AI systems like Anthropic’s Project Glasswing and Microsoft’s MDASH are aiding in the discovery of vulnerabilities, filling the ever-growing pool of defects that defenders have to address before exploitation occurs. Yet, through the first half of 2026, these vulnerabilities were no more or less likely to be exploited than all vulnerabilities disclosed during that period, VulnCheck said in a report Tuesday. 

Concerns remain high about AI-discovered vulnerabilities fueling more attacks, but VulnCheck’s review of exploitation data shows that those fears are unfounded, at least so far. 

Patrick Garrity, security researcher at VulnCheck and report author, identified 1,061 vulnerabilities attributed to AI-assisted discovery during the first six months of the year. Of those vulnerabilities discovered by AI, 14 ( 1.3%) were exploited in the wild, a breakdown that aligns with the exploitation rate researchers observed across all vulnerabilities during the same period. 

“While AI-assisted vulnerability discovery clearly has value for both attackers and defenders, the data does not suggest that AI discovered vulnerabilities are inherently more likely to be exploited than those found through traditional methods,” Garrity wrote.

While AI’s contribution to actively exploited vulnerabilities was muted in the first half of the year, it’s too soon to assume that trend will continue. Moreover, none of these major vulnerability-hunting models were running for that full period. Project Glasswing rolled out in April, while Microsoft’s MDASH and OpenAI’s Daybreak were both unveiled in May.

The upward trend in Microsoft’s monthly Patch Tuesday indicates how much the floodgates might open through the remainder of the year as AI models discover more vulnerabilities. The company’s July security update contained an all-time-record of 622 vulnerabilities, besting the previous record-breaking June update with 206 vulnerabilities.

VulnCheck’s state of exploitation report also found that vulnerabilities were exploited much faster after CVE publication, speeding up from an average of 120 days in 2025 to 80 days during the first half of the year.

The intelligence firm also determined which technology categories were actively exploited most often. Content management systems accounted for nearly one-third of the 495 known exploited vulnerabilities VulnCheck identified during the first half of 2026. Network edge devices were responsible for almost 14%, followed by operating systems at nearly 9%, server software at 8%, and AI products — an emerging attack surface — at almost 6%.

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2026 has turned out to be the year when predictions about AI-powered cyberattacks, long hypothesized as a potential risk associated with AI improvement, seem to be coming true. New models have capabilities on par with the best human hackers, marking a pivotal window of opportunity in both AI and cybersecurity policy. This is a transitional period where new technologies are pushing existing American cybersecurity infrastructure to the brink. The real question isn’t whether cybersecurity still matters, but rather: How will the risks that AI introduces be managed before they outpace defenses, and who will step up to lead this challenge?

Attempts to control access to models with powerful cybersecurity capabilities, such as the federal government’s export controls (and their subsequent revocation) on Anthropic’s Mythos and Fable models, can only ever be a temporary solution. As with previous generations of AI models, other companies will soon catch up and develop models with Mythos-level capabilities. OpenAI was already hot on Anthropic’s heels with its GPT-5.5 model; more recently, Chinese lab Z.ai released its open-weight GLM-5.2 model, which early research suggests may be on par with Anthropic and OpenAI’s latest models when it comes to cybersecurity. Controlling AI is nearly impossible when foreign companies race to build more powerful models and release them publicly, so anyone with sufficient computing power can modify them for their own purposes.

The only long-term solution is to invest in defense. 

The problem is that defensive efforts haven’t kept up with the pace of AI progress. The federal government cut resources to key agencies like CISA and redistributed their authorities. This created a gap that AI companies have filled by taking on responsibilities that should be government-led. Some examples are Anthropic’s Project Glasswing and OpenAI’s Patch the Planet initiative, which aim to shore up critical infrastructure providers and open-source software libraries. AI companies have some incentives to invest in defense, both to improve public relations and strengthen software supply chains that they also rely on—but only to a certain extent. Unlike the public sector, they are incentivized to limit liability and blowback associated with irresponsible corporate behavior, not to secure the nation or its citizens. It’s a good thing that OpenAI and Anthropic have publicly committed to improving U.S. cyber defense. However, they are only positioned to help with one part of a very large problem. 

AI companies shouldn’t be expected to singlehandedly coordinate U.S. cyber defense, because many of the most urgent fixes have nothing to do with AI. Right now, AI companies can use their most powerful models to find software vulnerabilities and write patches. This is undoubtedly important, but the real challenge is making sure patches actually work and deploying them to key systems without causing problems. This is especially true for critical infrastructure, which relies on systems that are fragile, understaffed, and required to run continuously. 

AI companies bear responsibility for cyber defense, especially given the threats their own technologies create. But this responsibility is shared with other companies and the government.  Critical infrastructure owners and operators, government agencies, and corporations all need a trustworthy source of information to judge the evolving risk landscape and to outline the options to reduce that risk. Traditionally, the federal government has played the role of an information clearinghouse, receiving intelligence from both the public and private sectors and releasing guidance to benefit various stakeholders. Responding to and recovering from cyberattacks has traditionally been the government’s job. It should stay the government’s job, not become an AI company responsibility. 

There is no question that cyberattacks, whether powered by AI or not, will happen in the future. Leaders should strengthen our defenses by doing the following: measuring our exposure to attack, testing how systems perform under attack, and shortening recovery times. AI companies have introduced new threats and should help address them, but they can’t replace the government’s role. So far, the federal government has only reacted to AI and cyberthreats instead of planning ahead. What we need is real long-term cybersecurity strategy, not quick-fixes like blocking individual model releases. 

Everyone sees the threat coming—the question is whether or not we have the will to do anything about it before it’s too late.

Jessica Ji is a senior research analyst at Georgetown University’s Center for Security and Emerging Technology (CSET), where she works on the CyberAI Project.

Andrew Lohn is a senior fellow at Georgetown University’s Center for Security and Emerging Technology (CSET), where he works on the CyberAI Project.

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An epidemic of cyberattacks on open-source software has mounted in recent months, making clear how uniquely difficult it is to protect the publicly available code, from both a policy and a technical perspective, that serves as the foundation for so much of the digital world.

While open-source software security got a boost in attention under President Joe Biden — whose administration grappled with the fallout from the potentially catastrophic Log4j flaw that emerged in 2021 — a number of open-source experts say that government protection efforts have suffered setbacks under President Donald Trump. Many also say companies that heavily rely on open-source software, which is basically all of them, haven’t shouldered enough of the responsibility for safeguarding it.

“What we’re seeing is years of lack of investment sustainment in open-source software that is finally starting to catch up to us, where it seems like every week there’s a new supply chain compromise,” said Jack Cable, who held a role at the Cybersecurity and Infrastructure Security Agency where he worked on open-source security before departing under Trump.

The advancements of frontier artificial intelligence models stand to exacerbate the risk further, while simultaneously illustrating what makes defending open source difficult: Project Glasswing said shortly after its announcement that it had uncovered 6,202 high- or critical-severity vulnerabilities in a scan of more than 1,000 open-source projects, but that it had disclosed only 502 of them to open-source project maintainers and only 75 had been patched as of May 22 (albeit some due to typical patching lagtimes).

At the same time, there are questions about how much the government can help, even as overseas governments seek to focus on open-source security.

The evolution of open-source risk 

There are a series of factors contributing to the current threat to open-source software, experts say.

One is simply that attackers go to the area where they can get the highest return on their work. Compromising open-source software gives them the chance to get into the supply chain and exploit additional targets.

“Twenty years ago, open source was still fairly niche,” said Æva Black, who also worked on open-source security at CISA but left when Trump came back into power. “The potential blast radius if you managed to compromise open source was relatively small, because back then the world didn’t run on open source. Now almost everything runs on open source,” she said, from modern cars to satellites.

Another part is the nature of open-source software itself.

“It’s a symptom [of having] lots of open source [that] is a little bit under-maintained or not cared for enough, so that we spend too little effort and money and infrastructure on them,” said Daniel Stenberg, who is the creator and maintainer of cURL, a popular open-source project. “Lots of open source is being maintained by small teams, lots of volunteers, and I think that that’s a tough situation.”

That doesn’t mean the maintainers are to blame, Stenberg said. The companies that rely on open-source need to be diligent about using it, Black said.

“What we’re seeing in that realm right now is not new; it is more advanced and far more widespread,” she said. “The problem remains that companies who use open source — because open source is by far the most efficient way to collaborate on non-product value features — most companies are not implementing a responsible and safe utilization pathway.”

Open-source projects lack a systematic way to handle coordinated vulnerability disclosures, unlike companies or industry groups with formal processes, said Dan Lorenc, CEO and co-founder of Chainguard. Project maintainers sometimes aren’t reachable, and those who are available are flooded with reports, many of them unverified findings from AI tools that waste their time without adding value..

Of course, some of those vulnerability reports turn out to be legitimate. “Mythos and AI models have contributed to an uptick in the number of vulnerabilities and things that we’re able to find” in open-source software, said Alex Zenla, chief technology officer for the cybersecurity company Edera.

All of that leaves more room for companies, non-profits and world governments to improve open-source security.

A moment of momentum

While open-source software security isn’t a new issue, the 2021 discovery of the Log4j flaw sounded alarms within the cybersecurity community. Jen Easterly, then the director of CISA, called it “one of the most serious I’ve seen in my entire career, if not the most serious,” with the potential to affect hundreds of millions of devices given the ubiquitous nature of the popular open-source logging library.

A year later, the Cyber Safety Review Board released its report on the incident, concluding that swift action from industry and government averted a disaster. But the incident “called attention to security risks unique to the thinly-resourced, volunteer-based open source community,” it wrote. “This community is not adequately resourced to ensure that code is developed pursuant to industry-recognized secure coding practices and audited by experts.”

The U.S. government actions after included some steps focused specifically on open-source software such as creation of the Open-Source Software Security Initiative and hires of well-regarded open-source security experts at CISA such as Black, but also some steps that could be applied more generally and still help with open-source security, such as greater promotion of secure-by-design, memory-safe languages and software bills of materials (SBOMs).

Some of the Biden administration work on open-source security started before Log4j, such as provisions from an executive order he issued in 2021 that directed CISA along with the Office of Management and Budget and General Services Administration to issue guidance to agencies. 

The administration’s 2023 cybersecurity strategy also stepped into the long, thorny discussions over software liability, with a mention of open-source security: “Responsibility must be placed on the stakeholders most capable of taking action to prevent bad outcomes, not on the end-users that often bear the consequences of insecure software nor on the open-source developer of a component that is integrated into a commercial product.“ The Biden administration always indicated that addressing software liability would take a prolonged battle ahead.

Under Trump, many of the Biden administration’s efforts have languished. CISA’s splashy hires on open-source are gone, including Black, Tim Pepper and Anjana Rajan. Also departed are leading figures on secure-by-design and SBOMs, with CISA personnel cutbacks slicing deep. 

No one has seen any sign that the national cyber director-led Open-Source Software Security Initiative is active, with few participants remaining in government today. The Trump administration cyber strategy doesn’t mention open-source.

“The loss of open-source experts at CISA “is unfortunate, and it will be hard for the government to try to rebuild capacity, but I do think now more than ever CISA has a core role to play to secure open source software,” Cable said.

The pressure is mounting

It’s not that the issue is getting zero attention from those in a position to make a difference. Nick Andersen, the acting director of CISA, said last month that open-source security was an area of particular concern for him.

Andersen responded to concerns about CISA staffing levels on open-source security and spoke more broadly on the topic in a statement to CyberScoop.

“As artificial intelligence and other technologies have the power to transform how vulnerabilities are discovered and exploited, CISA recognizes that the open source software (OSS) that underpins much of the nation’s critical infrastructure will need to be hardened,” he said. “CISA actively collaborates with our partners on shared priorities, including OSS security, to ensure time and resources are spent where they matter the most.  We have an immensely talented team, but are also accelerating our hiring in critical areas, to strengthen the nation’s defenses against cyber threats.”

The Office of the National Cyber Director did not respond to requests for comment.

There’s been some activity on Capitol Hill, too. The Securing Open Source Software Act, which Cable worked on during a stint as a Senate staffer, would direct CISA and other agencies to take actions to mitigate open-source software security risks, but the legislation has stalled since its introduction in 2022. A portion of the bill, however, was included in the Department of Homeland Security funding law Trump signed in April, directing CISA to brief Congress on the value of establishing something like an open source program office, which some companies use to manage open source within a given firm.

Senate Intelligence Committee Chairman Tom Cotton, R-Ark., has pushed the executive branch to improve its awareness of foreign adversaries playing roles in open-source software used by national security-focused agencies.

The annual defense policy bill in the House calls on the Defense Department’s chief information officer to report to Congress on a plan to secure open-source software supply chains, saying lawmakers are “concerned that the Department lacks sufficient visibility into the origins, maintenance, and security of OSS applications and software dependencies.”

That defense authorization bill language is “really beneficial, and I think it signals acknowledgement of this changing of culture” around open-source security risks, said Hayden Smith, founder of HuntedLabs, whose company won a contract with the Space Development Agency on supply chain security — agency work that the defense bill singled out.

“The report language is the first time the Hill is trying to get a true handle on foreign influence in open source code where they have oversight,” he said, saying it was a “piece of the puzzle” along with Cotton’s letter and a memo from Secretary of Defense Pete Hegseth last year about foreign influence in the Pentagon supply chain. “It’s good and would trickle down into everyone who provides software to the department.”

Zenla, though, believes trying to isolate China from open-source systems isn’t in and of itself a good idea. 

“I don’t think that that makes a lot of sense, because they’re actually pretty good things that people contribute to open source,” she said. “Not everyone is malicious, and what are we going to do, spy on every single open source maintainer?” It’s more about doing things like making sure that highly-classified systems are set up in a separate way, she said.

Europe is also taking action to secure open-source software that the United States doesn’t seem ready or willing to do right now. Germany, for instance, devotes grants to the security of open-source projects, although Stenberg pointed out that sometimes money doesn’t equate to maintainers being able to fix flaws more quickly, depending on the project’s size.

The Cyber Resilience Act (CRA) adopted by the Council of the European Union in 2024 could offer another road on open-source security. The CRA requires those who use open-source software products as part of any commercial activity to take certain security measures. 

Black said that when she was at CISA, there were discussions between the agency and European counterparts about finding compatible ideas on open-source security, but that momentum died with the Trump administration.

But “Europe kept rolling, and now has in place a new legal framework that is set to really reshape open-source security for potentially the whole world, but certainly for anyone who wants to work with Europe on open source,” she said.

Lorenc recently wrote that “open source isn’t governable.” He said an organization like a neutral nonprofit, possibly using some government funding, should take responsibility for things like coordinating vulnerability disclosure into one pipeline. He also said there needs to be one authority in charge of “forking” — that is, taking a project and assigning stewardship elsewhere — when a maintainer isn’t responsive to vulnerabilities. 

There are differing opinions on how much past government warnings, advisories and guidance have helped. Smith gave some credit to government agencies that “have all responded to open source attacks using the means they have.”

Stenberg said that “I don’t think they make any big dent at all in the big scheme of things.” They might get some attention initially, “then two years later we all forgot about them, and they actually didn’t change much.”

Ideally, everyone could get on the same page, Zenla said. “The best way to do this is if people actually collaborated on a global scale on some sort of regulation around this, but that seems nearly impossible at the current moment,” she said. (The United Nations’ Open Source Week runs all this week.)

But if there’s an upside to the spate of attacks on open-source software, it’s the energy it gives to how better to secure it, Lorenc said, invoking the political saying to never let a good crisis go to waste.

“Everyone knows the industry has to change,” he said. “This is a really good crisis, and the right things are happening in the right places, and organizations are rethinking their culture around software development, and they know what they have to do. It’s just something that’s never been top of the priority list for the last 10 years. Now it is, and they’re doing it, and it’s, ‘Can we do it fast enough?’”

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The U.S. government on Friday ordered Anthropic to immediately suspend foreign access to Fable 5 and Mythos 5, its two most advanced artificial intelligence models, citing national security concerns tied to a reported method of bypassing the models’ safety restrictions. 

The directive, issued late Friday afternoon by Secretary of Commerce Howard Lutnick in a letter to Anthropic Chief Executive Dario Amodei, placed the two models under export controls that prohibit use by foreign nationals, whether inside or outside the United States. 

Because of the scope of the restrictions, which includes foreign-born Anthropic employees, the company announced Friday evening that it disabled the models to ensure compliance. Access to the company’s other AI models was not affected. 

Fable 5 and Mythos 5 had been released earlier this week, with Anthropic describing them as the most capable systems it had ever deployed. Mythos was available to members of Project Glasswing, which allowed selected cybersecurity companies to use the model to identify and address security flaws.

It’s unclear how the Commerce Department action affects Project Glasswing. Anthropic did not respond to a request for comment.

The Commerce Department‘s letter did not detail the specific national security concern. In its blog post Friday night, the company said its understanding is that the government became aware of a technique for “jailbreaking” Fable 5, a term for methods that circumvent a model’s built-in safety guardrails. According to Anthropic, the government provided only verbal evidence of what it described as a “narrow, non-universal jailbreak,” which essentially involved prompting the model to read a specific codebase and identify software flaws. 

Anthropic disputed the severity of the finding. The company said it reviewed a report it believes formed the basis of the government’s directive and found that the capabilities demonstrated were already available in other publicly accessible models, including OpenAI’s GPT-5.5. The company said those same capabilities are used routinely by cybersecurity professionals for defensive purposes. 

Katie Moussouris, chief executive of the cybersecurity firm Luta Security, posted on BlueSky Saturday that the issue stems from “Defense Oriented Prompting,” a security-first method of engineering AI system instructions that treats natural language as code.

Other reports claimed that Amazon was responsible for flagging the security issues in the model. The company did not respond to CyberScoop’s request for comment. 

Anthropic acknowledged in its statement that perfect jailbreak resistance is not achievable for any model provider, and said it had designed Fable 5 around a “defense in depth” strategy, combining narrow jailbreak resistance with active monitoring. The company said no testers had found a universal jailbreak capable of broadly bypassing the model’s safeguards. 

“We disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people,” Anthropic wrote. “If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers.”

Friday’s directive is the latest episode in a prolonged dispute between Anthropic and the Trump administration. In February, President Donald Trump moved to bar Anthropic’s products from federal agencies after the company sought stronger restrictions on how the Pentagon used its technology.

Despite that, as Anthropic released Mythos under Project Glasswing, the National Security Agency was given Mythos 5 to conduct offensive cyber operations. Earlier this month, Trump signed an executive order directing federal agencies to bolster cyber defenses and establish a voluntary mechanism for the government to gain early access to powerful AI models before deployment. 

The administration’s stated rationale for Friday’s action drew widespread skepticism from researchers and analysts. Dean Ball, a senior fellow at the Foundation for American Innovation, called the move “baffling.” Chris McGuire, a senior fellow at the Council on Foreign Relations, said targeted export controls on model access could be a legitimate policy tool, but called the across-the-board restriction “highly questionable” and the deemed export provisions — which restrict foreign nationals inside the U.S. — “just absurd.” 

The broader implications for the AI industry remain uncertain. Aaron Levie, chief executive of Box, described the directive as “a big turning point for AI regulation,” arguing that the government’s willingness to deem specific models too powerful for certain uses establishes a precedent with potentially far-reaching consequences.

Other tech leaders in the government supported the action. 

“We fully support @POTUS and @SecWar in prioritizing national security and the security of our warfighters, DIB partners, critical infrastructure, international partners and allies,” DOD CIO Kirsten Davies wrote in a social post on X. “Some things are simply more important than revenue cycles, clickbait, and pre-IPO valuation. America First. Always.”

Anthropic said it believes the situation stems from a misunderstanding and is working to restore access as soon as possible.

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Troy West was in Warsaw when his dinner was interrupted by his phone. But he was happy about it.

West, associate director of cybersecurity for autonomous offensive security company XBOW, had just learned that a trial version of the company’s platform had found a vulnerability that led to a full takedown of a development environment used by Moderna, the pharmaceutical company primarily known for its work related to mRNA vaccines.

It was, by most measures, exactly the kind of outcome a security team dreads. But for West and Farzan Karimi, Moderna’s deputy CISO, it was something closer to a proof of concept. XBOW’s product had done in hours what a human penetration tester could not — and it had done so with a level of persistence and creativity that neither of them had fully anticipated.

The episode is one data point in a much larger shift now rippling through the cybersecurity industry: The artificial intelligence models discovering vulnerabilities are moving faster than the teams that have to patch them.

Across recent conversations and presentations, industry experts said the tools are getting sharper, the attack surface is getting larger, and the gap between finding a problem and fixing it is not closing fast enough. For now, most organizations are caught between the speed of discovery and the slowness of remediation, with vendors across the industry rushing to position their products as the way through.

A shift in scale 

The inflection point came with Claude Mythos. When Anthropic announced the highly guarded model, security executives at major enterprise technology companies took notice in a way they had not with prior frontier releases. 

Zscaler was among the early organizations given access to the model, and CEO Jay Chaudhry told CyberScoop that he directed his team to use it to probe the company’s own applications.

“Are we finding some serious stuff? Yes, indeed,” Chaudhry told CyberScoop at Gartner’s Security & Risk Management Summit. He was careful to note that the findings were not necessarily more severe than those produced by other models. The issue, he said, was volume. 

“There aren’t enough resources and cycles to fix all those,” he said. 

The reason Mythos changed the calculus, according to Tom Gillis, general manager for infrastructure and security products at Cisco, comes down to code complexity. Legacy network infrastructure was built on tens of millions of lines of code developed over decades, and earlier AI models lacked the context window and reasoning capacity to comprehend it in full.

“The models couldn’t understand the entirety of it before,” he told CyberScoop. “Now they can. That’s why they’re finding all these vulnerabilities.”

The problem runs deeper than application code. Firewalls and network switches often run for decades without updates or reboots, and many have never been patched in any meaningful way. The combination of aging infrastructure and newly capable AI models has created what Gillis described as a meaningful and accelerating shift in attacker capability that the industry’s existing operational rhythms were not built to absorb.

An opportunity in existing technology 

Cisco’s answer to the oncoming vulnerability deluge is a technology it calls Live Protect, a compensated control built on eBPF, a Linux feature that lets security software operate at the kernel level to block threats without rewriting system code.

“It’s a pinpoint, laser-fine control that can shield a vulnerability on a production system,” Gillis said. “We’re not touching or modifying the binaries of that production system.”

The intent is to shrink the window between discovering a vulnerability and the next scheduled patch, allowing IT teams to fix issues without taking systems offline.

“This is a finger in the dike that plugs a hole until you get to new change control windows,” he said, acknowledging that some customers may be tempted to treat the shields as a permanent solution. 

The product has been shipping since October, but customer urgency shifted noticeably after Mythos. “Customers are like, ‘Oh, good story, Tom. I’ll think about it.’ Now it’s like, ‘Oh my God, turn this thing on right now.’”

He also noted that eBPF is open source, and said he expects the broader industry to follow. 

“While I’m very proud of Cisco leading the market with these compensated controls, I know my competitors have to do this.”

The bot that broke everything 

But shielding vulnerabilities only works if you know they exist. Karimi, the Moderna deputy CISO, faced a different problem: His vulnerability management system was surfacing hundreds of high-severity findings with no reliable way to know which ones an attacker could actually exploit. His team had skilled red-teamers, but they were finite resources. What he needed was something that could test continuously, everywhere.

“We have some very senior red-teamers and pen-testers in our organization that are pointed in a specific direction,” Karimi said during a presentation at the Gartner summit. “XBOW is covering different attack stories for us.”

West, who leads offensive security for XBOW, describes the platform as a response to a structural problem in how offensive security has traditionally worked. Human testers scope an engagement, run it, write a report, and move on. The window between tests is where risk accumulates.

“Historically you have exploit developers spending time finding the right vulnerabilities, writing the exploits, finding if those exploits are reachable, and then finding a way to chain them all together,” West said. “That takes a long time.”

Given the realities, Karimi decided to put XBOW through a trial, which produced two notable findings.

In the first, XBOW identified a web application firewall bypass on a company application built on the Spring Boot framework. The bypass involved encoding a single character (a capital “A”) as its percent-encoded URL equivalent (A), which the WAF interpreted as a legitimate request, allowing the bot unfettered access. 

The second finding, which was the cause for West’s dinner interruption, was more consequential. West had provided XBOW with access to the source code of an internal application called Orders, used by Moderna’s research partners to procure drug substances, but no login credentials. The platform identified a valid API key embedded in the source code, used it to authenticate, and then began probing the application’s APIs for SQL injection vulnerabilities.

What happened next was not entirely planned. One of those APIs handled a malformed SQL injection attempt in an unexpected way, dumping garbage data into a shared routing application that other services depended on.

“Not only was it able to kick that Orders app I showed you, but it somehow kicked over the entire ecosystem of apps,” West said.

Human pen-testers who reviewed the findings afterward confirmed they were valid, and said they would not have found them on their own. Karimi said despite the outage, his team recognized the value immediately.

“If we’re able to demonstrate where you could have an outage in a safe testing environment, that’s a great signal,” he said.

The broader value, Karimi argued, is in forcing prioritization when bugs are discovered. “If you have exploit proofs, you can provide that plus-one modifier and really point your developers to remediate the top tier of real risk that’s been validated.”

But he does worry about the volume of bugs that will be surfaced by these tools. 

“How do we now handle the volume of bugs that have gone up due to AI-driven scale?” he said. “That’s a whole other problem space.”

A broader reckoning

Across these conversations, a consistent theme was that even as defenders are trying to get arms around the forthcoming wave of bugs, it’s going to be a tremendously uphill battle. That mirrors what some of the industry’s top leaders have been saying for months. 

It also mirrors what the model developers themselves have consistently been warning about. In its announcement about expanding access to Mythos, Anthropic admitted the timeline for a publicly available tool similar to its cybersecurity-focused model is shortening, and there are no guarantees it will be released with safeguards. 

“In that world, cyberattacks could occur much more often, and in much more unpredictable forms,” the blog post reads.

Gillis was blunter about what happens to organizations that don’t move. 

“Some people will be slow to change,” he said. “But the consequence of not making that change is gonna be front-page news. It’s a massive, massive compromise. You know, like, ‘you gave up every credit card number.’ Bummer.”

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Anthropic is broadening access to its Project Glasswing program, adding approximately 150 organizations in 15 countries, the company announced Tuesday, as its restricted Claude Mythos Preview model has already surfaced more than 10,000 high- or critical-severity software vulnerabilities since the program launched in early April.

The expansion follows an initial cohort of roughly 50 partners that were announced when Anthropic first unveiled the initiative. Those members included technology companies such as Amazon Web Services, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks, among others.  

According to the announcement, the new group covers sectors that were underrepresented in the first wave, including power, water, healthcare, communications, and hardware. Many of the new partners are vendors whose codebases underpin critical infrastructure systems.

The company did not give any further details on what companies or organizations were part of the new cohort.  Sources tell CyberScoop that NetSkope and Rubrik, which specialize in cloud security and data management, is part of the group given access in this latest round.

The scale of what Mythos Preview has already found is drawing attention across the security industry. Cloudflare identified 2,000 bugs across its critical-path systems, including 400 rated high or critical, with a false-positive rate the company described as better than that of human testers. Mozilla found and fixed 271 vulnerabilities in Firefox 150 while testing the model, more than 10 times the number found in a previous Firefox version using an earlier Anthropic model. Several other partners reported that their rates of bug discovery increased more than tenfold after deploying the model. 

Anthropic also used Mythos to scan more than 1,000 open-source projects, flagging 23,019 potential vulnerabilities, 6,202 of them estimated as high or critical. Of 1,752 high- or critical-rated findings independently reviewed, over 90% were confirmed as valid. 

The findings have shifted what Anthropic describes as the central issue in cybersecurity. Despite the enhanced ability to discover flaws, the company admits there are challenges with verifying, disclosing, and patching them before attackers can take advantage.

“The bottleneck in fixing bugs like these is the human capacity to triage, report, and design and deploy patches for them,” the company said in its blog post. 

That bottleneck has broader implications. A joint report from the Cloud Security Alliance, the SANS Institute, and OWASP concluded that organizations are “likely to be overwhelmed” in the near term by threat actors using AI to find and exploit vulnerabilities faster than defenders can patch them.

Anthropic has said it will not release Mythos-class models to the general public, citing the absence of safeguards sufficient to prevent serious misuse. In the interim, it has released Claude Security, a product using its publicly available Claude Opus 4.8 model that has been used to patch more than 2,100 vulnerabilities in three weeks. 

The program’s expansion comes as the Trump administration signed a scaled-back executive order on AI security. The order, which was signed hours after Anthropic’s announcement, sets up a voluntary framework requiring AI developers to submit advanced models to a government review up 30 days before public release.

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Anthropic said its month-old Project Glasswing initiative has uncovered more than 10,000 high- or critical-severity software vulnerabilities across systemically important code, a finding the company says has shifted the central problem in cybersecurity from discovering flaws to verifying and patching them.

The findings, drawn from partner reports and independent evaluations, mark one of the first large-scale accountings of what a frontier AI model can do when pointed at widely used code, and of the bottlenecks that emerge once it does.

Several partners reported that their rates of bug discovery had increased more than tenfold. Cloudflare identified 2,000 bugs across its critical-path systems, including 400 rated high or critical, with a false-positive rate the company said it considered better than that of human testers. At one unnamed partner bank, the model was credited with helping detect and prevent a fraudulent $1.5 million wire transfer initiated after a customer’s email account was compromised and followed up with spoofed phone calls.

External evaluations cited in the update tracked with the results Anthropic released. The United Kingdom’s AI Security Institute found that Mythos Preview was the first model to solve both of its cyber ranges — simulations of multistep cyberattacks — from end to end. Mozilla said it found and fixed 271 vulnerabilities in Firefox 150 while testing the model, more than 10 times the number found in Firefox 148 using an earlier Anthropic model. AI-powered security platform XBOW called the model a significant step up over existing systems on its web exploit benchmark.

Anthropic also used Mythos to scan more than 1,000 open-source projects. The model has flagged 23,019 potential vulnerabilities, 6,202 of them estimated as high or critical. Of 1,752 high- or critical-rated findings reviewed by six independent security research firms or by Anthropic itself, over 90% were confirmed as valid, and over 62% were confirmed to be high or critical.

The company did note that while it’s good at finding vulnerabilities, there is still a gap in having people fix every issue. 

“The bottleneck in fixing bugs like these is the human capacity to triage, report, and design and deploy patches for them,” the report states. 

Open-source maintainers have also been contending with a wave of low-quality, AI-generated bug reports, and Anthropic said it tries to reproduce and assess each issue before reporting it. At maintainers’ request, it has sometimes disclosed bugs without further vetting, reporting 1,129 such cases, of which the model estimated 175 to be high or critical.

Anthropic said it has not released Mythos-class models publicly because no company, including itself, has developed safeguards to prevent serious misuse. In the interim, it has released Claude Security in public beta for enterprise customers, which it said has been used to patch more than 2,100 vulnerabilities in three weeks using the publicly available Claude Opus 4.7, and has begun a Cyber Verification Program for security professionals.

The company said it plans to expand Project Glasswing with additional partners, including U.S. and allied governments, before any broader release of the underlying model.

“Glasswing helps the most systemically important cyber defenders gain an asymmetric advantage. However, there is an urgent need for as many organizations as possible to shore up their cyber defenses,” the report states. “We hope that our generally available models, and the new tools, resources, and research we’re providing to accompany them, will support those organizations to improve their cybersecurity posture.”

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Two of the most advanced artificial intelligence models — Anthropic’s Claude Mythos Preview and OpenAI’s GPT-5.5 — have significantly surpassed the already-accelerating pace at which AI systems are completing autonomous cybersecurity tasks, according to separate findings published Wednesday by the United Kingdom’s AI Security Institute (AISI) and Palo Alto Networks.

The AISI, which conducts pre-deployment evaluations of frontier AI models on behalf of the British government, said both Claude Mythos Preview and GPT-5.5 have substantially exceeded the doubling trend the institute had been tracking since late 2024. Whether the results represent an isolated capability jump or the start of a new, faster trajectory remains unclear.

The AISI estimated earlier this year that frontier models’ 80% reliability cyber time horizon — a measure of how long a task takes a human expert, used as a proxy for AI autonomy — had been doubling approximately every five months. That was itself roughly half the eight-month doubling time the institute estimated in November 2025. Now Mythos Preview and GPT-5.5 have since outperformed any trend lines the institute has measured.

“Frontier AI’s autonomous cyber and software capability is advancing quickly: the length of cyber tasks that frontier models can complete autonomously has doubled on the order of months, not years,” the AISI wrote.

The clearest evidence of the capability jump came from the AISI’s cyber ranges, its structured simulations of multi-stage attacks against small, undefended enterprise networks. A newer checkpoint of Claude Mythos Preview became the first model to complete both of the institute’s ranges. It solved “The Last Ones,” a 32-step simulated corporate network attack, in 6 of 10 attempts, and completed “Cooling Tower” — previously unsolved by any model — in 3 of 10 attempts. GPT-5.5 solved “The Last Ones” in 3 of 10 attempts.

Palo Alto Networks reached similar conclusions through its own testing. The company said it began testing Claude Mythos in April as a launch partner for Anthropic’s Project Glasswing, and has since tested Claude Opus 4.7 and OpenAI’s GPT-5.5-Cyber as part of OpenAI‘s Trusted Access for Cyber program.

“The latest models are extraordinarily capable at finding vulnerabilities and changing them into critical exploit paths in near-real-time,” Palo Alto Networks wrote.

The company released security advisories covering 26 CVEs representing 75 issues — compared to a typical monthly volume of fewer than five CVEs — that were identified through AI model scanning across more than 130 products. All important vulnerabilities in its SaaS products had been patched, with patches available for all customer-operated products.

The AISI was careful to note the limits of its data. The estimates are based on a relatively small number of models, and the hardest tasks in the test suite have the least amount of human comparison data. Even so, the institute said the overall trend holds up: dropping any single model from the analysis barely moves the needle, shifting the estimated doubling time by less than a month in either direction. Separate research from METR, a nonprofit that tracks how quickly AI handles software tasks, arrived at a nearly identical figure — a doubling time of approximately four months since late 2024.

“No single benchmark result should be read as a precise measure of AI capability,” the AISI wrote. “Regardless, the direction of change and rapid growth have been consistent across the models, methodological choices and independent data we examined.”

Palo Alto Networks outlined four immediate priorities for enterprises as these models continue to grow in usage: First, find and fix vulnerabilities in code and applications before attackers do. Second, shrink the attack surface and use AI to spot security misconfigurations. Third, deploy detection and response tools across all systems, using machine learning to catch threats in real time. Fourth, build security operations fast enough to respond in minutes, because AI-powered attacks may soon unfold that quickly.

The AISI said it is developing more demanding evaluations, including new cyber ranges and the addition of active cyber defenses, to better reflect real-world conditions as model capabilities continue to advance.

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