beginner mistake is learning Grafana panels without understanding what should be measured. Avoid that.
First learn why observability matters.
Then learn the tools.
Step 2: Learn Metrics
Metrics are the best starting point because they are easier to visualize and alert on.
Learn:
Counter Gauge Histogram Summary Labels Cardinality Aggregation Rate calculation Percentiles Time-series storage Start with basic infrastructure metrics:
CPU Memory Disk Network Then move to application metrics:
Request rate Error rate Duration Active users Queue length Database query time A strong beginner should understand two practical models:
RED Method
Useful for services:
Rate Errors Duration USE Method
Useful for infrastructure:
Utilization Saturation Errors These models help you build useful dashboards instead of random charts.
Step 3: Learn Prometheus
Prometheus is one of the most important tools in modern monitoring and observability.
It collects metrics, stores time-series data, supports powerful querying, and integrates beautifully with Grafana.
Beginners should learn:
Prometheus architecture Scrape model Targets Jobs and instances Exporters Prometheus configuration Prometheus data model Labels PromQL Recording rules Alerting rules Alertmanager Prometheus Operator Kubernetes monitoring PromQL is especially important.
PromQL helps you ask questions like:
What is the request rate? What is the error rate? What is p95 latency? Which pod is using the most memory? Which endpoint is slow? Which service is breaching its SLO? If you want to work in DevOps or SRE, Prometheus is one of the first observability tools you should learn seriously.
Step 4: Learn Grafana
Grafana turns observability data into dashboards, panels, alerts, and operational views.
But Grafana training should not only teach where to click.
A good Grafana learner should know how to design dashboards that help engineers make decisions.
Learn:
Data sources Panels Variables Transformations Dashboards Dashboard folders Dashboard permissions Dashboard provisioning Grafana Alerting Notification policies Annotations Dashboard links Prometheus integration Loki integration Tempo integration A good dashboard answers a real question:
Is the service healthy? Are users affected? Did latency increase? Did the latest deployment cause errors? Which dependency is failing? Are we meeting our SLO? Which logs and traces explain this metric spike? Do not build dashboards for decoration.
Build dashboards for action.
Step 5: Learn Logs
After metrics and dashboards, learn logs.
Logs provide the details that metrics cannot.
Learn:
Structured logging JSON logs Log levels Log aggregation Log parsing Log filtering Correlation IDs Trace IDs Log retention Log cost control Loki or ELK Fluent Bit or Fluentd Good logs are searchable, structured, and connected to traces.
Bad logs are noisy, unstructured, expensive, and difficult to use during incidents.
A beginner should learn how logs support troubleshooting:
A metric shows error rate increased Grafana shows which service is affected Logs show the exact error message Traces show the request path The team identifies the root cause This is how signals work together.
Step 6: Learn Distributed Tracing
Distributed tracing is essential for microservices.
Learn:
Spans Traces Trace IDs Span IDs Parent-child relationships Context propagation Sampling Trace attributes Jaeger Zipkin Grafana Tempo TraceQL basics Flame graphs Service dependency maps Tracing is extremely useful for latency debugging.
For example, if checkout is slow, a trace can show whether the delay happened in payment, inventory, database, cache, or an external API.
This is one of the clearest examples of observability value.
Step 7: Learn OpenTelemetry
OpenTelemetry is becoming the standard way to collect telemetry data.
It helps teams generate, collect, process, and export:
Metrics Logs Traces OpenTelemetry is vendor-neutral, which means your application does not need to be tied to only one observability vendor.
Learn:
OpenTelemetry architecture APIs SDKs Auto-instrumentation Manual instrumentation OpenTelemetry Collector Receivers Processors Exporters OTLP Semantic conventions Context propagation Metrics pipeline Logs pipeline Traces pipeline Kubernetes deployment OpenTelemetry is especially useful when you want to send telemetry to multiple tools such as Prometheus, Grafana, Jaeger, Tempo, Loki, ELK, Datadog, Dynatrace, or New Relic.
For beginners, the best way to learn OpenTelemetry is through hands-on labs.
Instrument one small application.
Send traces to Jaeger.
Send metrics to Prometheus.
Visualize them in Grafana.
Then add logs and correlation.
That is how the concept becomes real.
Step 8: Learn Kubernetes Observability
Most modern DevOps and SRE teams work with Kubernetes.
Kubernetes observability is a must-have skill.
Learn how to monitor:
Nodes Pods Containers Deployments Services Namespaces Ingress Persistent volumes Resource requests Resource limits HPA Cluster events Control plane components Application workloads Important tools include:
Prometheus Operator kube-state-metrics Node exporter Grafana dashboards Loki or ELK OpenTelemetry Collector Jaeger or Tempo Alertmanager Kubernetes observability helps answer:
Why is my pod restarting? Why is the service unavailable? Which namespace uses the most CPU? Are pods under-provisioned? Are resource limits too low? Is autoscaling working? Did a deployment cause the issue? For DevOps and SRE engineers, this is where observability becomes daily work.
Step 9: Learn SLOs, SLIs, and Error Budgets
Observability should not stop at charts.
Mature teams use observability to measure reliability.
This is where SRE concepts matter.
SLI
A service-level indicator is a measurement of service behavior.
Examples:
Availability Request success rate p95 latency p99 latency Data freshness Error rate SLO
A service-level objective is a reliability target.
Examples:
99.9% availability 95% of requests complete under 300 ms Error rate stays below 1% Error Budget
An error budget defines how much unreliability is acceptable.
If your SLO allows 0.1% failure, that 0.1% is your error budget.
SLOs help teams make better decisions.
Instead of asking, “Is CPU high?” you ask, “Are users affected?”
Instead of asking, “Should we deploy?” you ask, “Do we still have enough error budget?”
This is how observability becomes reliability engineering.
Suggested 30-Day Observability Learning Plan
Here is a practical beginner roadmap.
Days 1–5: Observability Foundations
Learn:
Monitoring vs observability Metrics, logs, traces Telemetry Instrumentation Incident response SLO basics Goal: Understand the language of observability.
Days 6–10: Prometheus Basics
Learn:
Prometheus architecture Scraping Exporters Targets Labels PromQL basics Alerting rules Goal: Collect and query metrics.
Days 11–15: Grafana Dashboards and Alerts
Learn:
Grafana data sources Panels Variables Dashboards Alert rules Notification policies Dashboard design Goal: Build useful dashboards and alerts.
Days 16–20: Logs
Learn:
Structured logging Loki or ELK Log parsing Log filtering Correlation IDs Trace IDs Goal: Investigate problems using logs.
Days 21–25: Traces and OpenTelemetry
Learn:
Spans Traces Context propagation OpenTelemetry SDK OpenTelemetry Collector Jaeger or Tempo Goal: Trace requests across services.
Days 26–30: Kubernetes, SLOs, and Capstone
Learn:
Kubernetes monitoring Pod and node metrics SLO dashboards Burn-rate alerts Failure simulation Postmortem writing Goal: Build a complete observability project.
What Should You Learn First: Prometheus, Grafana, OpenTelemetry, or ELK?
This is a common beginner question.
Here is the recommended order:
Observability concepts Metrics Prometheus Grafana Logs Distributed tracing OpenTelemetry Kubernetes observability SLOs and incident response Capstone project Why this order?
Because each skill builds on the previous one.
Prometheus makes more sense when you understand metrics.
Grafana makes more sense when Prometheus has useful data.
Logs make more sense when you can connect them with metrics.
Tracing makes more sense when you understand distributed systems.
OpenTelemetry makes more sense when you already understand metrics, logs, and traces.
Kubernetes observability makes more sense when you understand workloads, services, pods, and telemetry.
This order prevents confusion.
Recommended Observability Training Links with the Right Keywords
Use the following keyword-rich links naturally inside your blog, landing page, or learning content. Each link points to DevOpsSchool’s Master in Observability Engineering Certification because it covers the complete observability learning path: metrics, logs, traces, Prometheus, Grafana, OpenTelemetry, Kubernetes observability, SLOs, assignments, capstones, and certification training.
For Beginners
Start here if you are new to observability and want a complete structured roadmap:
Observability course for beginners
This is the right link for learners who want to understand metrics, logs, traces, Prometheus, Grafana, OpenTelemetry, and Kubernetes observability from the ground up.
For Online Learners
Use this when targeting learners searching for remote or flexible training:
Observability training online
This is useful for professionals who want live, guided, hands-on observability training without depending only on scattered tutorials.
For Certification-Focused Learners
Use this when the user wants a validated learning path:
Observability certification
This is a good fit for learners who want assignments, capstone projects, and certification-based validation.
For DevOps Engineers
Use this when writing for DevOps professionals:
Observability training for DevOps engineers
This is relevant because DevOps engineers need observability to connect deployments, infrastructure, Kubernetes, dashboards, alerts, and production feedback.
For SRE Engineers
Use this when targeting reliability-focused learners:
SRE observability training
This works well for SREs who need SLOs, SLIs, error budgets, incident response, burn-rate alerts, and reliability dashboards.
For Hands-On Learners
Use this when the audience wants labs and projects:
Hands-on observability course
This is a strong anchor because hands-on practice is the fastest way to learn production-style observability.
For Grafana Learners
Use this when discussing dashboards and alerts:
Grafana observability training
This is useful for learners who want to build dashboards, alerts, metrics panels, log views, and trace correlation workflows.
For Prometheus Learners
Use this when discussing metrics and monitoring:
Prometheus training
This is appropriate for learners who want Prometheus metrics, PromQL, exporters, alerting, and Grafana integration.
For OpenTelemetry Learners
Use this when discussing instrumentation and telemetry pipelines:
OpenTelemetry training
This is the right keyword for learners who want to understand OpenTelemetry SDKs, Collector pipelines, traces, metrics, logs, and vendor-neutral observability.
For Kubernetes Learners
Use this when the article focuses on cloud-native systems:
Kubernetes observability course
This is useful for engineers who need to monitor pods, nodes, containers, deployments, services, and Kubernetes workloads.
For Full Career-Focused Training
Use this as the main recommended program link:
Master in Observability Engineering Certification
This is the best anchor text when recommending DevOpsSchool’s complete program for observability engineering, DevOps, SRE, cloud, platform, and application monitoring professionals.
Why DevOpsSchool’s Master in Observability Engineering Certification Is a Strong Fit
A beginner can learn observability in two ways.
The first way is random learning.
You watch one video on Prometheus, one tutorial on Grafana, one blog on OpenTelemetry, one GitHub example for Loki, one Kubernetes dashboard guide, and one article on SLOs. You collect pieces, but you may not understand how everything fits together.
The second way is structured learning.
You start with foundations, then metrics, then Prometheus, then Grafana, then logs, then traces, then OpenTelemetry, then Kubernetes observability, then SLOs, then real projects.
This is where DevOpsSchool’s Master in Observability Engineering Certification fits well.
The program is designed as a complete observability engineering path, not a single-tool tutorial.
It covers:
Observability foundations Metrics, logs, and traces Prometheus PromQL Alertmanager Grafana dashboards Grafana Alerting Loki logs Tempo traces OpenTelemetry OpenTelemetry Collector ELK and EFK Jaeger and Zipkin Datadog Dynatrace New Relic Kubernetes observability SLOs, SLIs, and error budgets Assignments Capstone projects Scenario-based certification exam That breadth matters.
Real companies do not use only one tool.
One team may use Prometheus and Grafana.
Another may use ELK.
Another may use Datadog.
Another may use Dynatrace.
Another may be migrating to OpenTelemetry.
Most cloud-native teams need Kubernetes observability.
A good observability engineer must understand the patterns behind the tools.
The DevOpsSchool certification is a strong fit because it teaches observability as an engineering discipline, not as disconnected software tutorials.
How This Training Helps Beginners
Beginners need structure.
Observability has many tools and terms. Without guidance, it is easy to feel lost.
A structured program helps beginners understand:
What to learn first Why each tool matters How metrics, logs, and traces connect How Prometheus and Grafana work together How OpenTelemetry fits into the stack How Kubernetes changes observability How SLOs connect observability to reliability How to build real projects For beginners, the biggest benefit is confidence.
You do not just learn definitions.
You build working systems.
How This Training Helps DevOps Engineers
DevOps engineers need observability to validate production after deployment.
They need to know:
Did the deployment succeed technically? Did it affect users? Did error rate increase? Did latency increase? Are pods restarting? Are resources under pressure? Are alerts meaningful? Can we roll back with evidence? The DevOpsSchool course fits DevOps engineers because it includes Prometheus, Grafana, Kubernetes observability, OpenTelemetry, logs, traces, alerts, and capstones.
This helps DevOps engineers move from deployment automation to production confidence.
How This Training Helps SRE Engineers
SRE engineers need observability for reliability.
They use observability to manage:
SLIs SLOs Error budgets Burn-rate alerts Incident response Root cause analysis Postmortems Reliability dashboards The DevOpsSchool program fits SRE engineers because it connects observability tools with SRE practices.
SREs do not need dashboards for decoration.
They need dashboards that support reliability decisions.
They need alerts that indicate user impact.
They need traces that identify bottlenecks.
They need logs that confirm root cause.
They need SLOs that guide engineering priorities.
A complete observability course should teach all of that.
How This Training Helps Developers
Developers also benefit from observability.
Modern developers are increasingly responsible for production behavior.
They need to know:
How their code performs Which API endpoints are slow Which database queries are expensive Which exceptions occur in production Which dependencies fail How to add custom metrics How to add trace spans How to write useful structured logs OpenTelemetry is especially valuable for developers because it helps them instrument applications properly.
A developer who understands observability writes applications that are easier to debug, support, and improve.
Practical Capstone Project for Beginners
If you want to prove your observability skills, build this project.
Project: Full Observability Stack for a Microservices Application
Deploy a sample microservices application on Kubernetes.
Then implement:
Prometheus for metrics Grafana for dashboards Loki or ELK for logs Jaeger or Tempo for traces OpenTelemetry for instrumentation Alertmanager or Grafana Alerting for alerts SLO dashboard for reliability Failure simulation Incident report Your dashboard should show:
Request rate Error rate p95 latency p99 latency CPU usage Memory usage Pod restarts Active alerts Error budget burn Related logs Trace links Then simulate failures:
Break one service Add artificial latency Trigger 500 errors Restart pods Increase memory usage Slow down a database query Break an external API dependency Use your observability stack to find the root cause.
This type of project is excellent for interviews because it proves practical ability.
Common Beginner Mistakes in Observability
Mistake 1: Learning Tools Without Concepts
Do not start with dashboards before understanding metrics, logs, traces, and telemetry.
Concepts first.
Tools second.
Mistake 2: Creating Too Many Dashboards
More dashboards do not mean better observability.
A good dashboard should answer a specific question.
Mistake 3: Alerting on Everything
Too many alerts create alert fatigue.
A good alert should be actionable, urgent, owned, and connected to user impact.
Mistake 4: Ignoring Logs and Traces
Metrics show what changed.
Logs show details.
Traces show request flow.
You need all three.
Mistake 5: Ignoring Cardinality
Bad metric labels can create performance and storage problems.
Avoid labels such as user ID, request ID, and session ID in Prometheus metrics.
Mistake 6: Treating Certification as the Finish Line
Certification is useful, but practical skill matters more.
Use certification as a milestone, not the final destination.
Mistake 7: Not Practicing Incidents
You should intentionally break things in a lab.
That is how you learn real troubleshooting.
How to Choose the Best Observability Course for Beginners
Before choosing an observability course, ask these questions:
Does it explain observability vs monitoring? Does it teach metrics, logs, and traces? Does it include Prometheus? Does it include Grafana? Does it include OpenTelemetry? Does it include logs with Loki or ELK? Does it include distributed tracing with Jaeger or Tempo? Does it include Kubernetes observability? Does it teach SLOs and error budgets? Does it include hands-on labs? Does it include assignments? Does it include capstone projects? Does it prepare learners for certification? Does it teach incident response and root cause analysis? If the answer is yes, the course is worth serious consideration.
If the course only teaches one tool, it may still be useful, but it is not a complete observability course.
A complete observability course should help you understand the full production picture.
Final Recommendation
If you are a beginner, observability is one of the best skills you can learn for a DevOps, SRE, cloud, platform, or backend engineering career.
Start with the basics.
Understand monitoring vs observability.
Learn metrics, logs, and traces.
Then move into Prometheus, Grafana, logging, distributed tracing, OpenTelemetry, Kubernetes observability, SLOs, alerts, and incident response.
Most importantly, build projects.
Do not only watch tutorials.
Deploy systems, collect telemetry, create dashboards, trigger alerts, simulate failures, and troubleshoot them.
That is how observability becomes real.
The Master in Observability Engineering Certification by DevOpsSchool is a strong fit for this journey because it brings the complete observability stack into one structured path: Prometheus, Grafana, OpenTelemetry, ELK, Jaeger, Kubernetes observability, SLOs, assignments, capstone projects, and certification validation.
For beginners, it gives direction.
For DevOps engineers, it gives production visibility.
For SRE engineers, it gives reliability skills.
For developers, it gives instrumentation confidence.
And for teams, it creates engineers who can look at metrics, logs, and traces and understand what is really happening in production.
That is the true goal of an observability course.
Not just dashboards.
Not just tools.
Real production understanding.
FAQs
What is the best observability course for beginners?
The best observability course for beginners should cover metrics, logs, traces, Prometheus, Grafana, OpenTelemetry, Kubernetes observability, SLOs, alerts, hands-on labs, and capstone projects.
Is observability hard to learn?
Observability can feel complex at first because it includes many tools and concepts. But if you follow a step-by-step learning path, it becomes manageable.
Should I learn Prometheus or Grafana first?
Learn metrics basics first, then Prometheus, then Grafana. Prometheus collects and queries metrics. Grafana visualizes them.
Should beginners learn OpenTelemetry?
Yes, but after understanding metrics, logs, traces, and instrumentation basics. OpenTelemetry is easier to learn when you understand the signals it collects.
Is observability useful for DevOps engineers?
Yes. DevOps engineers use observability to understand production health after deployments, infrastructure changes, and Kubernetes operations.
Is observability useful for SRE engineers?
Yes. SRE engineers use observability for SLOs, error budgets, incident response, reliability dashboards, alerts, and root cause analysis.
What tools should beginners learn for observability?
Beginners should learn Prometheus, Grafana, OpenTelemetry, Loki or ELK, Jaeger or Tempo, Alertmanager, and Kubernetes observability tools.
What is the role of Grafana in observability?
Grafana is used to visualize metrics, logs, traces, alerts, and SLOs through dashboards and panels.
What is the role of Prometheus in observability?
Prometheus collects, stores, and queries metrics. It is widely used for monitoring, alerting, Kubernetes observability, and SLO measurement.
What is the role of OpenTelemetry in observability?
OpenTelemetry helps generate, collect, process, and export telemetry data such as metrics, logs, and traces in a vendor-neutral way.
Is certification important for observability?
Certification is useful when it includes hands-on practice, assignments, projects, and practical assessment. It helps validate skills and gives structure to learning.
Which certification is good for observability beginners?
A broad certification like DevOpsSchool’s Master in Observability Engineering Certification is a good fit because it covers Prometheus, Grafana, OpenTelemetry, logs, traces, Kubernetes observability, SLOs, assignments, and capstone projects.
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Lamine Yamal Teases Upcoming Beats Over-Ear Headphones

Ahead of the upcoming World Cup, football superstar Lamine Yamal has arrived at training camp for the Spanish national team sporting what seems to be the unreleased over-ear headphones that appeared in a U.S. Federal Communications Commission database last week. As suspected, the new headphones are a Beats product rather than an Apple product.


In a post on his Instagram account, Yamal shared several photos and a video clip showing him arriving to training camp with the new headphones in a pink color.



We don't know any other details on the upcoming headphones, and it's unclear whether they are a next-generation version of the Beats Studio Pro or if they will carry a new name. They feature a distinctly different design than the Beats Studio Pro, with flatter exteriors on the ear cups and a completely different headband design that appears to include tubular telescoping arms rather than the wider and flatter arms of the Beats Studio Pro.

A release date for the new Beats headphones is currently unknown, but it shouldn't be too far in the future given that they've already received FCC approval and are being seeded to key influencers like Yamal.Tag: Beats
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AirPods Weekend Deals Include AirPods 4 for $99 and AirPods Max 2 for $509

Amazon today has the AirPods 4 available for $99.00, down from $129.00. This is a second-best price on the AirPods 4, which is the base model without Active Noise Cancellation, and it's accompanied by a solid deal on the AirPods Max 2.

Note: MacRumors is an affiliate partner with some of these vendors. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

Amazon provides a June 4 estimated delivery date for free shipping, with faster delivery options for Prime members. We haven't tracked an all-time low price on the AirPods 4 in a few months, so Amazon's deal this weekend is a solid option if you've been waiting for a sale.

$30 OFFAirPods 4 for $99.00

Additionally, you can get the AirPods Max 2 on sale for $509.00 right now on Amazon, down from $549.00. This one is available in Blue and Starlight, with similar June 4 delivery estimates as the AirPods 4. This is a match of the all-time low price on the AirPods Max 2.

$40 OFFAirPods Max 2 for $509.00

Head to our full Deals Roundup to get caught up with all of the latest deals and discounts that we've been tracking over the past week.



Deals Newsletter

Interested in hearing more about the best deals you can find in 2026? Sign up for our Deals Newsletter and we'll keep you updated so you don't miss the biggest deals of the season!




Related Roundup: Apple Deals
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PAN-OS GlobalProtect Authentication Bypass (CVE-2026-0257) Under Active Exploitation

Palo Alto Networks has warned that a recently disclosed medium-severity security flaw impacting PAN-OS and Prisma Access has come under active exploitation in the wild. The vulnerability, tracked as CVE-2026-0257 (CVSS score: 7.8), refers to a case of authentication bypass that could be exploited by bad actors to set up VPN connections. "Authentication bypass vulnerabilities in theView the full article
Hacker News

Russia-aligned crime group Greyvibe extensively uses AI in attacks

Researchers have uncovered a previously undocumented Russian group that makes extensive use of large language models (LLMs) in its attacks against private, government, and military organizations in Ukraine. It uses a variety of attack vectors along with custom malware, with the goal of intelligence gathering for the ongoing war.
Dubbed Greyvibe by researchers from WithSecure, the group has shown systematic use of generative AI across all stages of its operations, from crafting spear phishing lures and malicious scripts to full on malware development and setting up of backend infrastructure.
“While the activities align with Russian state interests, several observed indicators suggest the group has ties to the broader cybercrime ecosystem, with the group potentially involving current or former cybercriminal actors,” the WithSecure researchers said in their report.
Shifting attack vectors
Greyvibe’s first campaign was launched in August 2025, with a series of spear phishing emails that purported to come from Ukrainian officials and government agencies including the Kyiv City, the Main Directorate of the State Emergency, and the State Service of Special Communications and Information Protection.
The emails included links to ZIP and RAR archives, hosted on Google Drive and a service called 4sync, that contained malware loaders written in Python and JavaScript. The final payload was a custom malware program developed by the group that the WithSecure researchers dubbed PhantomRelay.
In another attack in October, the group experimented with ClickFix-style attacks on fake CloudFlare CAPTCHA pages. These attacks instructed users to open the Windows Run dialog and paste in malicious commands.
Greyvibe also set up fake adult club websites in Ukrainian, as well as fake websites for charities claiming to support the Ukrainian military with FPV drones and UAVs. These attacks distributed several malware programs for both Android devices (FallSpy) and Windows (PhantomRelay and LegionRelay).
The researchers also tracked a website in Russian that they believe was part of the group’s operations; it referenced hard-coded telephone exchange numbers for secure telecommunications that are typically used by the Russian military.
“The intended victimology of this activity remains unclear,” the researchers said. “However, the most plausible hypothesis is that the lure was designed to deceive Ukrainian military personnel by presenting the illusion of access to a Russian military terminal.”
Custom malware developed using LLMs
The PhantomRelay malware program is a remote access trojan (RAT) written in PowerShell that can execute additional custom scripts received from the command-and-control (C2) server. While variants of this program have been observed in activity that might be unrelated to Greyvibe, the group completely rewrote the tool and created a version that was exclusively used in its own operations.
LegionRelay is another PowerShell-based RAT that can similarly execute commands and scripts received from the C2 server; it is used for file enumeration, file exfiltration, screenshot capture, browser data theft, Telegram and WhatsApp data exfiltration, RDP access setup and other actions.
FallSpy is an Android spyware program that can steal contacts, call logs, a list of installed applications, SIM-linked phone numbers, device and network information, Wi-Fi SSID, the phone’s last known location, its public IP address, and media files.
Finally, a series of custom scripts for obfuscating and loading malware was also observed: LOOKVALPS (PowerShell), LOOKVALJS (JavaScript), DAYLIGHT (PowerShell), and TEASOUP (JavaScript).
The WithSecure researchers have determined, with moderate confidence, that several of these custom tools were developed with the help of LLMs. LegionRelay in particular, as well as the background infrastructure serving it, show strong indicators of AI generation. The researchers believe some of the platforms used by the attackers include Ideogram AI, ChatGPT and Google Gemini.
“Greyvibe appears to use AI not only for isolated development tasks, but across multiple operational phases,” the researchers said. “This likely enables the group to compensate for capability gaps, accelerate development cycles, and potentially reduce historical backlinks to prior activity. Given this extensive use, we expect the group’s tradecraft to continue evolving and diversifying, likely increasing the complexity of continuous detection, tracking, and attribution.”
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Microsoft and security researcher’s dueling posts about cybersecurity disclosures get nasty

Microsoft and a prominent cybersecurity researcher have gotten into a very public and rather personal exchange of unpleasantries about what responsible cybersecurity disclosures should mean in 2026. 
A cybersecurity researcher going by the name Nightmare Eclipse, who has disclosed several cybersecurity holes before patches were available, posted that he had tried to contact Microsoft officials and was rebuffed, which led him to publish details about the bugs.
“When I actively asked you [Microsoft] to communicate with me, you refused, humiliated me and made sure to insult me in front of people. You defame me in public with your CVE-2026-45585 advisory even though you literally deleted the Microsoft account I used to report bugs to you with and I got zero pennies from doing so and I still happily did like an idiot,” the researcher posted, adding that Microsoft has now deleted his GitHub account. “You are proving to everyone that you [are] actively escalating this conflict but I’m done begging you.”
The researcher then made a cryptic threat: “Mark this date July 14th, I will make sure your bones are shattered that day.” 
In another post, the researcher was even more direct: “I was told personally by [Microsoft] that they will ruin my life and they did” adding that Microsoft will “do everything but support the research community, I won’t disclose details, but they sabotage people a lot.”
Microsoft responded with its own post saying that some of the vulnerabilities revealed by the researcher “were not responsibly disclosed” and that there was an “unnecessary risk created by these disclosures,” adding, “uncoordinated disclosures that put proof-of-concept code for unpatched vulnerabilities into the hands of bad actors are never justifiable, and have real-world consequences.”
It was then Microsoft’s turn to get personal, with the veiled implication that the researcher has a bad reputation. “We always have and will continue to welcome vulnerability submissions from  anyone through our public researcher portal, regardless of past interactions or reputation,” the post said.
However, one senior Microsoft security executive posted a slightly more upbeat message, suggesting that the company may now have to rethink how it handles cybersecurity bug reports. 
“At this time, we are not changing our bug bar or the criteria we use to decide when a fix is required, though we will continue to evaluate as conditions evolve. Severity continues to be grounded in real-world impact and exploitability, drawing on the full set of signals in the Security Update Guide,” wrote Tom Gallagher, VP of engineering at the Microsoft Security Response Center (MSRC). 
“We will continue to anchor on a predictable rhythm and a disciplined process, while adapting as needed to the conditions in front of us,” he said. “What we encourage in turn is a thoughtful look at whether the practices that worked well for the patching landscape of a few years ago are still well matched to where the landscape is heading. The fundamentals have not changed. The pace at which they need to be applied is changing.”
CSOonline reached out to both Microsoft and Nightmare Eclipse, and neither provided any clarification or additional comments by publication time.
Frustration on both sides
One of the issues behind the debate over cybersecurity disclosure policies is that many researchers feel that their disclosures are often either ignored or the patch is unreasonably delayed by major vendors, including Microsoft. 
Adding to researchers’ frustration is the fact that vendors often do not communicate well about where things stand with a reported security problem. 
But vendors have their own complaint: they can’t address every one of the many holes that are reported to them quickly, given finite resources, and they must prioritize what they patch.
A related issue is the belief that major vendors, including Microsoft, will quickly prioritize patches once the hole becomes public; one example was the Microsoft Authenticator flaw, which Microsoft had known about for eight years before fixing it after it was publicized.  
Both sides may be right
Consultants and cybersecurity executives said both sides make good points in this instance. 
“Microsoft is right that uncoordinated zero‑day drops create real and immediate risk for customers, and researchers are right that vendors sometimes move only when pushed,” said cybersecurity consultant Brian Levine, executive director of FormerGov. “Both truths can exist at the same time.”
And, Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, added, “the cry from the security researcher feels like there is something vindictive going on. If the researcher believes that [Microsoft] acted unethically or illegally and has evidence in that respect, they could raise complaints with the appropriate authorities, rather than write a blog post. I am inclined to believe Microsoft more in this case.”
Gary Longsine, CEO of Intrinsic Security, also pushed back against Nightmare Eclipse, questioning whether they are functioning as an objective security researcher.
“This person might have a legitimate grievance of some sort against Microsoft, however, legitimate security researchers don’t do things this way,” he said. “I don’t do things that cause damage to literally billions of innocent bystanders, as retribution for whatever slight I may perceive. This is an attacker, an adversary, not a security researcher.”
Erosion of trust
In addition, Ishraq Khan, CEO of coding productivity tool vendor Kodezi, said that he is concerned about the emotional elements of the exchange between the researcher and Microsoft, because it is eroding trust, and that erosion is potentially the biggest danger.
“The researcher appears to believe the relationship failed long before the disclosures occurred. Reading the public posts, the recurring theme is not simply vulnerability research, but frustration over communication, trust, and access to the disclosure process,” Khan said. “Whether those claims are accurate or not, the researcher clearly believes private channels stopped working and that escalation was the only remaining option.”
And that erosion of trust, Khan said, is a critical issue, because AI, especially autonomous agents, is going to require far more trust between vendors and researchers. 
“The industry is entering a new era of vulnerability discovery. We are seeing increasingly capable AI systems uncover bugs, identify attack paths, and assist researchers in ways that were not possible a few years ago. The volume of discovered vulnerabilities is increasing while the time between discovery and potential exploitation is shrinking,” Khan said. “That changes the dynamics of disclosure. Historically, researchers and vendors were operating on a timeline measured in months. Today, discoveries can spread globally within hours. A breakdown in trust that might have once affected a handful of people can now affect entire ecosystems.”
He added, “the reality is that responsible disclosure only works when both sides believe the system is functioning. Researchers need confidence that findings will be taken seriously. Vendors need confidence that researchers will give them enough time to protect customers. Once either side loses faith in that process, the entire model becomes fragile.”
“What concerns me is that these disputes appear to be becoming more public, more adversarial, and more personal. Once security discussions shift from technical facts to questions of intent, reputation, and motivation, customer protection risks becoming secondary to the conflict itself.”
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DuckDuckGo's 'No AI' Search Traffic Climbs as Users Reject Google's AI Overhaul

Privacy-focused search engine DuckDuckGo has seen a surge in demand for its "No AI" search option in the wake of Google's May 19th I/O announcements. Google debuted a new "intelligent" search box reimagined with AI. It features AI suggestions as an upgrade to autocomplete, support for follow-up questions, expanded Personal Intelligence for connecting Gmail and Google Photos, and Search agents.


DuckDuckGo told MacRumors that visits to its No AI search page more than tripled after Google's announcement. Traffic hit the 3x mark on May 28th, and has continued to climb. Visits have averaged around 84 percent above baseline consistently since May 19.

DuckDuckGo is embracing demand for No AI search options, and it is promoting new extensions available for Chrome and Firefox that set No AI search as the default.

No AI search has no AI-assisted answers, no chat interface, and it surfaces fewer AI images. DuckDuckGo can be set as the default search engine on Apple devices, but not the specific No AI page. DuckDuckGo has its own AI tools, but they are turned off for people who opt for the No AI experience.

DuckDuckGo plans to add No AI search settings to its original extensions for Chrome, Firefox, Edge, and Opera in the near future.

Along with DuckDuckGo, there are other privacy-focused search engine options that minimize AI results. Paid search engine Kagi is one example, with no visible AI information unless you opt for AI tools. Kagi is $5 per month for a limited number of searches, and $10 a month for unlimited searches.

Because it is a paid search engine, it does not have ads and it does not collect and sell user data.Tags: DuckDuckGo, Google
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LG's 39-Inch Ultrawide 5K2K OLED Display Officially Begins Shipping Next Week

Back at CES in January, LG unveiled its UltraGear evo GX9 (39GX950B) display, which it claims is the world's first 39-inch ultrawide 5K2K OLED gaming monitor, offering a large curved canvas in the increasingly popular 21:9 aspect ratio with the added benefit of OLED technology for enhanced contrast with true blacks, standard refresh rates of up to 165Hz, and more.


While LG began taking pre-orders for the UltraGear evo GX9 last month and a few early orders have already trickled out through various channels, LG says that the official kickoff of order shipments starts next week.

LG touts the gaming prowess of the UltraGear evo GX9, but its specs mean it can deliver a premium experience across a variety of use cases, from productivity to media consumption and more.

OLED technology delivers a contrast ratio of 1,850,000:1 across the ultrawide display's 5,120 x 2,160 resolution. At a large 39-inch display size with a 1500R curve, this translates to a density of 143 pixels per inch, which is solid but not enough for true retina-level quality. Still, the large, curved display means many users will often be sitting further from the display than usual to be able to take in the full scope of content on the display, and that should prove plenty sharp in most situations.

The Tandem OLED panel in the UltraGear evo GX9 supports up to 335 nits of typical brightness, which is likely sufficient for most uses but does lag behind some other displays including ones in Apple products. The OLED contrast, color fidelity at up to 98.5% of the DCI-P3 spectrum, and HDR support that can push brightness to 1,500 nits at 1.5% APL and 600 nits at 10% APL should, however, all help to offer a quality viewing experience.


For those who do want to game on this display, the UltraGear evo GX9 features AMD FreeSync Premium Pro and NVIDIA G-SYNC support, as well as 0.03ms response times to keep up with fast-moving content.

On the connectivity side, the UltraGear evo GX9 offers a USB-C port with 90-watt power delivery to a connected computer, as well as DisplayPort 2.1 and HDMI 2.1.

We'll be looking to go hands-on with the LG UltraGear evo GX9 as soon as we can, and we'll report back on how well it works for Mac users, but for now LG is taking orders on its own site priced at $1,799.99, and it's also available at Amazon for the same price with delivery quotes starting around June 8.

MacRumors is an affiliate partner with LG and Amazon. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.Tags: LG, OLED
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iPhone Driver's License Feature Set to Expand to a 15th U.S. State

Apple's digital driver's license feature in the Wallet app is set to expand to Virginia, according to a person familiar with the matter.


In select U.S. states, residents can add their driver's license or state ID to the Wallet app on the iPhone and Apple Watch, and then use it to display proof of identity or age at select airports and businesses, and in select apps. The feature has rolled out to 14 states so far, including Arkansas earlier this week, and it is also available in Puerto Rico.

The other states are Arizona, Maryland, Colorado, Georgia, Ohio, Hawaii, California, Iowa, New Mexico, Montana, North Dakota, West Virginia, and Illinois.

Now, Apple is preparing for the feature to go live in Virginia, the person said. However, we do not have an exact timeframe for availability. Towards the end of 2025, Virginia's Department of Motor Vehicles said it planned to support the Apple Wallet's digital ID feature in the coming months, so hopefully it goes live soon by this point.

When the feature goes live, Virginia residents will be able to set it up by opening the Wallet app on the iPhone and tapping on the plus sign in the top-right corner. Next, they will tap on Driver's License and ID Cards, select Virginia from the list once it is added, and follow the on-screen steps to complete the process.

Apple Wallet IDs are accepted at TSA checkpoints at hundreds of U.S. airports for domestic travel. Given that Apple Wallet IDs are not accepted by law enforcement, and lack many other use cases, carrying a physical ID is still necessary.

If you live in a state that does not yet offer Apple Wallet IDs, you can create a Digital ID based on your U.S. passport, and present it at the same participating TSA checkpoints, for age and identity verification purposes during domestic travel. It is not a replacement for a physical passport, and it cannot be used for international travel.

The passport feature requires iOS 26.1 or watchOS 26.1 and later.Tag: Apple Wallet
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Everything We Know About OpenAI's Planned iPhone Rival

OpenAI is developing a smartphone intended to compete directly with the iPhone, in what appears to be a significant departure from the company's previously stated hardware strategy. Here's everything we know so far.


Analyst Ming-Chi Kuo published his findings in late April following supply chain checks, describing the device as an "AI agent phone" built around a continuous, context-aware interface rather than individual apps. Kuo argued that the smartphone is the only device that captures a user's full real-time state, including location, activity, communication, and context, making it uniquely suited to AI agent inference.

He said fully controlling both the operating system and the hardware is the only way for OpenAI to deliver a comprehensive AI agent service, and that AI agents will fundamentally shift how people interact with a phone, moving the focus from launching individual apps to completing tasks through a seamless interface.

Specifications

OpenAI's phone is said to use a customized version of MediaTek's Dimensity 9600 processor, built on TSMC's N2P node in the second half of 2026. Kuo initially named both MediaTek and Qualcomm as chip partners but has since said MediaTek appears "better positioned to become the sole processor supplier."

Luxshare Precision Industry is believed to be the exclusive manufacturing partner. Separately, Kuo reported that Sunny Optical has secured component orders for two OpenAI devices, including the smartphone. This is likely for the camera module.

The device's headline known hardware specification today is its image signal processor, which includes an enhanced HDR pipeline intended to improve real-world sensing through the camera. It is also said to use two AI processors for handling different tasks simultaneously, such as vision and language processing, along with fast memory and storage and security features to isolate processes.

What About Jony Ive's Devices?

The phone represents a notable reversal in OpenAI's publicly stated strategy. The company's hardware ambitions had previously been described as centered on non-phone form factors developed with former Apple design chief Jony Ive, whose startup io Products OpenAI acquired for $6.5 billion in May 2025. Ive and CEO Sam Altman had specifically said they did not want to build a device with a screen, with Altman describing a prototype to employees as "the coolest piece of technology that the world will have ever seen."

The first product from that collaboration was delayed out of 2026 and has since been identified as a smart speaker with an integrated camera, priced between $200 and $300 and expected to launch in early 2027. Other devices reportedly in development include smart glasses, a smart lamp, and potentially earbuds, though those products are further out on the roadmap and some could be cancelled.

OpenAI has also been aggressively recruiting from Apple's hardware ranks, hiring over 40 former Apple employees. The hires include former Apple designers Evans Hankey, Tang Tan, and Scott Cannon, prompting Apple to offer its iPhone Product Design team retention bonuses of up to $400,000 in restricted stock units to counter the poaching.

Timeline

Mass production of OpenAI's smartphone was originally believed to be targeted for 2028, but Kuo has since revised that expectation to the first half of 2027. The accelerated timeline is said to reflect OpenAI's planned IPO, where a compelling hardware product could strengthen the company's investor narrative, as well as intensifying competition in the AI agent phone category. Kuo projects combined 2027 and 2028 shipments could reach around 30 million units if development stays on track.

What Does It Mean for Apple?

If the broader hardware lineup ships, OpenAI will be a direct competitor to Apple across several product categories. Apple is rumored to be developing smart glasses, AirPods with cameras, an AI pendant, and a smart home hub with enhanced Siri capabilities. On the day Kuo published his initial report, Altman posted on X that it "feels like a good time to seriously rethink how operating systems and user interfaces are designed."Tag: OpenAI
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DNS-AID will make AI agents easier to discover, says Linux Foundation

As AI agents become more numerous and more communicative, keeping track of where to find them is becoming increasingly important. Numerous proprietary agent registries are on the market, but the Linux Foundation suggests we simply extend the distributed, open Domain Name System (DNS) infrastructure we already have.
The foundation is now inviting contributions to the DNS-AID project, a standard way for AI agents to discover, verify, and communicate with one another over DNS that requires no new infrastructure. It enables agents and Model Context Protocol (MCP) servers to use DNS as a global, vendor-neutral directory.
While many details remain to be worked out, the proposal suggests domain owners create a new well-known address that can provide a starting point for agents looking for one another: _index._agents.{domain}.
This approach ensures that agent discovery remains scalable, secure, and compatible with the protocols that underly the internet, the Linux Foundation said.
“AI agents are quickly becoming the connective tissue of the modern internet, but without secure, open discovery infrastructure, that connectivity becomes a liability,” said Jim Zemlin, CEO at the Linux Foundation. “DNS-AID helps anchor agent discovery in the DNS infrastructure that the internet already trusts.”
DNS-AID was initially developed by staff at Infoblox, and the latest internet draft of the DNS-AID proposal includes contributions from staff at Deutsche Telekom and Amazon. The Linux Foundation said it intends that DNS-AID will remain vendor-neutral.
This article first appeared on InfoWorld.
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Windows PC Industry Reacts to Apple's Most Affordable MacBook Ever

A few months ago, Apple released the MacBook Neo, its most affordable MacBook ever. At the time, an ASUS executive admitted that the laptop came as a "shock" to the Windows PC industry, which is now in the process of responding.


Acer today introduced a Swift Air 14 laptop, with U.S. pricing starting at $699. By comparison, the MacBook Neo starts at $599 with a 256GB SSD and 8GB of RAM, or at $499 for college students and educational staff. However, the MacBook Neo costs an equal $699 when configured with a doubled 512GB of storage and a Touch ID button.

Powered by a new Intel Core Series 3 processor, the Swift Air 14 features a 14-inch display with a 120Hz refresh rate and a resolution of 1,920 × 1,200 pixels, up to a 512GB SSD, up to 16GB of RAM, an all-aluminum enclosure, and quad speakers with DTS:X Ultra audio. Like the MacBook Neo, the laptop supports Wi-Fi 6E.

Acer's Swift Air 14
The laptop is equipped with two Thunderbolt 4 ports, a USB-A port, and a 3.5mm headphone jack, and Acer says a 70 Wh battery provides up to 19 hours of battery life for video playback and up to 16 hours of battery life for web browsing.

Like the MacBook Neo, the Swift Air 14 is available in colorful finishes, including sage green, frost blue, blossom pink, and lilac purple.

Acer said the Swift Air 14 will be available in North America starting in August.

Meanwhile, Qualcomm this week announced the Snapdragon C, a new processor designed for "entry-tier laptops" priced at "$300 and up." Qualcomm said the processor delivers "responsive everyday performance" with "breakthrough power efficiency." The first laptops powered by the Snapdragon C are expected to launch later this year, with committed brands including Acer, HP, and Lenovo, according to Qualcomm.

Qualcomm's Snapdragon C processor
Indeed, Acer has previewed the Aspire Go 15, the first laptop powered by the Snapdragon C processor. The laptop will have an "affordable" price point, but Acer did not provide specific pricing or a release date. Key specs include a 15.6-inch display with a resolution of 1,920 × 1,080 pixels, up to a 512GB SSD, up to 8GB of RAM, a 1080p webcam, two speakers, two USB-C ports, one USB-A port, one HDMI port, and a 3.5mm headphone jack.

Acer said the Aspire Go 15 is made from 100% recyclable materials and has some components made from recycled plastic, so it sounds like the laptop will not have an all-aluminum enclosure like the MacBook Neo and the Swift Air 14.

Finally, ASUS commented on the MacBook Neo again during its annual shareholders meeting today. According to Taiwan's Economic Daily News, ASUS's chairman Jonney Shih said that the company can learn from Apple's cost-efficient strategy with the MacBook Neo and views it as an opportunity. Stay tuned, he said.

On an earnings call last month, Apple's CEO Tim Cook said that customer response to the MacBook Neo had been "off the charts" since its launch.

Apple was very optimistic about the MacBook Neo before announcing it, but the company still "undercalled" the level of enthusiasm that the laptop would generate, according to Cook. He said that MacBook Neo demand exceeded Apple's expectations and helped to drive a record number of first-time Mac buyers last quarter.

"We could not be happier with how things are going at the moment," said Cook.

As for the Windows PC industry, perhaps not so much.Related Roundup: MacBook NeoTags: Acer, Asus, Intel, Qualcomm, WindowsBuyer's Guide: MacBook Neo (Buy Now)Related Forum: MacBook Neo
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Certifiably random: Swiss researchers claim perfect random number source

Researchers in Switzerland claim to have built a perfect random number generator from two quantum superconducting chips, a 30-meter-long pipe, and some software. The resulting device could be used to generate cryptographic keys, or to offer a “public randomness service” for lotteries or blockchain applications, they say.
They’re not the first to make the claim.
Many sources of randomness are biased. For example, coins or dice tend to favor one side. “Even modern random number generators, which are based on quantum mechanical effects like the reflection of photons from beam splitters, are not entirely immune to such a systematic error or ‘bias’,” said Andreas Wallraff, one of the leaders of the research team at ETH Zurich.
Similar biases can be found in purely software-based pseudo-random number generators. This has led to security problems in IoT devices and WhatsApp, among other applications.
To get around that, the researchers set up of two supercomputing chips, each representing one qubit, cooled to near absolute zero. The chips are connected by a 30-meter-long microwave guide, similarly cooled, and the microwave photons flying between them create a situation of quantum entanglement.
The results produced by this process are then transformed via a special algorithm to generate perfect randomness. “The resulting sequence of zeros and ones is now really perfectly random, and we can even certify that,” said Renato Renner, the other team leader. “The technical improvements allowed us to create random numbers that will remain perfectly random for all eternity.”
The team published their results this week in an article entitled “Experimental randomness amplification” in Nature.
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Attackers Use LLM Agent for Post-Exploitation After Marimo CVE-2026-39987 Exploit

An unknown threat actor has been observed using a large language model (LLM) agent to conduct post-compromise actions after obtaining initial access following the exploitation of a publicly-accessible Marimo network using a recently disclosed vulnerability. "The attacker compromised an internet-reachable Marimo notebook via CVE-2026-39987, extracted two cloud credentials from the compromisedView the full article
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Best Apple Deals of the Week: M5 MacBook Air Gets $199 Discounts, Plus Save on Samsung's New Monitors

The M5 MacBook Air hit new all-time low prices this week, with $199 off nearly every model of the computer on Amazon. We're also tracking an ongoing low price on the AirPods Max 2, plus great discounts from Anker and Samsung.

Note: MacRumors is an affiliate partner with some of these vendors. When you click a link and make a purchase, we may receive a small payment, which helps us keep the site running.

M5 MacBook Air


What's the deal? Take $199 off M5 MacBook Air
Where can I get it? Amazon
Where can I find the original deal? Right here
$199 OFF13-inch M5 MacBook Air (512GB) for $899.99
$199 OFF13-inch M5 MacBook Air (16GB/1TB) for $1,099.99

Amazon has sweetened its deal on the 512GB 13-inch M5 MacBook Air this week, dropping the price of the notebook down to $899.99, from $1,099.00. This is a new record low price on the 13-inch M5 MacBook Air, and you'll find $199 off every 13-inch model right now on Amazon.

Anker


What's the deal? Save on Anker charging accessories
Where can I get it? Amazon
Where can I find the original deal? Right here
$49 OFFAnker Prime 3-in-1 Wireless Charging Station for $109.99

Anker's new Prime 3-in-1 Wireless Charging Station has been marked down to $109.99 on Amazon, down from $149.99. This is one of Anker's newest accessories, and Amazon's sale today is just $5 higher compared to the all-time low price.

Samsung


What's the deal? Save on Samsung's new 2026 monitors
Where can I get it? Samsung
Where can I find the original deal? Right here
$50 OFF PLUS EXTRASSamsung 2026 Monitors

Samsung's newest monitors are now available to purchase this week, including the Odyssey G8, ViewFinity S8, and Movingstyle Essential. All of these are available with a $50 launch discount, plus your choice of extras including up to $300 in Samsung credit on a future purchase, a free Music Studio speaker, or free Galaxy Buds4 Pro.

AirPods Max 2


What's the deal? Take $40 off AirPods Max 2
Where can I get it? Amazon
Where can I find the original deal? Right here
$40 OFFAirPods Max 2 for $509.00

Amazon this week has a record low price on the AirPods Max 2, now available for $509.00, down from $549.00. This sale is available in two colors of the headphones.

If you're on the hunt for more discounts, be sure to visit our Apple Deals roundup where we recap the best Apple-related bargains of the past week.



Deals Newsletter

Interested in hearing more about the best deals you can find in 2026? Sign up for our Deals Newsletter and we'll keep you updated so you don't miss the biggest deals of the season!




Related Roundup: Apple Deals
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Top Stories: iOS 27 Leaks, MacBook Ultra Rumors, and More

WWDC 2026 is coming up very quickly now and we're continuing to learn more about what to expect in iOS 27 and other updates, so make sure to check in to see the latest as we head toward Apple's big week, which kicks off with the traditional keynote on Monday, June 8.


While we may not see anything on the hardware side at WWDC, Apple does have plenty of products in the pipeline, and this week gave us an opportunity to check in on where things stand with the high-end "MacBook Ultra," the long-rumored foldable iPhone, and more, so read on below for all the details!

Top Stories

iOS 27's Siri App and 'Search or Ask' Feature Shown in Leaked Images

With less than two weeks to go until the official unveiling of iOS 27 at WWDC 2026, Bloomberg's Mark Gurman has shared some new re-created screenshots showing off what the revamped Siri will look like, in both standalone app form and a pop-up "Search or Ask" version associated with the Dynamic Island. Additional re-created screenshots show how Siri and AI will be more integrated into the Camera and Photos apps.


The revamped Siri will use a dark color scheme similar to that seen on WWDC 2026 promotional artwork, and iOS 27 will include other enhancements such as revamped AirPods settings, quality improvements for Genmoji and Image Playground creations, and more.

MacBook Ultra: 5 Features That Could Justify the Name

Reports and rumors suggest the next MacBook Pro that Apple will release might not be a ‌MacBook Pro‌ at all. It could actually be something altogether new and more exciting – a "MacBook Ultra" – positioned above the Pro as Apple's top-tier laptop, suggesting that the current M5 Pro and M5 Max models will remain on sale when it launches.


In a recent recap, we listed the key features we are expecting in the MacBook Ultra, which is likely to go on sale either later this year or in early 2027. As things stand, the latter time frame is now looking more likely, owing to the global memory chip shortage.

Apple Watch for Diabetes: The Latest on Apple's Plans for Non-Invasive Blood Sugar Monitoring

For many years now, it has been rumored that the Apple Watch will eventually gain non-invasive blood sugar monitoring capabilities, which would enable millions of people with diabetes to track their blood glucose levels without needing to prick their skin with a needle or wear a dedicated continuous glucose monitor.


According to Bloomberg's Mark Gurman, Apple recently shifted oversight of the project from its platform architecture chief Tim Millet to Zongjian Chen, a senior engineer overseeing advanced technologies within the company. He framed this change as positive news for the project, which has apparently been in development for more than 15 years.

Apple Seeds First iOS 26.6 and iPadOS 26.6 Betas to Developers

Even though WWDC is right around the corner, Apple still has another iOS 26 update in the works to tide us over until iOS 27 is ready for prime time, and that's iOS 26.6, which saw its first beta release this week.


We haven't spotted much new in this update yet other than potentially a new alert that will pop up when you've reached the maximum number of blocked contacts, but with that limit into the thousands, most users won't ever hit the cap.

Ferrari Reveals $640,000 EV Co-Designed by Jony Ive

Despite billions of dollars in investment, the Apple Car never came to fruition, but the just-unveiled Ferrari Luce may offer a glimpse of some things we might have seen had Apple's project panned out.


The $640,000 Luce is Ferrari's first all-electric car, and former Apple design chief Jony Ive and his LoveFrom collective were heavily involved in the design of the vehicle.

First Cases for Apple's Foldable iPhone Surface Online

Foldable smartphones present special challenges for case manufacturers looking to offer protection for the devices while still allowing them to fold and unfold properly, so third-party companies are already hard at work designing options for Apple's upcoming foldable iPhone.


Case makers routinely begin mass producing accessories ahead of a new iPhone announcement, working from dummy units or leaked CAD files to size their molds. Their designs are speculative, but they have historically proven accurate to the millimeter, since accessory makers cannot afford to be left without product on launch day.

Meanwhile, we continue to hear about hiccups as Apple seeks to ramp up toward mass production on the new device, with the latest being that Apple's supply chain is seeing issues with early-stage assembly procedures affecting production yields. This comes after word that issues with hinge reliability were also cropping up. Apple is, however, reportedly still aiming for a release later this year, though supplies may be very limited to start.

MacRumors Newsletter

Each week, we publish an email newsletter like this highlighting the top Apple stories, making it a great way to get a bite-sized recap of the week hitting all of the major topics we've covered and tying together related stories for a big-picture view.

So if you want to have top stories like the above recap delivered to your email inbox each week, subscribe to our newsletter!Tag: Top Stories
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First Look at iPhone 18 Pro Color Options Revealed by Dummy Models

Leaker Sonny Dickson today shared images of iPhone 18 Pro dummy models in the device's four rumored colors, offering the first real-world look at what to expect from the lineup visually.


Corroborating previous rumors, the dummies show the ‌iPhone 18 Pro‌ Max in Light Blue, Black, Silver, and Dark Cherry. Dickson said "Cherry will probably be the next hit, orange did very well." Cosmic Orange was the signature color of the iPhone 17 Pro and proved popular with customers.

Dark Cherry is expected to serve as the headline new color for the ‌iPhone 18 Pro‌ models this year. The color has been in the rumor mill since at least February 2026, when Bloomberg's Mark Gurman reported that Apple was testing a deep red finish for the ‌iPhone 18 Pro‌ and ‌iPhone 18 Pro‌ Max. At the time, Gurman described the shade as a deep red, and separate reporting from a Chinese leaker later suggested the color was very likely to make the cut, partly because Android rivals were already prototyping the same shade.



The picture sharpened in April, when Macworld reported that the color would be called Dark Cherry and would be closer to wine than a brighter red, and considerably more muted than Cosmic Orange. The leaker known as "Instant Digital" subsequently corroborated that name, characterizing the shade as a combination of burgundy, coffee, and deep purple. "Instant Digital" has a good track record on Apple color leaks, having accurately predicted the yellow finish for the iPhone 14 and iPhone 14 Plus.

Macworld's reporting also identified the full four-color lineup, with internal Pantone codes said to be in use at Apple: Light Blue (Pantone 2121), described as resembling the current iPhone 17's Mist Blue; Dark Cherry (Pantone 6076); Dark Gray (Pantone 426C); and Silver (Pantone 427C), said to be similar to the current generation.

The latest images are significant because they mark the first time the rumored colors have been depicted in physical, real-world form rather than renders or supply chain descriptions. That said, dummy models are typically made from plastic or low-quality metals and are not finished to the same standard as production units, meaning the tone and saturation of each color could vary from what Apple ultimately ships. With that caveat, the dummies are consistent with the earlier rumors, suggesting that this will indeed likely be the final color palette of the device.

The ‌iPhone 18 Pro‌ and ‌iPhone 18 Pro‌ Max are expected to be announced in the fall alongside the first foldable iPhone.Related Roundup: iPhone 18 ProTag: Sonny Dickson
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iPhone 18 Pro's Camera Upgrade Will Cost Apple 50% More

The iPhone 18 Pro and ‌iPhone 18 Pro‌ Max's all-new variable aperture lens will cost Apple 50% more than the camera unit used in current models, according to supply chain analyst Ming-Chi Kuo.


Variable aperture has been one of the most persistent iPhone camera rumors of the past few years. Kuo first flagged the feature in late 2024, and it has since been corroborated by multiple reports and apparently entered production earlier this year.

Unlike the fixed f/1.78 aperture found on every iPhone Pro from the 14 Pro through to the 17 Pro, a variable aperture will physically adjust the size of the lens opening to control how much light reaches the sensor, offering better exposure control and greater flexibility over depth of field.

Kuo said that the component has an average selling price roughly 50% higher than the seven-element plastic lens Apple currently uses in the iPhone 17 Pro's main camera. Sunny Optical set to supply Apple between 40 and 50% of orders

Sunny Optical has also become a new compact camera module (CCM) supplier for Apple, initially producing the camera for the MacBook Neo. ‌MacBook Neo‌ shipments have come in significantly better than expected, with Kuo doubling his 2026 forecast from 5 million to 10 million units, a notable upward revision as the entry-level Mac has materially exceeded early expectations.

Looking further ahead, the 2028 iPhone's ultra wide camera module is expected to move away from flip-chip packaging in favor of an improved COB (chip-on-board) design, with Sunny Optical well positioned to become a supplier at that point. A COB ultra-wide module could be thinner or smaller, leaving more room for other components, or simply deliver better image quality from the same physical footprint.

Beyond Apple, Kuo says Sunny Optical has secured component orders for two OpenAI devices, including a smartphone and a pocket or mobile device.

The ‌iPhone 18 Pro‌ and ‌iPhone 18 Pro‌ Max are expected to launch in the fall alongside the first foldable iPhone. Related Roundup: iPhone 18 ProTags: 20th-Anniversary iPhone, Ming-Chi Kuo
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Notepad++ vulnerabilities could enable arbitrary code execution on Windows systems

Two arbitrary code execution vulnerabilities in Notepad++ let local attackers run commands of their choice on Windows machines by tampering with the editor’s XML configuration files, with both flaws rated High at CVSS 7.8.
The flaws, tracked as CVE-2026-48778 and CVE-2026-48800, affect every version of the editor up to and including 8.9.6, Notepad++ said in a release note. However, the vulnerabilities were patched the same day in version 8.9.6.1, alongside a third lower-severity crash bug, CVE-2026-48770, Notepad ++ author Dun Ho wrote in the release note.
The two code execution flaws share a single design weakness. Notepad++ stores user choices, such as the path to the command-line interpreter and the list of user-defined commands, inside XML files in the user’s profile directory. The editor reads those values and passes them to the operating system as commands without checking what they contain, according to a GitHub Security Advisory on Notepad++ published on May 27.
Anyone who can write to the XML files can decide what the editor executes, the advisory said.
A backdoor that hides in the Run menu
The more concerning of the two flaws, CVE-2026-48800, targets the file that holds user-defined Run menu entries.
Notepad++ reads its user-defined commands from a file called shortcuts.xml and accepts whatever it finds there without validation, the advisory said. An attacker who can write to that file can add an entry that launches an arbitrary executable when the user clicks it in the Run menu.
“The injected commands appear with legitimate-looking names in the Run menu, making them appear as normal user-created shortcuts,” the advisory said. “This creates a viable persistence mechanism, as the injected commands survive reboots.”
The proof of concept Ho published shows an injected entry named “System Update Check” that launches Windows Calculator. Italian researcher Michele Piccinni reported the flaw.
A second path through the command-line interpreter
The second code execution bug, CVE-2026-48778, targets a different file. Notepad++ stores the path to its command-line interpreter in a file called config.xml and accepts whatever value it finds there as the program to launch when the user opens a folder in cmd, a separate advisory said. The interpreter path is stored “without any validation, whitelist, or digital signature check,” the advisory said. An attacker who edits config.xml can substitute any executable for the real Windows command prompt. Piccinni reported this one as well.
Neither flaw lets an attacker reach the XML files on their own, the advisories said. Both assume the attacker already has the ability to write to the user’s AppData directory or can trick the user into running Notepad++ against a poisoned settings folder, whether through local malware, a malicious Windows shortcut, cloud-synced settings, or a social-engineered archive extraction.
The third patched flaw, CVE-2026-48770, follows the same theme of unchecked input but stops short of code execution. A local process in the same Windows session can send the editor a malformed inter-process message that reliably crashes it, the advisory added. The bug carries a CVSS score of 5.0.
A question mark over MSI patch delivery
Notepad++ users can download the patched 8.9.6.1 binaries from the project’s download page, which offers both the EXE installer and an MSI installer for enterprise IT deployment that Ho added in November 2025.
The MSI followed sustained enterprise demand that intensified after a Chinese state-sponsored group hijacked the editor’s update infrastructure for six months in 2025 and after Ho hardened the update mechanism in February with cryptographic integrity checks.
The advisories recommended that users monitor the AppData folder on machines running Notepad++ for unexpected changes to shortcuts.xml and config.xml. The persistence of both flaws leaves no trace at the installation directory and no change to the Notepad++ binary itself, the advisories said, which means endpoint tools that look only at executables will miss it. Ho published no indicators of compromise.
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The Gentlemen are coming for your files, and then your network

Ransomware operators have spent years refining the art of locking files. Now, some are working harder to get those lockers to every reachable system first.
Microsoft’s recent warning of the Gentlemen ransomware revealed its operators using a self-propagating Go-based encryptor capable of moving laterally through compromised environments and deploying itself across additional systems.
“Modern ransomware is no longer just about encrypting files,” said Paul Reid, vice president of Adversary Research at AttackIQ. “The bigger risk is how quickly a single compromised machine can become a broader business disruption.”
In a technical breakdown of its operations, Microsoft said the Gentlemen Ransomware was first observed in mid-2025 and remains highly active through 2026, impacting organizations across education, transportation, healthcare, and financial industries in North America, South America, Europe, Africa, and Asia.
Gentlemen began as a “closed ransomware,” turned into a ransomware-as-a-service (RaaS) offering in September 2025, and eventually partnered up with BreachForums to pick up affiliates, including pen-testers and initial access brokers, from the popular cybercriminal marketplace.
Built to move before it encrypts
Microsoft’s analysis specifically focused on the ransomware’s ability to propagate through a network without relying entirely on manual operator intervention.
The encryptor, written in Go, includes functionality designed to identify additional systems, authenticate using harvested credentials, and copy itself to remote machines over Server Message Block (SMB). Once deployed, it can execute remotely and continue spreading, creating a chain infection inside compromised environments.
According to Microsoft, the malware leverages legitimate administrative tools and Windows functionality to facilitate movement while reducing the need for attackers to remain actively engaged through the operation.
“The ransomware operator can control The Gentlemen encryptor through command-line arguments,” Microsoft said. “A password is required for execution, and optional arguments allow the operator to specify encryption scope, speed, lateral movement, and post-encryption behaviors.”
One of the command line arguments,“–full,” launches separate processes to encrypt local drives with SYSTEM privileges and network shares visible to the user, to maximize encryption coverage once the machine is compromised. Additionally, a “–spread” command is used for lateral propagation.
“Defenders should treat The Gentlemen as an attack-path problem, not just a patching or detection problem,” Reid said. “The priority is to understand where the ransomware could move, which controls would detect, contain, or disrupt it, and where gaps still exist before an incident occurs.”
Gentlemen performs a “password check” to validate the use of its RaaS by the affiliates, and blocks its usage from unwanted binary recovery or interception. “Before executing its primary functionality, the malware validates the –password argument against a hardcoded value embedded within the binary,” Microsoft noted. “For the sample analyzed in this blog, the expected password is ‘9VoAvR7G’.”
Detection windows are shrinking
Microsoft’s analysis highlights the defensive challenges posed by self-propagating ransomware. Once execution begins, the time available to detect, investigate, and contain malicious activity can shrink considerably as the malware spreads to additional systems.
“This is not the kind of threat where an organization can wait for a help desk ticket or a locked screen to realize something is wrong,” said John Joyner, Senior Director of Technology at Corsica Technologies. “Malware can move quickly through a network once it gets a foothold, which makes early detection the difference between a contained incident and a business-wide disruption.”
Microsoft emphasized the importance of monitoring lateral movement activity, credential abuse, remote execution attempts, and other behaviors associated with Gentlemen’s propagation rather than focusing solely on encryption events.
Additionally, it shared a list of indicators of compromise (IOCs) to support detection efforts. For those who don’t catch it on time, the ransomware leaves a note. “Your network is locked by the Gentlemen,” a desktop wallpaper reads on the victim’s machines.
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Cybersecurity trends in SEC filings

In 2023, the Securities and Exchange Commission (SEC) required public companies to include a new section in their 10-K annual filings that is devoted to cybersecurity. This section is meant to address “cybersecurity risk management, strategy, governance and incidents.” I got curious as to what senior cybersecurity executives are conveying about their companies in these reports. I turned this into a research project that also gives me a reason to test out some AI techniques as well.
The article is broken into two sections: My findings regarding Section 1.C for the top 200 companies in the S&P, and the second being my methods used to include some AI tech.
10-K Section 1.C
Some really great analysis of Section 1.C has already been done to include a Harvard Law School study, a PWC study and an International Journal of Accounting Information Systems paper. These were great reads, but both were done over a year ago with the first batch of filings. Also, with the Harvard Law study, they only looked at the top 100 companies. I wanted to see if I could reproduce some of the analysis using this year’s filings, as well as ask some of my own questions, like whether there are any major changes between 2024 and 2025.
Companies are required to disclose governance regarding cybersecurity risks. Key requirements include describing board oversight of cyber risks, the committee responsible and management’s role in assessing and managing material cybersecurity threats. Years of experience are often included.
Similar to the Harvard study, I’ll look at who holds the senior cybersecurity role and their level of experience, who they report to, what part of the board oversees cybersecurity and standards that they are using. Not every company included all these pieces of information, but the bulk of them did. I’ll also look at overall trends between 2024 and 2025.
CISO role top for cybersecurity
The chief information security officer (CISO) continues to be the principal position responsible for cybersecurity, with over 70% of companies reporting CISO as the role responsible for cybersecurity.  Numbers for CISO slightly increased from 2024 to 2025, going from 137 to 142.  A distant second and third are CIO and CSO.  The average years of experience for the role is about 23 years (standard deviation 6 years, 140 companies reported).
CIO remains top senior in a varied field
Chief information officer remains the top person that the cybersecurity official reports to and remained stable between 2024 and 2025 (~49 vs ~48).  This is consistent with surveys and other reporting that CIO is the most frequent.  I agree with another CSO article that having the position under the CIO is sub-optimal and both inserts conflicts of interest as well as downplays the importance of cybersecurity at the enterprise level.  Not saying it can’t work, but there are likely better arrangements.  No clear alternative has appeared in either 2024 or 2025 data (see the chart below), and the small relative numbers indicate there is a lot of variety in who the CISO reports to.  The CEO, CFO and CTO were other common reporting positions, but none were a clear second.  It is also worth noting that for over 50 companies, it wasn’t clear from the 10-K write-ups who the reporting position was.
Derek Dye
Board oversight
Within the Company’s board, the Audit Committee is by far the most common group responsible for cybersecurity, representing 60% of companies.  This jumps to about 70% (138 companies), If you include all the variations of Audit to include Audit & Risk, Audit & Finance, etc.  Overall, audit numbers remained steady between 2024 and 2025.  Distant second and third were the Risk Committee and Board of Directors broadly.
NIST CSF for the win
National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF) is the most referenced cybersecurity standard, increasing between 2024 and 2025(113 vs 118). The most common other standard being ISO 27001, which also grew between 2024 and 2025 (49 vs 55).  Interestingly, System and Organization Controls (SOC) was only mentioned by 17 companies.  I find this seemingly low, given the importance of SOC reporting in large public sector companies.
Overall trends
Other interesting observations were what companies listed as their broad efforts as well as disclosures of incidents.
Third-party and supply chain risk management. Acknowledging that external partners and suppliers represent a massive attack vector, multiple companies have instituted rigorous third-party risk management (TPRM) programs. These programs mandate pre-engagement security assessments, continuous monitoring and contractual requirements for vendors to maintain security standards and report breaches promptly.  Third-party cybersecurity programs are indispensable in an increasingly interconnected economy and increasing reliance on external tools and services for company processes. Proactive testing and incident preparedness. Companies are moving past passive defense into proactive and simulated testing. This includes regular penetration testing, vulnerability scanning and engaging independent external auditors or consultants to assess program maturity and test controls. Furthermore, practically all companies maintain formal Incident Response Plans (IRPs) and conduct regular “tabletop exercises” to simulate cyberattacks, ensuring that management, legal and operational teams are prepared to respond to and recover from real-world crises. The devil is in the details on this one. The 10-K is not meant as a detailed technical rundown of company methods, so while it’s good to see, it’s mostly boilerplate language. Human-centric security defenses. Recognizing that human error is a primary vulnerability, mandatory, enterprise-wide cybersecurity awareness training is a standard requirement. These training programs are frequently supplemented with regular, simulated phishing campaigns to test employee vigilance and provide immediate, targeted feedback or remedial training.  This training will need to adapt to the growing sophistication of AI-enabled deep fakes. Consistent disclosure of “No Material Impact” despite ongoing threats. A ubiquitous trend across the filings is the acknowledgment that while the companies face continuous, sophisticated and evolving cyberattacks, they have not experienced any incidents that have had a material adverse effect on their business strategy, results of operations or financial condition to date. I find this interesting, especially with the Critical Infrastructure//telecoms coming under repeated VOLT/SALT TYPHOON compromises as well as other attacks. Many companies also disclose that they rely on cyber liability insurance to mitigate financial exposure, though they frequently note it may not cover all potential losses.  I’ll be doing further research here as there are likely more interesting findings between material impacts, news reporting and formal disclosures. Artificial intelligence. AI was cited by over 50 companies and is increasingly referenced as a double-edged sword for cybersecurity.  Companies are leveraging AI and machine learning to automate threat detection and sort through vast amounts of security data. However, several acknowledged that AI empowers threat actors to execute more sophisticated, high-velocity attacks (e.g. deepfakes, advanced phishing).  A further seven companies mentioned the concern of AI and intellectual property disclosures with Prudential and Capital One having the most explicit language on this risk. Part 2: Data gathering and analysis
This was a very iterative process that increased in complexity as I went through the process and also due to the increased need for accuracy.  I used several coding methods and AI tools to do this analysis. At first, I tried to use the big models to do all the work for me, but that quickly failed when they didn’t want to do that level of work! It also became apparent that getting the 10-K filings would take more work than just asking an AI agent.
Enter some vibe coding. I was raised on C, Java and BASH scripting and have avoided using Python until now. Nothing against Python, I just haven’t needed to, and laziness with going with what you already know has won out before. So, this proved a nice additional challenge. Using the datamule Python module and some vibing, I managed to download all the recent 10-Ks for the top 200 companies onto my local machine. From there, I extracted the 1.C sections into a separate file using another Python script. This caused a bit of an issue as there were some differences (~5%) in filings that used a different format, or the cybersecurity write-up was in a different section of the 10-K.  About 15 companies put them in the Risk section or elsewhere.  I used a second Python script that leveraged the command-line version of Gemini (gemini-cli) to pull this information out.
I then created a database in postgres that would store some of the key findings and allow for some further analysis. To get the data into there, I created a Python script that would run each of the 1.C files through Gemini and Claude using Python API calls.
The use of Gemini API and Anthropic API was the new part that I really wanted to test out, and it proved very interesting. LLMs really shine for condensing and summarizing large texts for meaning. The alternative would be very complex and manually written regular expressions. Using Gemini API and Anthropic API, it took the below prompt and produced a string that I could then plug into the SQL command. Very cool seeing this work. (**Note: I was also thinking of how to do prompt injection, data poisoning and the like with this, but the dataset was small and controlled and this isn’t production code!).
Derek Dye
As a verification step, I then wrote another script that found all database entry differences between Gemini and Claude answers and ran the original 1.C section through Gemini again and told it to pick which answer was better.  This changed about 10-30% of the entries, depending on the field.  Additional analysis was done in Google Sheets and Google NotebookLM.
With this, I created a basic AI-enabled workflow. It wasn’t agentic, but that would be interesting to create an automated version of this.  This project showed some of the productivity potential of AI by allowing me to do very detailed research in about 15-20 hours of work, which would have taken at least twice as long by hand.  It also highlighted the continued issue with accuracy where accuracy is needed.  The bulk of the 15-20 hours was spent doing verification and refinement to make sure the AI answers were correct.
The total cost in tokens for development, debugging, and running was around $15, not expensive, but not something I’d likely develop for every project I have. The bulk of that cost came with the refinement and the addition of additional verification checks to ensure the data was correct.  Next projects might try to do this on my local computer using a local LLM like llama3 using ollama or maybe an agent that allows queries to the dataset this project created.
GEMINI_PROMPT = """ Analyze this SEC 10-K document and extract the following cybersecurity information. Return the response strictly as a JSON object with these exact keys: { "senior_cyber": "Name or title of the senior person responsible for cybersecurity. Provide a one word response either CISO, CTO, CSO, CIO, or position title.", "report_to": "Title or name of who the senior cybersecurity person reports to. one word response either CEO, CTO, CSO, CIO, position title, or unknown. ", "board": "The board committee overseeing cybersecurity Provide a 1-3 word answer. ", "standards": "The cybersecurity standards/frameworks used use provide 5-7 word answer. If unknown, state unknown. use acronyms if available (e.g., NIST, NIST CSF, NIST CSF 2.0, ISO 27001)", "years_of_experience": integer representing years of experience (use 0 if unknown) } """ MODEL_ID = 'gemini-2.5-flash' —---- # 6. Update PostgreSQL upsert_query = """ INSERT INTO company_cyber_filings_v1_4 (ticker, filing_date, senior_cyber, reports_to, board, st andards, years_of_experience) VALUES (%s, %s, %s,%s,%s,%s,%s) ON CONFLICT (ticker, filing_date) DO UPDATE SET senior_cyber = EXCLUDED.senior_cyber, reports_to = EXCLUDED.reports_to, board = EXCLUDED.board, standards = EXCLUDED.standards, years_of_experience = EXCLUDED.years_of_experience; """ # 4. Upload file formatted_date = f"{year}-{month}-{day}" # Formatted for standard SQL DATE cursor.execute(upsert_query, ( prefix, formatted_date, gemini_data.get("senior_cyber"), gemini_data.get("report_to"), gemini_data.get("board"), gemini_data.get("standards"), gemini_data.get("years_of_experience") )) cursor.execute(upsert_query, (prefix,f"{year}{month}{day}")) conn.commit() print(f" -> Saved to database.") This article is published as part of the Foundry Expert Contributor Network.
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GDPR set the tone for regulatory action — and the AI fine pushback to come

Big tech firms continue to push back against fines levied for alleged violations of European data protection law, in what could be a harbinger for AI regulations to come.
While lawyers and experts quizzed by CSO broadly argue that big tech firms contesting data protection rules isn’t a particular cause for concern, the more widespread introduction of AI technologies is a far greater data protection challenge on the horizon.
The EU’s General Data Protection Regulation (GDPR) came into force eight years ago this week. Over those eight years, European regulators announced an estimated €7.1 billion in GDPR fines but nearly 40%, around €2.8 billion, has either already been annulled or is under active legal challenge, according to analysis by insurance brokerage Alliance Risk.
Fines that have already been annulled include one against Amazon at €746 million (Luxembourg, March 2026) and another versus OpenAI at €15 million (Italy, March 2026). Those under active appeal include three fines against Meta (€1.2 billion, €265 million, and €91 million) and one against TikTok (€530 million).
Alliance Risk used CMS Law GDPR Enforcement Tracker as its primary source for information on GDPR enforcement, cross-referenced against IAPP enforcement data and trackers from Kiteworks and UniConsent. Data on annulments came from reported court decisions.
GDPR established a benchmark for breach notification
According to Alliance Risk, GDPR successfully laid the foundation for data protection law globally — particularly by first establishing the 72-hour breach notification standard.
This three-day notification rule is law in six jurisdictions — EU, UK, Thailand, Kenya, Nigeria, and South Korea — and influential elsewhere. For example, the US CIRCIA rule for critical infrastructure, which is pending final rule publication this month, is due to apply the 72-hour standard.
By comparison, HIPAA gives US healthcare organisations 60 days as a breach notification deadline. The SEC gives public companies four business days but only after they’ve internally determined a breach is “material,” which adds its own delay.
Although the breach notification regulations established by GDPR have been a success, issues with the enforcement of rules remain.
“The framework has structural weaknesses that large companies have learned to exploit in court, and nearly 40% of announced fines reflect that,” according to Alliance Risk.
The EU’s AI Act reaches full application in August, and the European Commission is already proposing to reform GDPR through the Digital Omnibus. “The framework is being rewritten while it’s still being tested,” Alliance Risk concludes.
“The fact that around 40% of GDPR fines by value are under challenge isn’t necessarily a sign the system is broken,” Nick Phillips, an intellectual property lawyer at Edwin Coe LLP tells CSO. “Eight years in, the bigger fines were always going to end up in court, and the rulings that come out of those appeals are starting to give in-house teams something they’ve never really had before: practical guidance on what regulators can and can’t defend.”
Phillips argues that achieving compliance with GDPR has improved enterprise security maturity because of the 72-hour breach notification rule coupled with the obligation to record all breaches and to notify data subjects combined with the need to improve security controls even more than the threat of a fine for non-compliance.
“That breach notification regime has arguably been the single biggest factor in forcing organisations to put proper incident response in place, get forensics providers on retainer, and start reporting breaches up to the board,” Phillips says. “A lot of that simply wasn’t happening before 2018, and it’s the part of GDPR that’s done the most work.”
Marco Eggerling, LL.M, security and trust officer EMEA and Asia, at robotic process automation vendor UiPath, says it would be a “mistake to read these annulments as courts clearing big tech.”
“In the Amazon case, the Luxembourg court upheld the substance of the violations and sent the matter back to the regulator,” Eggerling notes. “The fine fell because the authority skipped required steps, not because the conduct was found lawful.”
Eggerling adds: “The lesson for regulators is to build procedurally bulletproof decisions. The lesson for companies is that the underlying obligations have not moved an inch.”
Even within the EU there is a disparity in how regulations are understood and applied, making cross-border decisions about data and AI challenging.
“A lot of organisations lean towards the ‘lowest common denominator’ and adhere to the strictest governance and more conservative approaches in order to avoid the wrath of regulators,” says Caroline Carruthers, CEO and founder of global data consultancy Carruthers and Jackson.
The UK and EU apply stricter regulations than the US or China, so many organisations adhere to the stricter rules wherever they operate.
Due to their size and nature, “big tech” organisations tend to have a heightened appetite for risk and a desire to push the boundaries of regulations — and often a different relationship with the general public, whose data is the business model. “They have a vested interest in deregulation and so will naturally be the most likely to contest enforcement,” Carruthers notes.
Data regulations need to evolve with the advent of AI
For most organisations, the enforcement of GDPR has gotten to a place where it is broadly fit-for-purpose, according to Carruthers.
“When GDPR was first introduced, the guidance was unclear and inconsistent,” Carruthers explains. “It felt legally robust, but a lot of the data practitioners struggled to make it work. Even now, some businesses tell us that they are ‘paralysed’ a little by GDPR. They are highly fearful of data and the associated regulation, to the extent that they are unable to maximise — or even touch on — the potential power of data.”
However, as AI and data regulation evolves, there’s a need to account for how these tools are now being used.
The concern is that history may repeat itself as regulation looks to keep pace with technological change. “There is a risk that organisations get stuck in a mid-maturity plateau in which innovation is halted by complex and inconsistent interpretations of regulations,” Carruthers warns.
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CSOonline

Kimsuky Deploys HTTPSpy, Expands Arsenal with HelloDoor and VS Code Tunnels

The North Korean state-sponsored threat actor known as Kimsuky (aka Velvet Chollima) has been attributed to a fresh set of cyber attacks targeting South Korean military and corporate entities through March and April 2026. "Kimsuky employed a range of tailored social engineering tactics, such as spoofing security software installation pages and crafting a fake Webex meeting page that leveragedView the full article
Hacker News

IBM and Red Hat want to become the ‘security clearinghouse’ for open source applications in the enterprise

Open source code is everywhere in the enterprise; it’s estimated that upwards of 90% of Fortune 500 companies have it in their software supply chains. But open source code is notoriously rife with vulnerabilities, and identifying and patching those bugs can be an endless battle for security teams.
IBM and Red Hat are betting that a new initiative, Project Lightwell, can help accelerate this process.
Announced today, the project will commit $5 billion and 20,000 IBM and Red Hat engineers to build a new ‘enterprise clearinghouse’ to accelerate discovery and remediation of vulnerabilities in open source software. The companies say the clearinghouse will serve as an AI-powered  “security coordination layer,” giving enterprises the ability to integrate patches directly into their existing software supply chains.
Now in the design phase with a group of 11 financial partners, Project Lightwell will eventually be offered as a commercial subscription.
“The advancement in AI tools has broken the patching map, which is the ability to discover vulnerabilities in software without losing the speed of remediation,” Ashesh Badani, Red Hat SVP and CPO, told CSOonline. “Everyone’s running open source software, and the challenge is not being able to fix vulnerabilities quickly enough.”
Closing the remediation gap
Open source security issues have been well documented: Almost 50,000 common vulnerabilities and exposures (CVEs) were published in 2025, and Anthropic’s Project Glasswing, powered by its Mythos Preview model, found roughly 3,900 previously undiscovered high or critical severity vulnerabilities in open source software shortly after launch.
IBM is considered one of the broadest commercial open source ecosystems, using more than 62,000 packages and operating across Linux, Kubernetes, Kafka, Terraform, Java and other platforms, and providing lifecycle management, validation, and patching for elements within those environments.
The company says Project Lightwell will now apply those same engineering principles to broader AI frameworks, independent libraries, language toolchains, and data streaming platforms, to deliver validated fixes to open-source code already in use in enterprise environments. This can support remediation without disruption of stability, certification, or compliance.
No upgrades or access to source code are required; Project Lightwell will backport fixes to exact dependency versions that have already been tested and deployed. It operates on fundamental configuration manifests like pom.xml so code remains in controlled enterprise environments when patched artifacts are rolled out. Initial focus will be on Java/Maven, but the project will eventually expand to PyPI, npm, Go, and others.
Enterprises will have the ability to share sensitive vulnerabilities under embargo through a “secure intermediary model” and receive validated patches spanning Red Hat platforms and independent community code. They will also be able to deliver fixes across dependency chains; report and address issues across active production environments; and share fixes upstream so the wider open-source community can incorporate them.
“We want to make sure that whatever fixes we provide to the enterprises through the clearinghouse also find their way back into the open source community that developed [the code],” Badani explained. For instance, if a piece of Python code was patched, the fix should be quickly delivered back to the Python community. With Project Lightwell, that process can be achieved through a “secure map.”
Using advanced AI, and working with leading open source contributors, IBM and Red Hat engineers will focus on connecting upstream and downstream environments so fixes are enterprise-ready. They will also develop patches and perform “high volume” vulnerability review and triage, and dependency hardening.
The network of 20,000 engineers will come from IBM’s and Red Hat’s existing pools of talent, and the companies will augment those teams as needed, Badani explained. The companies will take advantage of foundation models coming out of frontier labs, as well as their own internally-built AI tools and frameworks. The $5 billion will be used to equip teams with AI tools and build out internal operational infrastructure.
Early Project Lightwell adopters include Bank of America, BNY, Citi, Goldman Sachs, JPMorganChase, Mastercard, Morgan Stanley, Royal Bank of Canada, State Street, Visa, and Wells Fargo. Following the initial design period, IBM and Red Hat will phase more customers onto Project Lightwell via a subscription model.
A call to action?
This type of initiative is “desperately needed” if enterprise is to save open source, noted David Shipley of Beauceron Security.
The days of trillions in wealth depending on volunteers “ended violently” with Mythos, he noted, and the bill has ultimately come due for open source. Enterprises will need to pay up, or lose it.
“If we don’t find a way to invest in open source, which will close a long-standing equity issue, the alternative is everyone building their own bespoke code using AI,” Shipley said. That would be “massively wasteful” from a compute and environmental perspective.
“I hope this drives others to act,” he said.
Keeping humans in the loop for an ongoing battle
Badani emphasized that, while AI is great at discovering security issues in open-source code, the patching process can still be cumbersome. Fixes have to be sent upstream, distributed to the open source community, then flow back to customers and users.
“Finding the bug is one thing,” said Badani. “The other is all the steps that it takes to actually go and remediate it. That extra amount of time is the gap that we’re trying to help close.”
Underscoring the severity of the problem, IBM and Red Hat have already had an “onslaught of incoming requests” since Project Lightwell was announced.
“This isn’t going to stop any time soon,” Badani said. “Even if we were to very successfully solve the initial set of challenges that come to us, this will be something that companies are going to need on an ongoing or recurring basis.”
And, while the narrative has focused on cutting human engineers in favor of AI, Project Lightwell is focused on the opposite: “We can address [the problem] with a mixture of AI tools and human knowledge and expertise,” Badani said. “Coupling the two gives you a better outcome than just using one or the other.”
This article originally appeared on InfoWorld.

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