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Slashdot reader zlives shared this report from BleepingComputer: Microsoft used its AI-powered Security Copilot to discover 20 previously unknown vulnerabilities in the GRUB2, U-Boot, and Barebox open-source bootloaders. GRUB2 (GRand Unified Bootloader) is the default boot loader for most Linux distributions, including Ubuntu, while U-Boot and Barebox are commonly used in embedded and IoT devices. Microsoft discovered eleven vulnerabilities in GRUB2, including integer and buffer overflows in filesystem parsers, command flaws, and a side-channel in cryptographic comparison. Additionally, 9 buffer overflows in parsing SquashFS, EXT4, CramFS, JFFS2, and symlinks were discovered in U-Boot and Barebox, which require physical access to exploit. The newly discovered flaws impact devices relying on UEFI Secure Boot, and if the right conditions are met, attackers can bypass security protections to execute arbitrary code on the device. While exploiting these flaws would likely need local access to devices, previous bootkit attacks like BlackLotus achieved this through malware infections. Miccrosoft titled its blog post "Analyzing open-source bootloaders: Finding vulnerabilities faster with AI." (And they do note that Micxrosoft disclosed the discovered vulnerabilities to the GRUB2, U-boot, and Barebox maintainers and "worked with the GRUB2 maintainers to contribute fixes... GRUB2 maintainers released security updates on February 18, 2025, and both the U-boot and Barebox maintainers released updates on February 19, 2025.") They add that performing their initial research, using Security Copilot "saved our team approximately a week's worth of time," Microsoft writes, "that would have otherwise been spent manually reviewing the content." Through a series of prompts, we identified and refined security issues, ultimately uncovering an exploitable integer overflow vulnerability. Copilot also assisted in finding similar patterns in other files, ensuring comprehensive coverage and validation of our findings... As AI continues to emerge as a key tool in the cybersecurity community, Microsoft emphasizes the importance of vendors and researchers maintaining their focus on information sharing. This approach ensures that AI's advantages in rapid vulnerability discovery, remediation, and accelerated security operations can effectively counter malicious actors' attempts to use AI to scale common attack tactics, techniques, and procedures (TTPs). This week Google also announced Sec-Gemini v1, "a new experimental AI model focused on advancing cybersecurity AI frontiers."

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The advent of LLMs and machine learning-based applications "opened the door to a new wave of security threats," argues Google's security blog. (Including model and data poisoning, prompt injection, prompt leaking and prompt evasion.) So as part of the Linux Foundation's nonprofit Open Source Security Foundation, and in partnership with NVIDIA and HiddenLayer, Google's Open Source Security Team on Friday announced the first stable model-signing library (hosted at PyPI.org), with digital signatures letting users verify that the model used by their application "is exactly the model that was created by the developers," according to a post on Google's security blog. [S]ince models are an uninspectable collection of weights (sometimes also with arbitrary code), an attacker can tamper with them and achieve significant impact to those using the models. Users, developers, and practitioners need to examine an important question during their risk assessment process: "can I trust this model?" Since its launch, Google's Secure AI Framework (SAIF) has created guidance and technical solutions for creating AI applications that users can trust. A first step in achieving trust in the model is to permit users to verify its integrity and provenance, to prevent tampering across all processes from training to usage, via cryptographic signing... [T]he signature would have to be verified when the model gets uploaded to a model hub, when the model gets selected to be deployed into an application (embedded or via remote APIs) and when the model is used as an intermediary during another training run. Assuming the training infrastructure is trustworthy and not compromised, this approach guarantees that each model user can trust the model... The average developer, however, would not want to manage keys and rotate them on compromise. These challenges are addressed by using Sigstore, a collection of tools and services that make code signing secure and easy. By binding an OpenID Connect token to a workload or developer identity, Sigstore alleviates the need to manage or rotate long-lived secrets. Furthermore, signing is made transparent so signatures over malicious artifacts could be audited in a public transparency log, by anyone. This ensures that split-view attacks are not possible, so any user would get the exact same model. These features are why we recommend Sigstore's signing mechanism as the default approach for signing ML models. Today the OSS community is releasing the v1.0 stable version of our model signing library as a Python package supporting Sigstore and traditional signing methods. This model signing library is specialized to handle the sheer scale of ML models (which are usually much larger than traditional software components), and handles signing models represented as a directory tree. The package provides CLI utilities so that users can sign and verify model signatures for individual models. The package can also be used as a library which we plan to incorporate directly into model hub upload flows as well as into ML frameworks. "We can view model signing as establishing the foundation of trust in the ML ecosystem..." the post concludes (adding "We envision extending this approach to also include datasets and other ML-related artifacts.") Then, we plan to build on top of signatures, towards fully tamper-proof metadata records, that can be read by both humans and machines. This has the potential to automate a significant fraction of the work needed to perform incident response in case of a compromise in the ML world... To shape the future of building tamper-proof ML, join the Coalition for Secure AI, where we are planning to work on building the entire trust ecosystem together with the open source community. In collaboration with multiple industry partners, we are starting up a special interest group under CoSAI for defining the future of ML signing and including tamper-proof ML metadata, such as model cards and evaluation results.

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Back in 2023 Python's infrastructure director called it "the first step in our plan to build financial support and long-term sustainability of PyPI" while giving users "one of our most requested features: organization accounts." (That is, "self-managed teams with their own exclusive branded web addresses" to make their massive Python Package Index repository "easier to use for large community projects, organizations, or companies who manage multiple sub-teams and multiple packages.") Nearly two years later, they've announced that they're "making progress" on its rollout... Over the last month, we have taken some more baby steps to onboard new Organizations, welcoming 61 new Community Organizations and our first 18 Company Organizations. We're still working to improve the review and approval process and hope to improve our processing speed over time. To date, we have 3,562 Community and 6,424 Company Organization requests to process in our backlog. They've also onboarded a PyPI Support Specialist to provide "critical bandwidth to review the backlog of requests" and "free up staff engineering time to develop features to assist in that review." (And "we were finally able to finalize our Terms of Service document for PyPI," build the tooling necessary to notify users, and initiate the Terms of Service rollout. [Since launching 20 years ago PyPi's terms of service have only been updated twice.] In other news the security developer-in-residence at the Python Software Foundation has been continuing work on a Software Bill-of-Materials (SBOM) as described in Python Enhancement Proposal #770. The feature "would designate a specific directory inside of Python package metadata (".dist-info/sboms") as a directory where build backends and other tools can store SBOM documents that describe components within the package beyond the top-level component." The goal of this project is to make bundled dependencies measurable by software analysis tools like vulnerability scanning, license compliance, and static analysis tools. Bundled dependencies are common for scientific computing and AI packages, but also generally in packages that use multiple programming languages like C, C++, Rust, and JavaScript. The PEP has been moved to Provisional Status, meaning the PEP sponsor is doing a final review before tools can begin implementing the PEP ahead of its final acceptance into changing Python packaging standards. Seth has begun implementing code that tools can use when adopting the PEP, such as a project which abstracts different Linux system package managers functionality to reverse a file path into the providing package metadata. Security developer-in-residence Seth Larson will be speaking about this project at PyCon US 2025 in Pittsburgh, PA in a talk titled "Phantom Dependencies: is your requirements.txt haunted?" Meanwhile InfoWorld reports that newly approved Python Enhancement Proposal 751 will also give Python a standard lock file format.

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Wikipedia remembers Dave Täht as "an American network engineer, musician, lecturer, asteroid exploration advocate, and Internet activist. He was the chief executive officer of TekLibre." But on X.com Eric S. Raymond called him "one of the unsung heroes of the Internet, and a close friend of mine who I will miss very badly." Dave, known on X as @mtaht because his birth name was Michael, was a true hacker of the old school who touched the lives of everybody using X. His work on mitigating bufferbloat improved practical TCP/IP performance tremendously, especially around video streaming and other applications requiring low latency. Without him, Netflix and similar services might still be plagued by glitches and stutters. Also on X, legendary game developer John Carmack remembered that Täht "did a great service for online gamers with his long campaign against bufferbloat in routers and access points. There is a very good chance your packets flow through some code he wrote." (Carmack also says he and Täht "corresponded for years".) Raymond remembered first meeting Täht in 2001 "near the peak of my Mr. Famous Guy years. Once, sometimes twice a year he'd come visit, carrying his guitar, and crash out in my basement for a week or so hacking on stuff. A lot of the central work on bufferbloat got done while I was figuratively looking over his shoulder..." Raymond said Täht "lived for the work he did" and "bore deteriorating health stoically. While I know him he went blind in one eye and was diagnosed with multiple sclerosis." He barely let it slow him down. Despite constantly griping in later years about being burned out on programming, he kept not only doing excellent work but bringing good work out of others, assembling teams of amazing collaborators to tackle problems lesser men would have considered intractable... Dave should have been famous, and he should have been rich. If he had a cent for every dollar of value he generated in the world he probably could have bought the entire country of Nicaragua and had enough left over to finance a space program. He joked about wanting to do the latter, and I don't think he was actually joking... In the invisible college of people who made the Internet run, he was among the best of us. He said I inspired him, but I often thought he was a better and more selfless man than me. Ave atque vale, Dave. Weeks before his death Täht was still active on X.com, retweeting LWN's article about "The AI scraperbot scourge", an announcement from Texas Instruments, and even a Slashdot headline. Täht was also Slashdot reader #603,670, submitting stories about network latency, leaving comments about AI, and making announcements about the Bufferbloat project.

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Wikipedia remembers Dave Täht as "an American network engineer, musician, lecturer, asteroid exploration advocate, and Internet activist. He was the chief executive officer of TekLibre." But on X.com Eric S. Raymond called him "one of the unsung heroes of the Internet, and a close friend of mine who I will miss very badly." Dave, known on X as @mtaht because his birth name was Michael, was a true hacker of the old school who touched the lives of everybody using X. His work on mitigating bufferbloat improved practical TCP/IP performance tremendously, especially around video streaming and other applications requiring low latency. Without him, Netflix and similar services might still be plagued by glitches and stutters. Also on X, legendary game developer John Carmack remembered that Täht "did a great service for online gamers with his long campaign against bufferbloat in routers and access points. There is a very good chance your packets flow through some code he wrote." (Carmack also says he and Täht "corresponded for years".) Raymond remembered first meeting Täht in 2001 "near the peak of my Mr. Famous Guy years. Once, sometimes twice a year he'd come visit, carrying his guitar, and crash out in my basement for a week or so hacking on stuff. A lot of the central work on bufferbloat got done while I was figuratively looking over his shoulder..." Raymond said Täht "lived for the work he did" and "bore deteriorating health stoically. While I know him he went blind in one eye and was diagnosed with multiple sclerosis." He barely let it slow him down. Despite constantly griping in later years about being burned out on programming, he kept not only doing excellent work but bringing good work out of others, assembling teams of amazing collaborators to tackle problems lesser men would have considered intractable... Dave should have been famous, and he should have been rich. If he had a cent for every dollar of value he generated in the world he probably could have bought the entire country of Nicaragua and had enough left over to finance a space program. He joked about wanting to do the latter, and I don't think he was actually joking... In the invisible college of people who made the Internet run, he was among the best of us. He said I inspired him, but I often thought he was a better and more selfless man than me. Ave atque vale, Dave. Weeks before his death Täht was still active on X.com, retweeting LWN's article about "The AI scraperbot scourge", an announcement from Texas Instruments, and even a Slashdot headline. Täht was also Slashdot reader #603,670, submitting stories about network latency, leaving comments about AI, and making announcements about the Bufferbloat project.

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Is OpenAI's ChatGPT violating copyrights? The New York Times sued OpenAI in December 2023. But Ars Technica summarizes OpenAI's response. The New York Times (or NYT) "should have known that ChatGPT was being trained on its articles... partly because of the newspaper's own reporting..." OpenAI pointed to a single November 2020 article, where the NYT reported that OpenAI was analyzing a trillion words on the Internet. But on Friday, U.S. district judge Sidney Stein disagreed, denying OpenAI's motion to dismiss the NYT's copyright claims partly based on one NYT journalist's reporting. In his opinion, Stein confirmed that it's OpenAI's burden to prove that the NYT knew that ChatGPT would potentially violate its copyrights two years prior to its release in November 2022... And OpenAI's other argument — that it was "common knowledge" that ChatGPT was trained on NYT articles in 2020 based on other reporting — also failed for similar reasons... OpenAI may still be able to prove through discovery that the NYT knew that ChatGPT would have infringing outputs in 2020, Stein said. But at this early stage, dismissal is not appropriate, the judge concluded. The same logic follows in a related case from The Daily News, Stein ruled. Davida Brook, co-lead counsel for the NYT, suggested in a statement to Ars that the NYT counts Friday's ruling as a win. "We appreciate Judge Stein's careful consideration of these issues," Brook said. "As the opinion indicates, all of our copyright claims will continue against Microsoft and OpenAI for their widespread theft of millions of The Times's works, and we look forward to continuing to pursue them." The New York Times is also arguing that OpenAI contributes to ChatGPT users' infringement of its articles, and OpenAI lost its bid to dismiss that claim, too. The NYT argued that by training AI models on NYT works and training ChatGPT to deliver certain outputs, without the NYT's consent, OpenAI should be liable for users who manipulate ChatGPT to regurgitate content in order to skirt the NYT's paywalls... At this stage, Stein said that the NYT has "plausibly" alleged contributory infringement, showing through more than 100 pages of examples of ChatGPT outputs and media reports showing that ChatGPT could regurgitate portions of paywalled news articles that OpenAI "possessed constructive, if not actual, knowledge of end-user infringement." Perhaps more troubling to OpenAI, the judge noted that "The Times even informed defendants 'that their tools infringed its copyrighted works,' supporting the inference that defendants possessed actual knowledge of infringement by end users."

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An anonymous reader shares a report: Yet another busy hurricane season is likely across the Atlantic this year -- but some of the conditions that supercharged storms like Hurricanes Helene and Milton in 2024 have waned, according to a key forecast issued Thursday. A warm -- yet no longer record-hot -- strip of waters across the Atlantic Ocean is forecast to help fuel development of 17 named tropical cyclones during the season that runs from June 1 through Nov. 30, according to Colorado State University researchers. Of those tropical cyclones, nine are forecast to become hurricanes, with four of those expected to reach "major" hurricane strength. That would mean a few more tropical storms and hurricanes than in an average year, yet slightly quieter conditions than those observed across the Atlantic basin last year. This time last year, researchers from CSU were warning of an "extremely active" hurricane season with nearly two dozen named tropical storms. The next month, the National Oceanic and Atmospheric Administration released an aggressive forecast, warning the United States could face one of its worst hurricane seasons in two decades. The forecast out Thursday underscores how warming oceans and cyclical patterns in storm activity have primed the Atlantic basin for what is now a decades-long string of frequent, above-normal -- but not necessarily hyperactive -- seasons, said Philip Klotzbach, a senior research scientist at Colorado State and the forecast's lead author.

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