It's FOSS reports: Asahi Linux is the reason you can run Linux on an Apple Silicon Mac today. For years, the project put most of its own code under the MIT license so that anyone, including the BSDs, could reuse it as needed... They have announced that Asahi is moving all of its repositories to the GNU General Public License version 2 or later. Going forward, all Asahi Linux code will be under strong copyleft licenses only. Asahi Linux compares their dilemma to the 1990s, when "permissively licenced projects were viewed by tech companies as free real estate to pilfer and exploit," adding that "Those who do not learn from history are doomed to repeat it... The malicious practices of the 90s and 2000s have reared their ugly heads once again, this time driven in large part by both the insatiable appetite of frontier AI labs for high-quality training data, and militant animosity toward the Free Software community." In short, they argue that for Asahi Linux, using the permissive MIT license "is no longer viable." Some time ago, we were made aware of multiple attempts to exploit our permissive licensing to hijack our work, with a direct view to replacing the project and aggressively monetising Linux on Apple Silicon. In some extreme cases, in-development feature code was being lifted straight out of our personal forks and published without attribution or lazily rewritten with LLM assistance to obfuscate its origin. The MIT licence explicitly allows this, and has no carveouts to protect against bad faith actors. As such, we made the difficult decision to begin the process of transitioning all of our repositories to the GNU General Public License version 2 or later. Going forward all Asahi Linux code will be licenced under strong copyleft licences only. It's FOSS explains that "Under the GPL, anyone who distributes a modified version has to release the source under the same license. You can't legally build a closed, paid Apple Silicon distro on top of GPL code. And if a product's source has to be public, the copying is easier to spot." And their article also points out that before AI, bad actors attempting code laundering "usually meant manually rewriting code line by line, renaming variables and stuff, and shuffling functions around until it no longer looked like the original. LLMs made it much more easy and cheaper of course." In case you did not know, Asahi Linux forbids AI generated code contribution... As far as I can tell, this is the first time a major open source project has moved to copyleft specifically because of AI-assisted code laundering. Projects have relicensed before, but usually to stop cloud providers from reselling their work as a service. This one is specifically about AI... The move has drawn criticism, particularly from the BSD side, which was one of the main reasons Asahi chose MIT in the first place. OpenBSD's Apple Silicon support relies on Asahi's m1n1 bootloader.

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"AlmaLinux OS charted a new path in 2023 when Red Hat restricted source code access to Red Hat Enterprise Linux," remembers Linux Magazine. "Rather than pursue 1:1 compatibility with RHEL like other Enterprise Linux alternatives, the AlmaLinux OS Foundation chose application binary interface (ABI) compatibility, which guarantees that applications and kernel modules that work on RHEL will work on AlmaLinux OS." No longer constrained by the 1:1 promise, AlmaLinux can offer new features, or add back deprecated ones, while still remaining a stable, drop-in replacement for RHEL... The release of AlmaLinux OS 10.2 in May 2026 reveals just how far AlmaLinux has come.... When RHEL 10 increased the x86-64-v2 architecture baseline to x86-64-v3, many older server platforms (and some new ones) were left in the lurch. These older servers couldn't support the modern CPU feature sets in x86-64-v3, including AVX2 CPU instructions. AlmaLinux recognized that many community members employed older hardware that relied on x86-64-v2. While x86-64-v3 is enabled by default in AlmaLinux OS 10 and AlmaLinux OS Kitten 10, an additional x86-64-v2 build is also available. The x86-64-v2 build works best for environments that rely on standard OS packages or that can rebuild any additional packages required by x86-64-v2. For users with older hardware, the additional x86-64-v2 build allows continued use of current hardware, saving money and keeping servers from the landfill. AlmaLinux OS 10 is actively supported through 2030 with security support through 2035, significantly extending the life of older hardware.... Many legacy applications and containerized workloads still run on i686, a 32-bit computer processor architecture belonging to the Intel x86 family, including software that only ships as i686, CI pipelines that require a specific 32-bit glibc, and seemingly normal looking containers that require a 32-bit environment. RHEL 10 completely removed 32-bit application support and multilib libraries after deprecating support in earlier releases... AlmaLinux OS 10.2 has added package repos and official container images for 32-bit x86 (but not a 32-bit kernel or installer)... NVIDIA GPUs, ubiquitous in modern computing, have been a pain point for Linux users. Getting the closed-source NVIDIA drivers has required extra steps and manual installation. When NVIDIA released an open source version of their graphics driver, AlmaLinux integrated the drivers, along with the associated CUDA components, into the AlmaLinux repository starting with AlmaLinux OS 9 and 10. AlmaLinux also signs the open source drivers, allowing secure boot with the NVIDIA drivers they ship. Unlike RHEL users, AlmaLinux users now have native support, allowing them to skip manual installation. AlmaLinux users can now enjoy a fully integrated NVIDIA experience. In addition, "Thanks to ABI compatibility, AlmaLinux can sometimes deliver patches more quickly than RHEL," the article points out. And it lists other points where AlmaLinux OS 10.2 diverges from RHEL 10: Re-enabling frame pointers disabled by default in RHEL and CentOS Stream to provide system-wide, real-time tracing and profiling to diagnose and optimize code. Enabling both kernel and userspace components that allow installation of AlmaLinux on a Btrfs filesystem Adding SPICE support dropped in RHEL 9, allowing users to interact with a virtual machine's desktop remotely instead of just from the host server. Restoring support for older storage and network drivers disabled upstream, including Adaptec, Dell PERC, HP, Mellanox, QLogic, Emulex, LSI, and Broadcom.

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Google said today it worked with industry partners to dismantle a botnet made up of more than 1.3 million Android TV set-top boxes that ship with malicious software which secretly rents the user’s Internet connection out to strangers and conducts advertising fraud. Experts involved in the takedown say the action is likely to sever the botnet’s access to more than 10 million additional compromised systems, including other Internet of Things (IoT) products such as Android-based healthcare devices and karaoke machines.

A report from the Google Threat Intelligence Group (GTIG) said Google has severed ties with Videostrong Technology Co., a TV box manufacturer in China that GTIG and other industry experts concluded was responsible for including malware that enrolled more than 1.3 million set-top boxes in a botnet dubbed “XCPE” (named after one of the malware’s core code components).

The report said the XCPE botnet loaded the user’s device with software that makes their Internet address available for resale by multiple “residential proxy” services, which allow customers to rent Internet addresses as a way to anonymize their web traffic or spoof their true location. As detailed in several stories here over the past year, residential proxy services are a major source of unwanted and often malicious traffic, including account takeover activity, advertising fraud, and mass content scraping for AI projects.

“Videostrong monetized the backdoor by selling access to criminal affiliates and loading those affiliates’ malware modules onto infected devices,” the GTIG post reads, noting that Google took legal action to take down the domains and servers Videostrong used to control the botnet and install additional malware. “A device manufacturer intentionally infecting their users with malware is an unacceptable breach of trust, and represents a serious risk to those users. Without users’ knowledge or consent, infected devices were offered as a distribution mechanism for a wide array of criminal actors.”

The report notes that while TV boxes made up the the majority of the botnet, the same XCPE malware was found in other Android-based IoT products, including telehealth devices, karaoke machines, and even digital signage systems.

Google said it requires all Android device makers to pass compatibility and security tests to ensure their products don’t harm users, but that Videostrong nevertheless found a way to load their malicious software immediately after a user boots up one of these devices for the first time (Google says it has since addressed that weakness).

The GTIG report calls out a number of residential proxy providers whose software development kits were routinely downloaded by devices infected by the XCPE botnet. Those include AppSalt, also known as Honeygain, which was developed by the Vilnius, Lithuania-based Oxylabs. In October 2026, Oxylabs and Honeygain were reorganized as independent platforms under an umbrella entity called Source, which received $130 million in funding from private equity firm Warburg Pincus that reportedly valued the business at $3.6 billion.

Another provider linked to the botnet was NetNut, a once sprawling residential proxy company operated by the publicly-traded Israeli company Alarum Technologies. In July 2026, the FBI worked with industry partners to seize hundreds of domains associated with NetNut, saying the company was responsible for operating a separate botnet called Popa that enslaved TV boxes as residential proxies. Other proxy providers named in the GTIG report include EarnFM, Traffmonetizer, Jaguar, LINKFOG, BMW/IPWeb and GaGaNode.

Google’s announcement credits a number of industry partners, including Synthient, a startup that tracks proxy providers. In January 2026, Synthient published research into the rapid spread of the Kimwolf botnet, which at the time was hammering networks worldwide with record-smashing DDoS attacks. Synthient documented how Kimwolf infected millions of Android set-top boxes virtually overnight, using a widespread vulnerability in these residential proxy services to compromise other Android-based devices inside the local networks of unsuspecting TV box users.

Synthient’s research caused dozens of residential proxy providers to patch their networks, which helped to fragment the Kimwolf botnet into much smaller pieces that made its incessant and disruptive DDoS attacks far easier for network operators to manage. It also attracted the attention of the Motion Picture Association (MPA), a trade group representing the major Hollywood studios, which partnered with Synthient to purchase and tear apart a number of popular Android TV set-top boxes — including some that were being infected by Kimwolf and other IoT botnets.

In a report published today, Synthient examined a popular, pre-infected TV box sold under the brand MECOOL by a publicly traded company in China called Skyworth Technology, concluding that MECOOL also bundled the malware from Videostrong Technology Co. and likewise installed AppSalt/Honeygain and other proxy components.

Synthient founder Ben Brundage told KrebsOnSecurity that Videostrong’s malware also distributed software for the proxy service Grass. In our November 2025 story, “Is Your Android TV Streaming Box Part of a Botnet?” we quoted a researcher from Censys who found the popular Superbox TV streaming device bundled Grass’s residential proxy software. We also quoted the Grass founder, who denied any affiliation with the TV box maker.

That November 2025 story noted that Superbox and similar Android TV set-top boxes gain new customers primarily by engaging influencers on Tiktok and other social networks who praise the product’s ability to offer hundreds of (pirated) pay TV streaming services for a one-time fee.

Likewise, Synthient’s report observes that the MECOOL set-top boxes exploded in popularity after the product was mentioned in 2023 by the Youtube channel Linus Tech Tips, which has an audience of nearly 17 million subscribers [Linus Tech Tips said in a statement shared with Synthient that it has since updated the video to explicitly frame the product as a scam and have removed all associated affiliate links].

The GTIG report estimates the XCPE botnet takedown should sever access to at least 10 million additional compromised devices, noting that the various proxy and ad fraud affiliates of Videostrong “each have diversified distribution channels for their modules beyond XCPE.”

Synthient’s Brundage echoed that conclusion, calling the 1.3 million number “highly conservative.”

“The scale of affected devices is likely way higher,” he said. “This is probably the largest operation against backdoored TV boxes that we’ve seen.”

Update, Nov. 5, 11:07 a.m. ET: In a statement shared post-publication, Oxylabs said that while its system is built so that a device can’t be used as a proxy exit node unless they receive a signal that it has consented, the developer faked these signals.

“We have been conducting regular audits of the applications provided by our partners,” the statement reads. “However, following this incident, we will improve the application and application’s build fingerprinting logic to better detect cases where the SDK is running on an unapproved build of an originally authorized application.”

The statement said Oxylabs has no record of any company called Videostrong Technology Co., but it did find between is suppliers the domain “com.abc[.]nexus,” mentioned in the GTIG report as an external malware loader.

“As soon as we saw that, we suspended the relationship and sent them a formal legal inquiry,” the statement reads. “This supplier accounts for less than 1% of our IP address pool. We had a signed contract with them, they assured our QA team that users had given consent, and we saw the consent flow ourselves.”

Further reading:
Plume Security Labs white paper on the XCPE botnet (PDF)
Black Lotus Labs writeup on LinkedIn
TrendAI’s analysis
Synthient’s list of affected devices, broken down by model number

Cisco open sourced Project Quarry, a rule language for detecting AI agent attacks that only emerge across sequences of benign actions.

The post Cisco Open Sources Quarry to Catch AI Agent Attacks Hidden in Harmless Steps appeared first on SecurityWeek.

Even OpenAI is admitting there is a lot of AI slop in the security space. 

An executive for the frontier AI company said Tuesday that AI-generated vulnerability reports are overwhelming open-source maintainers, leading some to stop accepting submissions as the workload exceeds their capacity to review them.

Michael Aiello, OpenAI’s cyber executive in residence, described the problem during a question-and-answer session Tuesday at the DataTribe Challenge in Washington, D.C., when he was asked how he viewed OpenAI’s Patch the Planet program versus open-source maintainers banning AI-based submissions because of concerns about quality and clarity. 

“It’s an ecosystem problem where that [maintainer] is just not going to respond to hundreds and hundreds of AI-submitted vulnerabilities,” Aiello said. 

He contrasted the volume against his company’s own program, which partners with security research organizations to identify vulnerabilities, develop fixes and help bring them into the affected projects. According to the company’s website, 661 patches have been developed and tested, with 458 of them accepted by maintainers for inclusion in the projects they oversee. 

However, Aiello said those involved with Daybreak often coordinate with maintainers, as opposed to just sending a message into GitHub and hoping for the best. 

He further described the broader burden on maintainers, who are often tasked with running through hundreds of AI-generated reports to determine validity, the security implications and evaluate the proposed fix.

“In most cases, they’re likely real bugs of some degree,” he said. “It’s, ‘Are they critical bugs?’ And are they bugs worth the time of the maintainers?…The result is they’ve just stopped taking” submissions.

Aiello further laid out advice for those that do create vulnerability disclosures for open-source maintainers, including more work in establishing the quality of the finding, writing an understandable report and allocating resources to coordinate disclosure once a report is filed.

“Make sure they’re really high quality, make sure it’s consumable and readable by a single person, and then spend your own resources coordinating vulnerability disclosure,” he said. “You need to now coordinate the vulnerability disclosure across the whole ecosystem, which is itself another project.”

Aiello used OpenAI’s work on the recent PHP vulnerabilities as an example of that coordination. He said a partner research organization discovered the flaw, and OpenAI worked with the PHP Foundation to validate it, develop a fix and coordinate its release.

“You have to work with response partners, and you have to work with the operators to actually issue the patch,” he said.

The process involved coordinating with a broad group of organizations, extending the work from technical review through patch release. Aiello acknowledged difficulties in carrying out the entire process.

“Now, did it go perfectly? No,” he said. “We’re learning as we do this over time.”

The post Before you file that AI bug report, OpenAI has some advice appeared first on CyberScoop.

Alarm bells ratcheted up yet again about an AI-discovered vulnerability that could — if exploited in the wild — destabilize vast stretches of the internet. 

The critical defect — CVE-2026-111497 — affects the Google-developed Brotli decompression library that’s bundled into many components, applications and cloud services riding on open-source software. The buffer overflow vulnerability, which OpenAI Codex Security discovered and Node.js disclosed in a Oct. 21 security advisory, can trigger system crashes and remote code execution. 

Researchers haven’t observed any active exploitation of the defect in real networks, but concerns are mounting. Multiple vendors and software packages, including Microsoft and Python, that rely on vulnerable versions of Brotli have rushed emergency patches out to address the risk of exploitation in their respective systems.

Wiz researchers warned that “91% of cloud environments have resources vulnerable to CVE-2026-111497 and 82% of cloud environments have publicly exposed Node.js instances.”

The attack surface is “extremely broad and vast, but much of the impact of exploitation depends entirely on its implementation and how it is used within that piece of software,” said Jake Knott, head of threat intelligence at watchTowr.

Christiaan Beek, vice president at Rapid7 Intelligence, said the Brotli vulnerability is especially concerning because it’s effectively hiding out in the open, as are many open-source software libraries.

“Most businesses have no idea how deeply components like Brotli are embedded in their technology. A vulnerability in a compression library sounds almost boring until you realize it could affect applications your business depends on every day,” he said. “That’s the uncomfortable reality of modern software: a small component can create a very large business risk.”

The defect, dubbed “Node2Shell” or “Broccoli2Shell,” is the latest in a series of vulnerabilities that have provoked an emergency response. Defenders and researchers were on heightened alert last week over a pair of critical PHP vulnerabilities dubbed “PHP2Shell” that also have yet to be exploited in the wild as of Monday afternoon.

These cyclical and increasingly recurring so-called emergencies are putting organizations and defenders at a significant disadvantage. 

“After being part of numerous channels and message threads full of defenders trying to discern what’s real and what’s BS over the past several months — and especially the past week — I can say unequivocally that the human cost of these events is real, pronounced, and will have downstream impacts far beyond what the community can immediately predict,” Caitlin Condon, vice president of research at VulnCheck, told CyberScoop.

“None of this — high-profile vulnerability disclosures, the overblown exploitability assumptions, the community arguments, the race to weaponize — is new,” she added. 

“What’s changed now is that AI is pumping out complex — and often not clearly explained — finds at a much higher rate, exponentially increasing noise levels and making it much more difficult both to debunk overhyped vulnerability discoveries and to determine which vulnerabilities are broadly exploitable in real-world production configurations.” 

Researchers acknowledge the additional stress defenders and organizations are in because of this always-in-emergency mode.

“We’ve reached a point where everything seems to be an emergency. And when everything is critical, businesses risk losing sight of what actually matters,” Beek said. “The real cost isn’t just patching. It’s the constant disruption to operations, the diversion of resources, and the difficult decisions about which business systems deserve immediate attention.”

Experts also consider this the new normal as more high-impact vulnerabilities with far-reaching potential consequences are found. 

“We suspect in the very near future, defenders may lose the ability to tell what is a true emergency versus a loud and prevalent vulnerability in a ubiquitous product or library,” Knott said. 

Experts advise organizations to focus on their specific risk, weighing the Brotli vulnerability’s potential impact in their environment. 

“Attackers don’t care about CVSS scores,” Beek said. “They care about what they can compromise, disrupt, or monetize and that’s exactly how we should prioritize our defenses.”

The post Experts temper emergency-level alarms over critical Brotli defect appeared first on CyberScoop.

OSS Scanner sends unreviewed, model-generated vulnerability reports to open source maintainers that opt in.

The post Anthropic Fast-Tracks AI Bug Reports to OSS Maintainers, Taps 11 Firms for OT Security appeared first on SecurityWeek.

Anthropic announced a new program Thursday that will combine its AI tools with outside cybersecurity companies to find and fix cybersecurity vulnerabilities in critical infrastructure and open-source software.

Advertised as part of a new, “long-term commitment” to cybersecurity, the critical infrastructure defense program will pair Claude models, Anthropic engineers and threat research with the expertise of cybersecurity companies, including Accenture, Booz Allen, CrowdStrike, Deloitte, Dragos, Hitachi, Insane Cyber, Nozomi Networks, Palo Alto Networks, PwC, and Rockwell Automation.

“Defenders of critical infrastructure and the [open-source software] community have decades of security experience but have faced severe resource shortages that are exacerbated by this moment,” Anthropic wrote in a blog post. “We are recognizing their expertise in these domains and offering our support.”

Anthropic said it has offered frontier models and technical support to more than half the states in the U.S., as well as large operators of critical infrastructure to scan and patch code or assist in incident response and red teaming activities.

“Critical infrastructure is hard to defend in many ways that AI cannot fix, but we believe that frontier models can help find and repair weaknesses before those weaknesses are used to cut off power or make water unsafe, the company wrote. “Our first step is to work with a small cohort of providers to learn which strategies are most effective and practical.”

The company said it has engaged in similar work with maintainers of large open-source software projects, and some organizations asked for everything the model had found in their software, even unreviewed findings.

That spurred Anthropic to create a new opt-in scanning service for open source software, where organizations can get free, periodic scans of their projects with a proof of concept, explanation and suggested patching options. Anthropic noted that under this program, organizations will receive their reports faster but they may contain inaccuracies.

The long-term goal is to automate most triage and patching and develop new security architectures and coding standards.

As AI models have developed increasingly powerful cybersecurity capabilities, such as finding and exploiting known vulnerabilities, frontier AI companies have worried that bad actors could gain access to the tools faster than legitimate organizations. Anthropic and OpenAI have since both started programs funneling their tech to businesses and governments, free of charge, for defensive cybersecurity.

The post Anthropic rolls out program for ‘long-term commitment’ to secure critical infrastructure, open source software appeared first on CyberScoop.

- In 2022 Altair Engineering open sourced "OpenRadioss", an industry-leading finite element solver under the GNU AGPL License, reports Phoronix. - Siemens acquired Altair Engineering last year - Siemens closes access to OpenRadioss. "Siemens didn't just end the project but they shutdown the GitHub repository that hosted the open-source code and removed other resources that had built around it," writes Phoronix, before announcing a new open-source community fork: Radioss has been an industry-leading solver for analyzing crashes, blasts, and other purposes. Altair creating OpenRadioss was a huge milestone for the technical computing industry. It also had other effects like for myself in finally being able to benchmark with this (Open)Radioss code for CPU performance testing compared to the expensive licensing on Radioss itself... Siemens argues the OpenRadioss closure is for consolidating efforts: "After four years of successful community-driven research, Siemens is entering a new chapter for the Radioss solver. To accelerate innovation and provide the robust support our global users require for production-grade simulation, we are consolidating our efforts." It was Brian Clemens. founder/VP Rocky Linux and the Rocky Enterprise Software Foundation who launched the community fork of OpenRadioss — to be called OpenCourant. Its web site announces they'll be "carrying forward the OpenRadioss code base under the GNU AGPL, in the open, where it belongs." Phoronix writes in a follow-up: OpenCourant is based on the last publicly known open-source OpenRadioss snapshot before Siemens shut it down and removed access to the Git repository... OpenCourant continues with the OpenRadioss GNI AGPLv3 licensing and will be run as part of the Rocky Enterprise Software Foundation. They have run into a small issue though with part of OpenRadioss consisting of binary dependencies and not having the very latest versions prior to Siemens' removal of the repository. Details on that via this discussion thread for anyone that happens to have recent OpenRadioss binaries. In other open source news, Kagi is ending their development work on the Orion Browser for Linux and Windows, reports the site Linuxiac — but Kagi "plans to open-source both versions so other developers or organizations can keep them going."

Read more of this story at Slashdot.

Google has temporarily stopped accepting product vulnerability reports through its Open Source Software Vulnerability Reward Program (OSS VRP).

The post Google Narrows Open Source Bug Bounty Amid Wave of Invalid Automated Reports appeared first on SecurityWeek.

Long-time Slashdot reader Andreas(R) announces Northstar, a "minimalist" web browser written entirely from scratch in C — now available in version 1.0.11: One window, one page, one process — about 160,000 lines of original C, small enough for one person to read and audit end-to-end. It carries no upstream browser engine: no Gecko, no WebKit, no Blink. It is free software under the GNU General Public License, version 3 or later, and it runs on Linux, macOS and Windows... Compliance is not inherited from an upstream engine; it is measured, section by section, against the specification text... Private by construction. No telemetry, no update pings, and no AI assistant or AI-style web APIs. Safe browsing checks a top-level navigation's host against a local SHA-256 blocklist before it is fetched — the check never leaves your machine. Cookies, storage and the cache are partitioned by origin. It's logo reminds me of the old Netscape logo. (And I love the "Best viewed in..." GIF at the upper-right of its web site.)

Read more of this story at Slashdot.

"F-Droid, a third-party app repository that only distributes free and open-source software packages for Google's Android mobile platform, on Thursday announced version 2.0 of its Android app," reports The Register. Though they also note "a big banner across the top of the F-Droid site" pointing to a site describing pending changes from Google that threaten the future of F-Droid... [D]evelopers who want their apps broadly distributed outside the official Google Play Store will need to register with Google and verify their identities. Google's Full Distribution option includes paying a one-time $25 fee, handing over a copy of a government-issued ID to verify one's identity, and conforming to Google's terms of service. Google also offers a free Limited Distribution option that doesn't require government ID verification but restricts distribution to 20 authorized devices, while apps from unverified developers can still be installed by users who enable Android's advanced installation flow. The potential death warrant hanging over its head hasn't stopped the F-Droid team from rolling out a bunch of new features for an app it says it intends to keep working on for years to come... The team also credited the EU's many Digital Markets Act decisions against Google for making the installation experience smoother for users. F-Droid can now use a unified installation service for its apps thanks to the availability of a pre-approval API that allows users to approve an installation when they request it, rather than waiting until the app has finished downloading. That, said the F-Droid team, "brings the F-Droid install experience on official Android devices much closer to what the built-in app store can provide." Additionally, F-Droid 2.0 can fetch and install app updates automatically, which it now does by default. All of those changes, however, won't matter much if Google pushes ahead undeterred with its plans to force registration onto non-Play Store developers. F-Droid's announcement on Thursday makes it seem that the team isn't going to go quietly. F-Droid has gone 10 years without a major update, notes Ars Technica — and spent over a year developing F-Droid 2.0: The new F-Droid client was redesigned from scratch in Kotlin Compose, which is the standard for modern Android apps. This makes the store much more responsive, and there's optional support for Android's Material theming. The interface has also been cleaned up considerably, making the most important functions easier to access and hiding some others in overflow menus... Unlike the Play Store, F-Droid doesn't track your taps and installs to push ads and suggestions — it helps you find things and gets out of the way. F-Droid now includes a huge number of categories, drilling down to specialized niches like firewalls, password managers, and VPNs. You can see all these groups in the search tab. There are also higher-level categories listed on the main Discover page. When searching for apps, F-Droid will now be able to return results based on app descriptions rather than just names. "One of the goals of the rewrite was to lower the barrier for new contributors," F-Droid said in their announcement. "We are excited to begin rolling out F-Droid 2.0 to users over the coming weeks after 14 test releases." (And if you want the 2.0 release right now, it's available on the versions page.)

Read more of this story at Slashdot.

Targeting students and beginners, Adafruit released "CircuitPython" in 2017 (as a derivative of the MicroPython microcontroller-optimized programming language). Now Adafruit managing director Phillip Torrone (also long-time Slashdot reader ptorrone) brings this update: Adafruit has published CircuitPython Turbo, a workflow that compiles selected Python functions into native machine code on a computer, then loads them onto compatible microcontroller boards. It builds on MicroPython's Native and Viper emitters. In a documented Metro RP2040 fixed-point Mandelbrot test, Viper cut computation time from 8.335 seconds to 0.423 seconds, a 19.71x speedup over bytecode. The rest of the application stays in Python. The guide includes benchmarks, source code and hardware demos. The speedup is for the measured computation, not the whole application. "Turbo support is now included in the latest official CircuitPython builds for RP2040 and RP2350 boards..." explains Torrone's announcement at Adafruit.com. "The new Turbo in CircuitPython helps when the board spends time calculating: making neopixel effects, drawing fractals, processing audio, filtering sensor readings, or preparing lots of pixels. Those projects can get smoother animation, quicker responses, or room to do more things at once." With Turbo, it's easier, better, and now even faster to make LED light up costumes that also reacts to sound at the same time, a sensor dashboard with animated graphics, or a tiny game doing physics while drawing the screen. Turbo speeds up the busy Python parts. It won't make a slow sensor or display connection faster... Your computer turns selected functions into instructions the chip can run directly. Python still handles the rest. We have measured speedups, real display captures, and examples you can pull apart to see what happened. None of this arrived alone. CircuitPython, MicroPython, PyMCU, compiler tools, open hardware, and people sharing their work gave us pieces to connect. The Bao experiments take that idea somewhere else, handing calculations to four helper cores. Now we get to make those paths easier to use, compare results, and find the next useful thing. Maybe that's smoother animation, a responsive instrument, or an idea we haven't tried yet. That's what I like about open source. Someone shares a piece, someone else sees a possibility, and we get to keep building it together.

Read more of this story at Slashdot.

TeamPCP, the threat actor behind an unrelenting flurry of attacks on open-source software this year, has been active much longer than previously thought, according to research Oligo Security shared exclusively with CyberScoop. 

The threat actor, which gained notoriety and has captivated threat hunters as it compromised and injected malicious code into more than 1,000 software packages in less than four months earlier this year, was also responsible for attacks dating back to 2020, Oligo Security found. 

The security vendor’s research team found multiple attacks that bear the markings of TeamPCP, including a late 2025 campaign involving the exploitation of a ShadowRay vulnerability that resulted in the first self-propogating botnet running on hijacked AI infrastructure.

Evidence uncovered during that investigation into the ShadowRay 2.0 campaign was linked to more historical attacks originating from the same IPs, domains and other infrastructure TeamPCP used in attacks that captured widespread attention earlier this year. 

“The scariest thing in this campaign is the speed at which the payloads evolved and changed and adapted to the environment they run in. We saw changes in the speed that we’re not used to seeing in these kinds of attacks. They’re usually slow, careful,” said Uri Katz, director of research at Oligo Security. “This was clearly with the help of AI — the payloads changed rapidly to adjust and change to the environment that they were trying to attack.”

One of the domains that Oligo Security identified in July 2025 was in the profile of TeamPCP’s official GitHub account, said Avi Lumelsky, AI security researcher at Oligo Security. “It’s public, they’re not even trying to hide their identity,” he said. 

From there, Oligo linked TeamPCP to activity tracked under multiple names, including TA-NATALSTATUS and IronErn, spanning from 2020 to late 2025. Much of that activity was traced to the same IPs, domain names, a file server and command-and-control server, researchers said. 

TeamPCP emerged publicly as a brand in late 2025. Soon after, “TeamPCP started to go really broad and do campaigns, which are much more noisy,” said Gal Elbaz, co-founder and CTO at Oligo Security. 

Widespread adoption of AI and TeamPCP’s use of the technology supported this growth as the threat actor built a brand, got more active on social media and boasted publicly about its activities and claimed victims.

“The ability to control the infrastructure and orchestrate the attack with AI was also super new, and I’m sure it helps them,” Elbaz said. 

“All of the companies in the world are in this race to adopt AI because they are afraid their business will die, and they understand, of course, the opportunity. But it’s also what gives the attacker this power to go into it,” he added. “If you don’t really have visibility in what’s going on there or how it behaves, that’s exactly what attackers are after.”

TeamPCP’s more recent attacks have capitalized on new security gaps created by developers’ increasing reliance on AI and the automated systems companies use to deploy code. The threat actor is also consistently wrecking the open-source frameworks and software packages these systems rely on. 

“Most AI infrastructure is open source by design because nobody has the manpower and money to develop everything from scratch,” Lumelsky said. 

“We love open source. We use many of these products ourselves, but it’s all about reading the documentation, and I think many of these tools place the responsibility of using it right and security on the user, and developers are not used to these new kinds of animals,” he added. “That’s why the trust can be exploited at scale.”

As it uncovered a long operational history spanning multiple campaigns, Oligo Security has gained more confidence in understanding how TeamPCP operates. It also means TeamPCP was likely involved in other attacks that haven’t been attributed to it yet or attacks that haven’t been detected. 

“There’s a lot more out there that we haven’t caught or been able to prove up until now,” Elbaz said.

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While AI is supposed to help defenders, it’s now creating more than twice as much noise as human-triggered incidents CrowdStrike detects as potentially malicious. The company’s threat hunting team and systems triaged an average of 14 million detection leads daily, resulting in about 36,000 customer alerts during the one-year period ending in June.

“AI agent-driven behaviors have surged past human triggers,” said Adam Meyers, senior vice president of counter adversary operations at CrowdStrike. “AI has driven the detections significantly above what humans are causing, and this gives you a sense of how frequently AI is being used, and really just that it’s being used everywhere.”

The threat posed by AI showed up incessantly during the past year, sparking alarming shifts and heightened targeting across software defects, open-source supply chains and AI tools themselves — all of which create greater difficulties for defenders, CrowdStrike said in its annual threat hunting report. 

“The AI tools that are being implemented by every enterprise across the globe right now are also creating an extended attack surface,” Meyers said during a press briefing. 

“AI is now a tool, a target, and a force multiplier for adversaries,” researchers wrote in the report, adding that AI-enabled malicious activity surged 89% during the past year as attackers used the technology to scale operations, hasten tradecraft and target AI infrastructure.

Attackers are using frontier AI models to uncover vulnerabilities and develop resources, including AI-generated scripts, payloads and commands that increase their effectiveness and efficiency. The technology also allows threat groups to design more creative ways to run automated attacks and boost impact by manipulating, interrupting or sabotaging AI systems and data.

“AI is both the weapon and the target,” Meyers said. 

Most organizations don’t view it as such, and thus far haven’t secured or put proper guardrails around the AI tools they use or address the ways attackers can use AI against them, he added. 

AI’s mark on vulnerabilities is particularly concerning, as reflected by what Meyers described as “one of the scarier stats” in this year’s report: 88% of vulnerabilities were weaponized through AI within 48 hours. 

“This is creating a rich ecosystem of vulnerabilities for attackers to use against various systems,” he said. It also renders the 30-day patch window obsolete, forcing organizations to struggle under a new baseline patch cycle of 24 to 48 hours, according to Meyers.

The AI ecosystem also became the next software supply chain battleground during the past year, as evidenced by TeamPCP’s rampage through open-source software in the first half of this year. 

The threat cluster compromised more than 300 software dependencies in one day, Meyers said. 

AI tools are already in the crosshairs and the attack surface will continue to grow as agentic systems, AI application integrations and dependency managers for AI agents proliferate, the report concluded.

“The same AI tools driving modern businesses are creating under-defended attack surfaces that adversaries are exploiting,” Meyers said. “We have to secure AI. This is absolutely critical.”

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The Cybersecurity and Infrastructure Security Agency published a guidebook for federal agencies Thursday to aid them on managing security risks with open-source software, touching on topics like patching and open-source AI models.

An executive order President Joe Biden signed and that President Donald Trump amended ordered CISA and other agencies to issue open-source security recommendations to federal agencies. But the guidance is also timely, given a recent slew of attacks on open-source software (OSS).

“As part of our statutory mission, CISA remains laser-focused on enhancing the nation’s cybersecurity by collaborating with government, industry and the open-source community to understand and securely use OSS,” said Chris Butera, acting executive assistant director for cybersecurity. “CISA encourages federal civilian agencies to review this guide and implement the principles and practices to improve risk management, better execute their mission, and better serve the public.” 

The document, “Open Source Software: Security Principles and Practices,” touts the advantages of open-source software — which anyone can use, modify and share — as offering benefits in efficiency, cost, security transparency and more, but notes that it also has unique tradeoffs.

“All software carries risk, and OSS is no more or less risky than other software. The key distinction is that, with OSS, agencies can directly assess code quality and security, rather than relying solely on vendor assurances,” the guidance reads. “OSS is increasingly intertwined with emerging technologies such as artificial intelligence. Agencies that adapt to OSS’s unique characteristics will position themselves to meet future challenges and leverage new innovations.”

The guidance says that agencies need to take steps to evaluate the trustworthiness of an OSS project before approving an OSS component for use, and track OSS in their asset management repositories. It details how agencies should deal with patching, including when there’s a new OSS vulnerability that doesn’t have one. It offers advice on how agencies might contribute to OSS projects, produce them and secure rights for government reuse of code when contracting for custom software development. And it explains how it should approach open-weight AI models.

“Agencies should approach ‘open source’ AI systems differently from other OSS because open source licenses for AI software do not require the level of transparency needed to evaluate the trustworthiness of the software,” the guidance states.

Æva Black, an open-source security expert and former OSS lead at CISA, said she applauded her former agency for the guidance, telling CyberScoop that it “demonstrates a grounded understanding of the global, diverse, and participatory nature of open source software development, and provides essential guidance for federal agencies to safely use open soure during a crucial moment.” 

She singled out its recommendations on the risks of deploying unverifiable open-weight AI models on sensitive networks.

“Due to recent advances in AI, particularly in large language models capable of finding and exploiting software vulnerabilities, vulnerability management is facing a global crisis,” she said. “Many proprietary software vendors are using this as an opportunity to spread ‘fear, uncertainty, and doubt’ about open source in order to capture public attention, and, I presume, public money — but when used responsibly and maintained collaboratively, I believe open source software is, and will remain, the safest and most cost-effective means for building large scale public infrastructure.” 

CISA has produced a bevy of security guidance and updated advisory materials this week: on the creation of software bills of materials written in conjunction with other agencies and allied governments that won praise from experts; on the isolation of vital operational technology during a crisis, also written with other agencies and allied governments; and the release of updated secure cloud configuration baselines for Google Workspace.

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Amazon’s security researchers say a hacking group tied to North Korea targeted small, little-noticed software packages more than a year before it struck one of the internet’s most widely used programming tools.

The company’s threat intelligence team said Wednesday at a media roundtable at its Arlington, Va., offices that the same group linked to the recent compromise of the open-source axios software library also planted malicious code in a package called typo-crypto in March 2025, a full year before the axios breach. Researchers found the connection while tracing domain records tied to the axios attack back to earlier activity.

“We believe the March 2025 typo-crypto campaign was a rehearsal,” said CJ Moses, Amazon’s chief information security officer, adding that the target’s small scale let the group test its methods “without putting that on the big stage.”

Amazon said the group also compromised two other packages, debug and chalk, in September 2025. Until now, those three incidents had not been publicly linked to the same actor. Security researchers track the group under several names, including UNC1069, Sapphire Sleet and Stardust Chollima. 

Axios, debug and chalk are code libraries used by software developers around the world to build applications. Axios alone is downloaded more than 100 million times a week. “That number represents real organizations putting real code into production systems every single week,” Moses said.

In the typo-crypto case, the malicious file was named “core.js” and was made to look like a legitimate, unrelated package called core-js. Amazon said the file activated only when it received a specific numeric input, then reached out to a server controlled by the attackers to download a second piece of code. That second stage was written differently depending on whether the infected computer ran Windows, macOS or Linux. The code combined encoded text with a cipher, a method Moses said was meant to slow down analysis, including by AI-based review tools, without relying on heavy encryption.

Amazon said the typo-crypto package had few downloads compared with axios, debug or chalk. Researchers believe that initial target served as practice, letting the group refine its approach before turning to more widely used software. “They did what a lot of people do: crawl, walk, run,” Moses said. 

In each of the four cases, Amazon said, the attackers built a relationship with a maintainer who already had access to a package, then used that access to publish an update containing hidden code. “They didn’t break through a window,” Moses said. “They basically earned the trust of an employee to hand them the keys.”

Cybersecurity firm Wiz separately found that about 1 in 10 cloud computing environments were affected by the debug and chalk incident within a two-hour span, a finding Moses cited to illustrate how fast the impact spread. “Going from there not being a vulnerability, to there being a vulnerability, to there being an exploited vulnerability … used to be days to weeks. Now it’s hours to minutes,” he said.

Rick Anthony, senior engineering manager at Amazon Web Services, said the research further shows how attackers face two basic problems in these types of incidents: getting malicious code into a package that will eventually run inside an organization, and keeping that code hidden from developers or security tools. He said groups are increasingly building reputations as legitimate contributors over time. 

“Let me get my package deployed in as many places as possible so that I can spring the trap later,” said Anthony, describing the mindset behind the approach.

Researchers said generative AI has made it easier for attackers to produce code, documentation and contribution histories that look authentic. Anthony also described a technique in which attackers register package names that AI coding tools sometimes generate by mistake, so a developer following an AI suggestion could install malicious software without making any typing error of their own.

The findings come two years after a separate incident involving a program called xz-utils, in which an attacker spent time gaining the trust of the software’s maintainers before inserting a backdoor. Moses pointed to that case as an early example of a pattern now appearing “at scale” and tied to a nation-state.

Since that incident, separate groups have been running roughshod over open-source software. Another group known as TeamPCP has compromised and injected malicious code into more than 1,000 software packages over a four-month span this year. 

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Malware targeting AI coding assistants and software developers’ automated workflows is spreading into more environments with more capabilities, placing defenders at a growing disadvantage.

A malware strain dubbed Sandworm_Mode, first discovered by Socket in February, represents a growing threat to software development. According to a CrowdStrike report, the self-propagating worm can spread through code repositories with minimal detection, raising alarms about software supply chains.

The malware’s capabilities are extensive, but not especially unique compared to the series of supply-chain worms known as Shai-Hulud, and more recently Mini Shai-Hulud.

“This is the new trend,” Adam Meyers, senior vice president of counter adversary operations at CrowdStrike, told CyberScoop. “This is something we’re seeing more and more. It’s the new hotness right now.”

Sandworm_Mode targets and steals sensitive data, including credentials, keys and secrets that unlock paths to additional services and dependencies throughout the AI toolchain. This includes AI assistants, cloud providers, API keys for nine major LLM providers, CI/CD pipelines and automated systems that build, test and publish code.

These actions blend in with tens of thousands of other commands occurring daily in any given environment infused with AI development tools. 

“Trying to find the signal of something malicious happening is very difficult because there’s so much noise out there,” Meyers said. 

The worm also paces itself, setting multi-day delays to separate initial access from follow-on malicious activity — creating a gap in victims’ telemetry windows, which makes it even more challenging for defenders to detect and attribute the chain of infection properly. 

“AI agents are pulling down all of these different dependencies continuously throughout the day,” Meyers said. “When you’re looking downrange from the perspective of the security operations team, you’re just seeing everybody pulling down these dependencies, and these dependencies self-unpacking and executing, so it just gets really, really noisy to try to find something bad happening.”

The malware covers its tracks further with a bit of a mean streak, by automatically destroying compromised environments if it can’t spread or accomplish its objectives.

“It’s well thought-through, and well developed, so somebody spent some time caring and feeding this thing,” Meyer said.

Despite CrowdStrike’s four-month review of Sandworm_Mode, the cybersecurity firm has yet to gain a firm handle on its intent, but Meyers said it is designed to attain a strong foothold, which could enable long-term access.

CrowdStrike hasn’t determined who is responsible for the malware, yet Meyers said he doesn’t think TeamPCP, a threat group that’s been on a rampage through open-source software this year, is involved. 

“It could be a nation-state threat actor, or it could be an e-crime actor that’s looking to use this to then sell access to other organizations,” he said. “We don’t really know what the intention is.”

The state of Sandworm_Mode and whether it remains active is also unclear. CrowdStrike said it continues to observe recently active malicious supply-chain packages that follow similar but technically divergent patterns.

Ultimately, “the world has changed,” Meyers said, adding that many attackers are pursuing similar paths in the AI toolchain, requiring defenders and threat hunters to place a greater focus on this burgeoning mode of aggression.

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The AI-focused executive order President Donald Trump signed last month gave the Treasury Department, the National Security Agency, and the Cybersecurity and Infrastructure Security Agency (CISA) 30 days to establish a new “AI cybersecurity clearinghouse.” The deadline passed last week.

The clearinghouse is meant to coordinate the scanning, discovery, and validation of software vulnerabilities in critical infrastructure, and then prioritize how those vulnerabilities get patched and distributed.

It’s the right problem to solve. The question now is whether what is created will actually solve it.

The risk is that urgency produces something that looks like a clearinghouse, but functions like a committee: collecting information, convening meetings, and then stalling when it gets to the hard part.

Going beyond bug discovery is mission critical

It’s counterintuitive at a moment when AI-assisted vulnerability discovery is advancing rapidly, but the hard part is no longer just finding bugs. Those of us working at the intersection of AI and cybersecurity know where the real bottleneck is. HackerOne has seen it firsthand as a launch partner in Patch the Planet, OpenAI‘s initiative to use AI to find and fix vulnerabilities in critical open-source software at internet scale. The lesson underpinning that work, and informed by more than a decade of running vulnerability disclosure programs, is consistent: AI tools can surface vulnerabilities faster than anyone can act on them. What lags behind is everything that comes after discovery: deciding which findings are real, assessing severity in context, writing and testing a fix, and getting a patch accepted and deployed by the people responsible for maintaining the affected code.

Experienced human reviewers frequently disagree with AI-assigned severity ratings, because a model cannot see a project’s threat model or operational context. Software providers, especially the many volunteer open-source maintainers that so much of today’s digital infrastructure rely upon, face a relentless queue: verify the claim, assess the importance, write the patch, coordinate disclosure. AI has accelerated the incoming volume without yet equally accelerating our people and processes’ capacity to manage it. Better bug-finding tools mean you find more bugs. The improvements that really matter are the ones that help defenders push patches out and get them deployed faster.

That lesson should sit at the center of how the clearinghouse is designed.

If the clearinghouse focuses primarily on scanning coordination, which the executive order’s text emphasizes, it risks widening that gap rather than closing it. A body that finds more vulnerabilities but cannot move them to resolution is not a security win. At national scale, it is a backlog generator.

Laying a foundation for success

The administration can get this right, but it requires building the correct infrastructure now, not layering it on later.

The clearinghouse needs to do more than coordinate scanning. It needs to actually triage the results. Its core job should be filtering reports to identify which findings are truly credible, exploitable, and consequential for critical infrastructure. Using shared validation standards and risk-based prioritization, it can determine what warrants a national response. Otherwise, it’s just automating bigger backlogs.

Second, the clearinghouse also needs to tackle something more fundamental. Defenders don’t have the resources to respond to what gets reported. Vulnerabilities in critical infrastructure often live in open-source code maintained by small teams or individuals with no formal obligation to respond to disclosures and limited capacity to act quickly. The clearinghouse should work with the National Institute of Standards and Technology (NIST) to develop guidelines for open-source maintainers on structuring repositories and workflows to speed up patch review and deployment.

These guidelines should include how to use AI-assisted patching and clarify what downstream consumers of open-source code should do to help maintainers address vulnerabilities.  Federal policy should create incentives for downstream users to share responsibility for remediation through funding, engineering support, AI-assisted patch development, and procurement requirements that reward participation in coordinated vulnerability response.

Third, the clearinghouse should treat software bills of materials (SBOMs), the structured inventories of the components that make up a software product, as foundational infrastructure. SBOMs are what make it possible to trace where a vulnerable component lives across the supply chain. Without them, validated findings won’t be fixed fast enough at scale.

Finally, the clearinghouse should measure success based on what is fixed, not based on what is discovered.  Agencies need to publish data on validation rates, time-to-patch, adoption of fixes, and recurring classes of vulnerabilities. These metrics help AI systems, software vendors, and policymakers to continuously improve how vulnerabilities are addressed.

Most importantly: the agencies standing up this clearinghouse should resist the temptation to build its operational model from scratch. The private sector and the open-source security community have years of experience running exactly the kind of vulnerability intake, triage, and coordinated disclosure workflows the clearinghouse needs. The executive order wisely calls for voluntary collaboration with industry. That collaboration should be structural, not advisory, embedded in how the clearinghouse operates from the start, not bolted on after the architecture is already set.

The clearinghouse can work. But the challenge is no longer finding vulnerabilities. It is building a system that can turn discoveries into action. That is how its success should be measured.

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Click on the timecodes to jump to that part of the video (on YouTube) Slides for this webcast can be found here: https://www.blackhillsinfosec.com/wp-content/uploads/2020/09/SLIDES_OpenSourceExploitsinCloudsBigDataServices.pdf 4:18 Problem statement and exploitation timeline8:28 MapReduce and […]

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