A policy paper published Tuesday advocates for software bills of materials (SBOMs) for artificial intelligence as a mechanism for reducing cyber risk and improving transparency, and seeks to give lawmakers, federal agencies and others a roadmap on how to proceed.

The SBOM, commonly described as an inventory of software ingredients, emerged in the 2010s and has expanded beyond software to include hardware and AI.

But the paper from the Institute for Security and Technology, which CyberScoop is the first to report on, argues that AIBOMS require foundational work before they can be widely implemented.  This comes as some companies are already offering AIBOM services and other organizations are actively shaping AIBOM policy.

“What we’re worried about is we would end up in a ‘fire, ready, aim’ situation where everyone was doing it, but we were all doing slightly different things,” said a co-author of the paper, Allan Friedman, who has worked on SBOMs in multiple U.S. government roles. “If we don’t have a shared vision, it becomes a lot harder to have a coherent policy. It becomes a lot harder to have common tools and interoperable data and it becomes a lot harder to use the data that we’re tracking to actually deliver on the promise of supply chain transparency.”

The idea for the paper sprung from discussions with Hill aides and Pentagon staffers, Friedman said, and people like them are the target audience as well.

A key premise is that AIBOM policy needs to explore the topic from two sides.

“How do you solve the chicken-and-egg issue, where no one’s providing the data, so no one’s asking for it, and no one’s asking for it, so no one’s providing it?” Friedman told CyberScoop. “The answer is, you have to go from both supply and demand.”

On the supply side, “An AIBOM should capture relevant details about the models and datasets used for training, fine-tuning, evaluation, validation, testing, retrieval, grounding, augmentation, or other model development or operational purposes,” the paper suggests.

“The demand side begins with some form of forcing function or requirement that organizations understand what is in the products they manufacture and sell,” it states, with one such requirement potentially being an industry mandate to require the tracking of system components — for example, like the “lightweight” standards used in the payment card industry on data security that isn’t overly exact about how components should be tracked.

But it could also include government regulations or contracting conditions, Friedman argues with his Institute for Security and Technology colleague Nick Leiserson. (The scope of government directives on AI is a topic of considerable debate on Capitol Hill and within the Trump administration right now.)

Friedman said the paper isn’t meant to be the be-all, end-all, and acknowledged the prior work of organizations like the Open Worldwide Application Security Project (OWASP) and Linux Foundation.

“We’re not saying this is a brand new topic, nor are we saying that AIBOM will solve all AI security issues,” he said. “I’ve been fighting this fight for SBOM for a decade. You know, SBOM will not pick up your dry cleaning.”

And as AI continues to evolve rapidly, that means papers like the one published Tuesday are just at the beginning of the discussion, Friedman said.

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Major technology companies have joined forces in an effort to use advanced artificial intelligence to identify and address security flaws in the world’s most critical software systems, marking a significant shift in how the industry approaches cybersecurity threats.

Anthropic announced Project Glasswing on Tuesday, bringing together Amazon, Apple, Broadcom, Cisco, CrowdStrike, the Linux Foundation, Microsoft, and Palo Alto Networks. The initiative centers on Claude Mythos Preview, an unreleased AI model that Anthropic will make available exclusively to project partners and approximately 40 additional organizations responsible for critical software infrastructure.

The model has already identified thousands of previously unknown vulnerabilities in its initial testing phase, including security flaws that have existed in widely used systems for decades, according to Anthropic. Among the discoveries is a 27-year-old bug in OpenBSD, an operating system known primarily for its security focus, and a 16-year-old vulnerability in FFmpeg, a widely used video software program that automated testing tools had failed to detect despite running the affected code line five million times. The company has been in contact with the maintainers of the relevant software, and all found vulnerabilities have been patched. 

Anthropic will commit up to $100 million in usage credits for the project, along with $4 million in direct donations to open-source security organizations. The company has stated it does not plan to make Mythos Preview available to the general public, citing concerns about the model’s potential misuse.

The initiative reflects growing concerns within the technology sector about the dual-use nature of advanced AI systems. While Mythos Preview was not trained specifically for cybersecurity purposes, its coding and reasoning capabilities have proven effective at identifying subtle security flaws that have eluded human analysts and conventional automated tools.

“Although the risks from AI-augmented cyberattacks are serious, there is reason for optimism: the same capabilities that make AI models dangerous in the wrong hands make them invaluable for finding and fixing flaws in important software—and for producing new software with far fewer security bugs,” the company said in a blog post. “Project Glasswing is an important step toward giving defenders a durable advantage in the coming AI-driven era of cybersecurity.”

The project comes as the industry has predicted that similar AI capabilities will soon become more widespread. Anthropic executives have indicated that without coordinated action, such tools could eventually reach actors who might deploy them for malicious purposes rather than defensive security work.

Participating organizations will be required to share their findings with the broader industry. The project places particular emphasis on open-source software, which forms the foundation of most modern systems, including critical infrastructure, yet whose maintainers have historically lacked access to sophisticated security resources.

“Open source software constitutes the vast majority of code in modern systems, including the very systems AI agents use to write new software. By giving the maintainers of these critical open source codebases access to a new generation of AI models that can proactively identify and fix vulnerabilities at scale, Project Glasswing offers a credible path to changing that equation,” said Jim Zemlin, CEO of the Linux Foundation. “This is how AI-augmented security can become a trusted sidekick for every maintainer, not just those who can afford expensive security teams.” 

Additionally, Anthropic says it has engaged in ongoing discussions with U.S. government officials regarding Mythos Preview’s capabilities. The company has framed the project in national security terms, arguing that maintaining leadership in AI technology represents a strategic priority for the United States and its allies. Anthropic has been locked in a high-stakes dispute with the Department of Defense about the U.S. military’s use of the startup’s Claude AI model in real-world operations. 

The project’s success will depend partly on whether the collaborative approach can keep pace with rapid advances in AI capabilities. Anthropic has indicated that frontier AI systems are likely to advance substantially within months, potentially creating a dynamic environment where defensive and offensive capabilities evolve in parallel.

“Project Glasswing is a starting point,” Anthropic wrote in a blog post. “No one organization can solve these cybersecurity problems alone: frontier AI developers, other software companies, security researchers, open-source maintainers, and governments across the world all have essential roles to play. The work of defending the world’s cyber infrastructure might take years; frontier AI capabilities are likely to advance substantially over just the next few months. For cyber defenders to come out ahead, we need to act now.”

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