A new Democratic bill in Congress would establish a federal Cybersecurity and AI Board of Investigations to provide independent government oversight of cyberattacks carried out by AI agents, following recent hacks by models run at companies like Anthropic, OpenAI, Meta and others.

The bill, introduced by Sen. Ed Markey, D-Mass., would attempt to establish a federal mechanism to investigate incidents where AI models escape sandbox environments and access live internet systems.

Currently, frontier AI companies like OpenAI and Anthropic largely control the investigation and public reporting of such incidents. Markey and other critics argue that these companies have too much control over investigations and reporting due to their financial and legal interests. 

“Despite the unprecedented depth and scale of recent AI-enabled cyberattacks, the public is learning critical details piecemeal,” Markey said in a statement. “Building stronger defenses requires a full accounting of what goes wrong, and we cannot depend on companies with little incentive to disclose their failures to give us one. We need the Cybersecurity and AI Board of Investigations to get to the bottom of major incidents and give companies and the government the critical information necessary to build resilience and better secure our economy and our country.”

Although frontier AI companies maintain external red-teaming programs and allow limited access to organizations like METR and Redwood Research, they control the scope, terms and time frames of those engagements.

The board, which would coordinate with the secretary of commerce, could subpoena witnesses and conduct “independent and impartial reviews and assessments” of AI agent-led hacks that impact federal information systems or critical infrastructure. 

It would be led by five members, appointed by the president and confirmed by the Senate for five-year terms, with no more than three members from one political party.

The board would also investigate systemic vulnerabilities in the AI supply chain, so-called “near misses” where unauthorized agent-led hacks were “narrowly averted,” and gaps in federal regulatory oversight. It would have technical staff including engineers, malware analysts, and digital forensic experts.

The board would “operate independently from regulatory review and enforcement actions without assigning legal fault or liability for any review and assessment” it conducts, according to the bill.

OpenAI confirmed Wednesday its AI agents breached a statistics portal used by the Australian government’s social services agency, Services Australia. Though the breach happened in June, OpenAI learned of the incident in August. Australian Prime Minister Anthony Albanese said the company did not notify him until Sept. 10, when it sent findings to a general government email inbox, according to the BBC.

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The Pentagon’s top civilian cyber policy official said Tuesday her single priority is expanding the cyber options available to the president and the defense secretary, describing a gap between what commanders are asking for and what the force can deliver.

“I’m focused on one single priority, and that is building a more robust set of capabilities for the secretary and the president,” said Katie Sutton, assistant secretary of defense for cyber policy, at DefenseTalks, hosted by DefenseScoop. “The demand far exceeds the supply we have.”

Sutton traced the department’s posture to 2018, when the military gained authorities to run cyber operations as a traditional military activity. “In those last eight years, we’ve learned a lot, but I feel like the last year has really been the year that cyber has sort of entered the limelight,” she said. “We’ve built up the capabilities, we’ve built up our force, we have the operational experience.”

One particular instance of that limelight are previous reports from an operation in Venezuela to apprehend the country’s former president Nicolás Maduro. Various public statements, including those made by President Donald Trump, stated that power outages during the operation were the result of a cyberattack. Experts told CyberScoop in the aftermath that cyber ops may have been involved, but the visible physical attacks that were also part of the operation alone could plausibly explain the outages.

Sutton further described the shift as rooted in how cyber is used, “not just to counter other malicious cyber actors as a cyber-on-cyber tool, but actually as an integrated tool of cyber warfare.” 

Sutton put data at the center of that argument. “Data is fundamental to every battle that we fight going forward,” she said. “Being able to use our cyber tools to deny that to our adversaries as we go into a kinetic fight will ensure our mission success and provide greater safety for our troops.” 

She also described cyber as a tool leaders can use “below the level of armed conflict to provide options before having to move forward to our kinetic options.”

How AI fits in

Sutton also spoke about how she sees artificial intelligence being integrated into the military’s cyber operations, casting it as a natural fit. “Cyber is a digital domain; it’s all based on zeros and ones,” she said, further arguing the department needs to be “an AI-first organization.” 

However, she highlights AI-specific risks—like data poisoning and weakened guardrails—that demand the Pentagon adopt a fundamentally different approach to cyber operations.”

“We’ve spent a long time chasing cybersecurity and dealing with decisions that we made in moving quickly to creating an internet,” she said, adding that security came second in that era. “We’re going to need to fundamentally think about that differently from AI.”

The remarks were similar to Gen. Randall Reed, head of U.S. Transportation Command, who extended this concern to logistics, warning that the military’s predictable supply chains have become vulnerable to AI-enabled adversaries. 

At the conference, Reed suggested AI itself may be the solution, helping Transcom become less predictable and illustrating how military officials are leveraging the technology across multiple operational domains.

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OpenAI and the Ukrainian government have agreed to a partnership that will provide AI tools and subsidized computing resources to better protect the nation’s critical infrastructure from cyberattacks.

The agreement, announced Wednesday at OpenAI’s New York office, will provide Ukrainian cybersecurity officials with access to advanced AI models designed for cybersecurity work through the company’s Daybreak program. OpenAI said it is also pledging over $1 billion in subsidized tokens to support the initiative.

During a panel discussion Dmytro Kushneruk, consul general of Ukraine in San Francisco, outlined how the tools would be used for cybersecurity automation, including functions such as incident response, threat triaging, login analysis, inventorying systems, code analysis and validating vulnerabilities.

In nearly all cases, Kushneruk said the primary benefit was carrying out those functions at machine speed. But this speed is meant to complement, not replace, Ukrainians’ human expertise.

In regard to incident response Kushneruk said humans must view “thousands and thousands of these logs and they have to find what’s really important, that’s why AI can give capable defenders really much greater advantage and leverage.” 

“This is why the object is not to replace the cyber defender with AI, but to make sure the cyber defender acts faster,” he added.

Kushneruk said that for Ukraine, the partnership “is really not about protecting computers, it is about actually keeping our country running.”

Ukraine faces approximately 6,000 cyberattacks per year, or about 15 per day, according to Kushneruk. Over the past twelve years, the country’s critical infrastructure, including electricity and water systems, has endured sustained attacks from Russia in the form of cyberattacks and physical strikes.

Since Russia’s 2022 invasion, Ukraine’s critical infrastructure has been under constant threat. While missiles remain the primary concern, Kushneruk said Ukraine has been preparing to protect vital services since Russian GRU hackers shut down the country’s power grid in 2015. 

He added that while the country was “maybe not so much prepared” to deal with the fallout in 2015, it improved over time, including the resilience displayed in 2025 when trains kept running after Russian hackers attacked Ukraine’s railway system.

Some national security experts and congressional committees have explicitly cited the resilience of Ukrainian critical infrastructure as a model for U.S. industry.

Naz Durakoğlu, minority staff director of the U.S. Senate Foreign Relations Committee, said there is “pretty much across the board” agreement between the parties in favor of similar adoption of defensive AI tools by U.S. critical infrastructure operators, though issues like regulation remain sticking points.

“This is something that’s already happening, and frankly, it’s just kind of a basic duty of government to make sure that when you turn the tap on, water comes out, the electricity doesn’t go out, and hospitals keep running and treating patients,” said Durakoğlu. “So there is a broad understanding that this is a major issue, and I will say seeing what Ukraine has to go through day-to-day is also a huge wake-up call to our members on a bipartisan basis.”

OpenAI has publicly pushed for its product, and AI at-large, to be used to solve these types of problems. Company president and co-founder Greg Brockman signed an open letter released earlier this year calling for “collective action” and widespread use of AI models to find and fix vulnerabilities before the rest of the world,  including foreign governments and cybercriminals, got access to the same capabilities.

According to Politico, OpenAI CEO Sam Altman met with U.S. power companies in July to discuss using AI to protect the nation’s electrical grids.

On Wednesday, OpenAI’s national security policy head, Sasha Baker, said the company felt “urgency” to try to strike similar agreements with other governments and industries.

“There’s this period of time where we’re really rushing to get [these tools] in the hands of critical infrastructure operators, of governments around the world, of people who want to patch systems, defend their networks, remediate vulnerabilities because we know as these tools proliferate out there in the ecosystems, there are going to be bad guys out there that also try to use them,” said Baker. “So, we have this window of time to take action and we’re really motivated by the idea that we need to act with some urgency.”

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A House Democrat tapped to lead his party’s efforts on artificial intelligence has introduced legislation that would establish a test program within the Cybersecurity and Infrastructure Security Agency to give critical infrastructure operators free access to frontier AI models to protect their systems.

Rep. Josh Gottheimer, D-N.J., introduced the AI Cyber Defense Act Monday, inspired by the series of cyberattacks on water facilities in recent months. “If we don’t get ahead of it, it can mean a disaster for our families,” he said at a news conference when he first announced the measure and others tackling water cybersecurity.

Gottheimer holds a couple of posts relevant to the legislation: He’s one of three co-chairs of the House Democratic Commission on Artificial Intelligence, and the top Democrat on the House Intelligence Committee’s cyber subcommittee. He also has bipartisan support for the bill, with co-sponsors Reps. Don Bacon, R-Neb., Zach Nunn, R-Iowa, Hillary Scholten, D-Mich., and Greg Landsman, D-Ohio.

The bill directs the Department of Homeland Security, through CISA, to create a program “through which owners and operators of critical infrastructure that participate in the Program are able to securely utilize artificial intelligence procured through the Secretary and technical assistance provided by the Secretary to protect against, detect, test for, and remediate vulnerabilities in the cybersecurity of such critical infrastructure.”

AI-tinged, water-focused cybersecurity pilot programs are all the rage lately. The introduction of Gottheimer’s legislation is adjacent to, but different from, a test program that the Office of the National Cyber Director recently announced in Texas.

One criticism of that program is that private sector companies offered their cyber and AI services through it on a purely voluntary basis, with no significant budget to bolster the pilot. Gottheimer’s bill would authorize $100 million for the pilot program from 2027 to 2031 before it ends, although appropriators would have to follow through on providing the actual dollars. The Trump administration has significantly cut CISA funding in its second term.

“Right now federal funding for critical infrastructure has an uncertain future and many of our local communities just don’t have the resources they need to pay for AI tokens to do the patching they need,” Gottheimer said when he introduced the bill. “It’s expensive to bring the AI in to analyze your system and find those vulnerabilities.”

Critical infrastructure owners and operators could apply for the pilot program, which the bill directs to give priority to nonprofit, publicly owned, rural and small-sized organizations.

“The same technology that can help a small town’s IT guy find and patch a gap in cybersecurity can also help a hostile government find a hundred more it hasn’t even discovered yet,” Gottheimer said when he announced the bill. “AI didn’t create this threat, but it’s accelerated it, and our defenses have to keep up.”

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President Donald Trump continued to defend his administration’s hands-off approach to AI regulation in the wake of hacks carried out by U.S. commercial frontier models that have rattled policymakers and industry veterans and spurred calls for more regulatory oversight.

In a Truth Social post Monday, Trump dismissed worries from critics that “AI is going to kill us,” comparing them to complaints from environmentalists about climate change, which he also alleged was a false narrative. He also posited that nothing may matter more than future U.S. dominance of the technology over geopolitical rivals like China.

“Whoever wins AI, WINS!” Trump posted. “We are leading now over China, and everyone else, and I’m going to keep it that way! I’m not going to stifle Growth, of something that will be bigger than the Industrial Revolution, or the internet, itself.”

Trump has previously suggested that good leadership is the only regulation the U.S. needs for artificial intelligence. He later claimed the Department of Justice was ready to “rein things in” if companies overstepped, but offered no specifics on enforcement, legal authority, or where he would draw that line.

“We will be careful, and that’s why we have the Department of Justice, and other Law Enforcement bodies, that will rein things in if we have to, but I will only encourage AI or, SI (SUPER INTELLIGENCE)!” Trump concluded.

Secretary of the Treasury Scott Bessent recently told Congress that private lawsuits could force AI companies to institute better security, saying it’s clear what the government “shouldn’t do on safety is to give these labs a liability exemption, which is what they are asking for.”

“The best way to guarantee safety is that the creators are liable for what they build and generate,” Bessent said.

Beyond existential fears, critics also argue that inadequate regulation or cybersecurity controls in current AI systems make them impossible to fully control or monitor.

Recently, former President Barack Obama criticized the argument from Trump administration officials that the free market will naturally push industry toward self-regulation and that “these companies will solve the safety issues because they have every incentive to do so.”

“If it turns out to be dangerous, people will just sue them and they’ll be worried about financial liability,” Obama said last week in remarks at Colgate University in New York. “That’s not how we treat airlines or drug companies or food companies.”

The Trump administration issued an executive order earlier this year that set up a voluntary testing regime for some commercial frontier models, largely at private industry’s discretion. That order was significantly delayed and altered by AI industry boosters to ensure that governmental review did not cause companies to postpone their release timelines for new models.

That agreement did not last long before fast-moving events caused the administration to strike another, non-public agreement with frontier AI companies like OpenAI, Anthropic and others governing pre-release testing for models.

But the Trump administration has consistently argued that regulation will harm, not help, U.S. innovation and global competitiveness, and the threat of China frequently looms large in those discussions.

Experts believe China’s AI models are behind U.S. models at the top of the market, where OpenAI and Anthropic have consistently pushed the frontier limits of model capabilities. But Chinese lower and “middle class” models are often cheaper, more efficient and can even outperform more powerful models because users can dedicate exponentially more tokens for their tasks.

The U.S. government has accused Chinese AI companies of conducting widespread, “systematic” distillation of U.S. frontier models, with the implicit encouragement of Beijing.

In defending the administration’s approach, David Sacks, co-chair of the President’s Council of Advisors on Science & Technology and a top adviser on AI issues, specifically cited the threat from China and other countries that he claimed would not be subject to similar restrictions.

“We’re not the only country that has advanced AI labs, and as the president declared…we have to win this AI race,” Sacks told Politico in May, later adding “I think that’s the first thing to recognize is that if somehow we slow down or stop AI development, it doesn’t mean that AI progress is going to stop. It just means it’s going to happen in other countries and specifically China.”

Some observers have alleged that despite their larger differences, top leaders in the U.S. and China may view AI similarly at the strategic level, specfically that increased adoption – and risks – of AI are inevitable.

Ronan Murphy, director of the tech policy program at the Center for European Policy Analysis, posited that while there may not be a formal agreement between the two countries, “they share views both in Beijing and in Washington, particularly in the White House, of: you have to allow this to happen.”

“Clearly there’s a call for regulation from many quarters of AI in the U.S. and elsewhere, but in the White House – and we heard David Sacks talking about it [recently] – It’s ‘let them cook,’ and the Chinese approach seems to be the same,” said Murphy in a press briefing. “So there might be consensus at that level, if nothing else.”

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Microsoft, along with a group of industry partners, disrupted EvilTokens, a short-lived but highly consequential cybercrime platform that investigators linked to more than 12,000 compromised Microsoft customer email inboxes across more than 10,000 organizations globally, the company said Tuesday.

Acting on federal court order Sept. 15, Microsoft and partners seized 50 websites the phishing-as-a-service used for operations and disabled more than 175 domains linked to EvilTokens’ supporting infrastructure. 

EvilTokens, launched in February 2026, was “a powerful cybercrime platform that used AI at every step of the attack chain — from compromising email accounts to designing intricate roadmaps for financial fraud and scams,” Steven Masada, associate general counsel and general manager of Microsoft’s Digital Crimes Unit, wrote in a blog post.

About 1,000 cybercriminals used EvilTokens over the course of its operation, a Microsoft spokesperson told CyberScoop.

The service was centered on an AI-style chatbot that cybercriminals used to analyze victims’ inboxes, identify trusted relationships, payment authorizations and other sensitive details that could facilitate fraud.

“AI was not simply helping attackers write more convincing messages. It helped them decide who to target, who to impersonate, and how to most effectively exploit the relationship to extract as much money as possible,” Masada wrote. 

EvilTokens was one of the most widely used phishing-as-a-service platforms prior to its takedown. It facilitated business-email compromise campaigns by stealing session tokens that allowed cybercriminals to sift through a victim’s inbox and maintain persistent access.

“We cannot estimate the total fraud attributable to all EvilTokens activity. However, we were able to correlate at least 13 complaints filed with the FBI’s Internet Crime Complaint Center to EvilTokens-linked activity, representing approximately $1.7 million in reported losses,” a Microsoft spokesperson said. “Because many incidents go unreported and not all victims can be definitively linked to specific campaigns, we believe this is a conservative estimate.”

Victims of EvilTokens were largely concentrated in the United States, Canada, the United Kingdom, Australia, India and France, according to Microsoft. SpyCloud, which supported the takedown, identified compromised email domains spanning 79 countries.

Microsoft said it also identified two men behind EvilTokens — Felix Utomi and Waidi Segun Adams — and attributes the development and support of the platform to Storm-2992, a threat actor unaffiliated with any other known cybercrime groups.

The United Kingdom’s Metropolitan Police acted on that information Sept. 18 when it served warrants in the greater London area, arrested the men accused of making articles for use in fraud and money laundering and seized their digital devices.

The Metropolitan Police said it received information from Microsoft about EvilTokens’ administrators in August. Utomi and Adams were released on bail as the investigation continues. 

“The two primary operators identified in our investigation were residing in the U.K.,” a spokesperson for Microsoft told CyberScoop. “While our investigation focused on those individuals, we believe others may have supported the operation in various capacities.”

Microsoft’s legal filing in the U.S. District Court for the Eastern District of Virginia refers to five additional unidentified people allegedly acting as support personnel and users.

Microsoft and others involved in the EvilTokens takedown, including Health-ISAC, Cloudflare, OpenAI, Shadowserver and TRM Labs, didn’t fully quantify how much fraud the service enabled, but it gained popularity quickly among cybercriminals and was lucrative for its operators.

Coinbase, which also aided the investigation into EvilTokens, said it traced about $1.1 million in revenue for EvilTokens from its paying customers. The virtual currency company’s threat researchers found more than 1,000 deposits to EvilTokens from more than 700 distinct addresses through June 2026. 

Operators sold access to the service through Telegram for a $1,500 initiation fee and a recurring $500 subscription. EvilTokens significantly lowered the barrier to entry for cybercriminals by including specialized tools for identity attacks, cloud systems, social engineering and financial fraud in a single interface.

The service allowed cybercriminals to map organizational structure and permissions in Microsoft Graph, which enabled lateral movement, researchers said. With active tokens gained through a collection of highly-targeted phishing lures, cybercriminals consistently bypassed multi-factor authentication, email gateways and endpoint security tools.

Microsoft said the platform’s creators developed portions of the platform with AI and it uncovered capabilities from multiple AI models. 

“It packaged much of the criminal process into a commercially run service, complete with subscription pricing, customer support, management dashboards and tools designed to move customers from account access toward financial exploitation,” Masada added.

The companies and organizations involved in the globally-coordinated takedown identified and notified potential victims, shared indicators of compromise and shared intelligence with law enforcement about EvilToken’s operators and some of its customers.

Experts advised organizations and employees to treat unsolicited device codes as a red flag, assume compromised accounts are fully cataloged in minutes, and independently verify requests to change payment information or redirect funds.

“The infrastructure supporting EvilTokens has been disrupted, but the model it demonstrated will not disappear with it,” Masada warned.

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Researchers said they used Anthropic’s Claude and OpenAI’s Codex to identify a damaging flaw embedded in a popular software decoding tool that could leave major internet platforms, enterprise services, and web frameworks vulnerable to data theft and remote access.

The vulnerability, nicknamed HEIF Heist, refers to the malware’s ability to trigger memory corruption errors in affected software, allowing the attacker to pilfer sensitive data from its victims. In a report published Thursday, the researchers laid out the potential damage an attacker could cause, including gaining access to internal OpenAI repositories, leaking user files, access tokens, and other sensitive data for online services like Amazon Web Services, and gaining remote code execution privileges across a range of online services, including Meta’s core product suite, GitHub Enterprise servers and open-source internet forum Discourse.

“Even when Remote Code Execution isn’t immediately achievable, the attack primitives may still allow arbitrary heap disclosure, letting an attacker ‘heist’ in-memory data such as other users’ data and environment variables,” wrote Hacktron researchers Harsh Jaiswal, Mohan SRK, Rahul Maini and Sudhanshu Rajbhar.

The researchers relied heavily on AI systems, including frontier models from OpenAI and Anthropic, to conduct their research. Attribution for the research is described as being “led” by the Hacktron human researchers “assisted by Hacktron Harness, GPT-5.6 Sol, and Opus 5.”

According to the research, the attack exploited the way that code parsing tools in many popular software decoders — specifically libheif and libde265, used to parse C and C++ software — process certain image files.

By uploading HEIF, HEIC and AVIF image files corrupted with malicious code, the attacker could bypass most of the victim’s application layer defenses, in many cases achieving remote code execution privileges for accounts or products tied to major AI and tech brands.   

While the latest version of libheif has been patched, the researchers said “any deployment lacking the latest upstream security patches is potentially vulnerable.”

In one incident detailed in a Sept. 13 blog, Jaiswal, Maini, and Hacktron researcher Mohan Pedhapati described how chaining two vulnerabilities, including an image parser flaw, could compromise OpenAI employee accounts.

With access to the compromised accounts, researchers could reach OpenAI’s internal repositories. As a proof of concept, they opened a pull request in the company’s “monorepo,” a centralized library where code is shared across projects, using the employee’s Codex credentials. 

According to a timeline provided by the researchers, the flaw was discovered on July 25 and patched within days. They said the entire attack, from discovering the initial vulnerability to gaining access to the repositories, took less than 72 hours. OpenAI paid them a bug bounty of $6,500 for their work.

Given that AI models are increasingly integrated into enterprise and personal networks, an attacker exploiting HEIF Heist could have accessed far more than just OpenAI’s systems and data.

“Until two months ago, a user or OpenAI employee logging into OpenAI’s own help forum could have had their ChatGPT and Codex accounts taken over,” the researchers wrote. “Since people can connect various services to Codex and ChatGPT, the scope of what we could theoretically access was huge, including GitHub, Slack and emails.”

CyberScoop has reached out to OpenAI for comment on the research and additional information.

At the same time, the researchers said the attack paths they found were not particularly easy or efficient to exploit.

“Exploitation requires fingerprinting the target version and tailoring the payload images,” the blog stated. “Some of our RCE attempts landed only after thousands of image uploads. That said, an AI agentic approach with a frontier model like GPT-5.6 Sol cut exploit development time down to roughly 1 to 3 days from initial probe to remote RCE. A motivated attacker can convert a vulnerable upload endpoint into RCE or an info leak.”

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The past few weeks have “felt very strange” for Juan Andres Guerrero-Saade.

Like many, he is trying to sort through the spate of frontier-model AI agents from OpenAI, Anthropic, Meta and others hacking their way onto the open internet over the past few months, particularly amid the already-heated national debate around the emerging technology and its impact on society.

Guerrero-Saade, a fellow for AI and security research at SentinelOne and an adjunct professor at Johns Hopkins University, said the hacks are worth taking seriously, but at a time when businesses and open-source maintainers should be focused on further hardening their systems and policymakers should be discussing new solutions,  “what we see is cybersecurity being used essentially as an excuse for these AI doomer arguments.”

The incidents have spawned those “doomer arguments” amid an intense public debate about the technology, the pace of industry development, and whether government and the private sector are doing enough to protect against “doomsday”-type scenarios, where AI systems take over or attack large parts of the internet or society.

Guerrero-Saade is among a growing chorus of cybersecurity professionals who say that while AI systems pose real, unique threats to our systems, the apocalypse is far from inevitable. Most of the public concerns around the incidents, let alone worries about killer AIs attacking critical infrastructure, assuming control of the internet and wiping out humanity, are either technically impossible or can largely be controlled through established cybersecurity principles.

There is this “narrative or magical thinking of ‘Well, AI is going to be able to hack everything, and therefore it can control everything, and therefore it’s going to kill us all,’” he told CyberScoop. “And you [think] these just don’t add up. They’re not very well-reasoned arguments.”

This fatalistic narrative tied to AI’s eventual dominance doesn’t hold up under scrutiny, according to experts CyberScoop spoke with. In recent conversations, cybersecurity and national security professionals raised questions about both the technical solutions OpenAI and Anthropic use to contain their models, as well as the glaring absence of federal oversight from federal regulators or truly independent third-party review.

For example, Jacob Coxon, an Anthropic employee who resigned over AI safety concerns, told CBS News that frontier models could not be “unplugged” by humans once deployed because the model would copy itself to thousands of other computers connected to the internet.

By contrast, Matt Tait, a former information security specialist at UK signals intelligence agency Government Communications Headquarters (GCHQ), pointed out that the models run by Anthropic and other frontier companies require extremely expensive, “ultraspecialist” machines that “are functionally supercomputers.”

“There is a zero chance that Anthropic’s most capable models will be able to extract their own model and run in the wild, because those supercomputers essentially only exist in datacenters,” Tait said.

“Not a credible warning”

Other former cybersecurity government leaders say the agentic hacks represent a failure by regulators and industry to deploy known technical and policy options that make it harder for these types of incidents to occur.

Matt Hartman, former deputy executive assistant director for cybersecurity at the Cybersecurity and Infrastructure Security Agency, said “we should not accept harmful AI behavior as inevitable or unmanageable.”

“There are meaningful steps companies can take to monitor agent activity, constrain permissions, detect anomalous behavior, and build stronger safeguards into how these systems operate,” said Hartman, now a chief strategy officer at Merlin Group. “Those controls will inevitably involve trade-offs in capability and speed, but that’s a familiar cybersecurity challenge. Our goal should be to manage the risk without unnecessarily limiting the enormous benefits AI can provide.”

Ciaran Martin, former head of the UK’s National Cyber Security Centre, took issue with the way the CEOs of frontier AI companies have framed the threat of “rogue” AI behavior as inevitable, while issuing dire warnings about future threats and capabilities with little transparency.

Martin’s comments came after an essay published by Anthropic CEO Dario Amodei that cited the threat of a HuggingFace-style swarm of agents that could create a botnet capable of “taking over the entire internet” within 6-12 months.

This, Martin said, “is not a credible warning,” because it doesn’t explain how the exploitation would function, how such a botnet would persist on the internet, or how it would escape law enforcement. 

 “It assumes no monitoring of systems, no anti-virus, no DDoS protection, no network segmentation, no incident management, no nothing of any kind of the cybersecurity on the global Internet of the type that has developed over the last 30 years,” wrote Martin. “For a claim of this magnitude, there is neither evidence for the contention nor a credible account of a path to this outcome.”

Meanwhile, some federal government cybersecurity leaders have touted the technology’s disruptive potential and called for more widespread adoption of AI tools by defenders.

Joseph Alm, assistant secretary of cyber, infrastructure and risk resilience at the Department of Homeland Security, said classified systems may retain stronger protections. But for most other data, AI models are “just going to know things and be able to infer things about the world, and we’re going to have to adapt to that as almost inevitable.”

Asked by CyberScoop whether the government or frontier AI companies could be doing more to prevent or deter their models from carrying out unauthorized hacks via agents, Alm cited recent efforts by the Trump administration this year to establish pre-release testing of commercial models as a step in the right direction. But he called unauthorized AI agent hacks “a new threat class” that is different from previous threats and can be easily distributed to users through open-source software today.

“I think what we can do is…encourage the building of good sandboxes, so that the best models aren’t used for this and the stuff you see out in the wild is the kind of detritus that you can actually respond to effectively and control your networks,” said Alm.

Other experts have shared similar concerns. Earlier this month, CrowdStrike CEO George Kurtz recently warned of a new threat class emerging alongside nation-states, cybercriminals, and hacktivists: “the agent state.” By pairing AI systems with small human teams, these operators can now match the speed, scale, and sophistication of government-backed hackers.

“It took a nation to fund the talent, the tooling, the infrastructure, the patience,” said Kurtz. “That scarcity is over.” 

To be sure, frontier AI companies tout their commitment to both approaches. OpenAI and Anthropic have rolled out an array of cybersecurity partnerships, external red-teaming programs, vulnerability disclosure programs and cybersecurity technical advisory bodies filled with cybersecurity experts.

Mohammed Husain, strategic delivery lead for government at OpenAI, told CyberScoop that the company deploys both internal safety guardrails for their models and relies on outside cybersecurity vendors for additional expertise.

Internally, OpenAI focuses on vulnerabilities at the training level: filtering data poisoning attacks, blocking harmful datasets, and using network controls to prevent prompt injections. For other security layers like sandboxing, identity management, networking controls, they outsource to external vendors. 

“I don’t think OpenAI has all the answers here but what we do as a research lab is we’re going to focus on levels of protection we have expertise in and we partner to self-complement,” said Husain.

AI safety vs. AI cybersecurity

In response to the HuggingFace hack, OpenAI and Anthropic have allowed third-party organizations, such as nonprofit AI research firms METR and Redwood Research, to investigate. But multiple cybersecurity professionals told CyberScoop that both firms lack incident response experience and focus primarily on AI alignment and safety. Their reporting on the hack also lacked critical details: network monitoring logs, telemetry, and other data standard in cybersecurity threat intelligence reports.  

METR president Chris Painter addressed those general concerns in a post on X, saying since 2022 the organization has worked with Google, Anthropic, OpenAI, Meta, Amazon and others on investigations and third-party evaluations. Painter said none of the AI companies fund METR and that his employees are not uniformly “doomer” or “accelerationist” around AI.

Painter also said METR’s work ensures that if AI systems become autonomous or “rogue” within a company, there are ways to share that information with governments and people “outside the company’s walls.”

“We don’t accept money from frontier AI companies,” wrote Painter. “They haven’t paid us for our work, and we don’t accept donations from them or their employees. As we’ve shared previously, multiple frontier AI companies currently provide us with free access to their models in order to perform our evaluations, research, and engineering.”

AI safety and AI cybersecurity advocates take different approaches to securing “rogue” AI behavior. Safety advocates focus on aligning models around ethical training and behavior. Cybersecurity advocates argue that technical and regulatory controls must go further—actively preventing models from accessing what they need to carry out malicious behavior.

Guerrero-Saade said sandboxes in particular can easily be programmed with aggressive cybersecurity monitoring in order to spot when something odd may be happening and react in real time.

“I can’t think of an easier situation in which to set up trip wires, set up configurations like DNS servers, just different parts where you can say ‘Hey, anomalous behavior is happening,’” he said. “We should have been able to tell this immediately, not weeks and months later. So watching [the AI hacking incidents] go down is a little ‘crazy-making’ because we’re seeing things that, frankly, look like neglect, negligence, people just mishandling things, and then being told that these are categorically new incidents that mean that AI systems need to be treated completely different from anything that’s come before.”

While cybersecurity experts say AI systems are, at their core, still software, they do operate differently from more traditional code in ways that can make them harder to predict and control.

John Hultquist, chief analyst at Google’s Threat Intelligence Group, said most software has been deterministic. It may have bugs or vulnerabilities, but an expert could generally understand how it would react to certain stimuli, making it easier to design straightforward controls.

AI models are non-deterministic, with far more variability than traditional software. That can break security controls that rely too much on predicting behavior in advance. Using AI to enforce security controls on other AI models faces the same problem: the systems being deployed to control AI are just as unpredictable. 

But people are also non-deterministic, and people have developed systems in other industries and practices to account for that.

Hultquist drew on his Army experience, noting that “they give incredibly dangerous, expensive things to 18-year-olds” and expect responsible use. The military manages this through two types of controls: deterministic ones like strict weapons and ammunition protocols, and non-deterministic ones like human officers who monitor and correct violations.

Similarly, established cybersecurity controls have been used by incident responders to detect and prevent or mitigate ongoing cybersecurity breaches.

“I don’t think we should throw out all the other tools that we have learned to use as well. I think that would be utterly foolish,” he said, later adding “I will say that if we use only non-deterministic tools to figure out when things are happening, we shouldn’t be surprised when we get the wrong answer.”

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Researchers say they have discovered thousands of malicious software packages uploaded to an online public software repository that were left by a “swarm” of OpenAI agents.

According to an incident timeline published Friday by researchers Spencer Kitts, Thomas Larsen and Sydney Von Arx, the campaign began May 5 when they observed a handful of suspicious packages being uploaded to RubyGems, a public library for the Ruby programming language. By May 11 and 12, the site saw more than 2,000 malicious uploads from the same actors before RubyGems maintainers halted new user sign-ups for four days to stop the flow.

In one instance, the agents attempted to exploit a very recent vulnerability that had only been discovered this past July that would have given them access to RubyGem user API keys. According to Colby Swandale, the technical lead at RubyGems, the flaw involved an improper cache configuration. While initial access logs showed no evidence of malicious key use, Swandale acknowledged the review was limited in scope and inconclusive. 

According to the report published Friday, the agents also used “disposable” email addresses and exploited another bug in RubyGems platform (since patched) that allowed them to register new accounts and gain API keys without verifying their email address.

The researchers said their understanding, based on discussions with “people in the RubyGems community,” is that OpenAI had yet to disclose the involvement of their agents in the May campaign.

An OpenAI spokesperson told CyberScoop that the company is aware of the incident and said they were in contact with both the researchers and RubyGems to conduct a broader review. The company characterized the episode as “benign,” describing it as routine training runs where agents attempt to access publicly available data.

“Based on our review, our agents used the RubyGems platform to access the internet to carry out benign tasks and retrieve public information,” the spokesperson said. “We’ll continue to investigate as part of our broader review of agent activity during training and evaluation.”

In many ways, the agents were not subtle about their identities or goals.

Days into the campaign, researchers noticed that some of the packages had “oai” in their filenames, while fifteen of them had “oai” set as their author and another listed the email “openaixyz65947@gmail.com” as their point of contact.

They also “clearly regarded what they were doing as hacking,” naming some of their files “hack.rb,” “evil.rb,” “inject.rb” and “exploit.rb.” Other packages were given names like “pwnp999,” “exfiltestwand3,” and “hacksvn,” and comments referring to things like a “malicious probe” or “#hack” are present through the files.

They also said the actors’ behavior was extremely similar to another incident revealed earlier this month where OpenAI agents flooded a German wiki  with thousands of hacking-related posts. OpenAI has confirmed their agents were involved in that incident.

The RubyGems campaign used some of the same retrieval methods as the German Wiki agents, while thousands of malicious packages uploaded included a similar snippet, r.jini.ai, that was contained in the German posts.

Cybersecurity company Socket first flagged the campaign in a threat intelligence report posted May 13, but it does not mention or attribute any of the activity to OpenAI or AI agents.

However, the researchers said they had only limited visibility over the model’s actions and how successful some of them were, noting only OpenAI had the full details.

“This analysis is entirely based on the publicly available RubyGems packages uploaded by these agents,” the researchers wrote. “However, we do not have access to the rest of the AI behavior, in particular the chain-of-thought produced by the model during the incident, which is internal to OpenAI. Therefore, we do not know why the AI agents chose this strategy or whether it was successful.”

OpenAI’s spokesperson told CyberScoop that to date, they have not been able to verify the specific claims about malicious packages or exploitation detailed in the report and are continuing to investigate.

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OpenAI is facing mounting pressure from Capitol Hill due to the attack its agents carried out on Hugging Face, while lawmakers voice widening concerns about AI’s potentially existential risks.

Sen. Josh Hawley, R-Mo., criticized OpenAI leadership for what he described as “reckless” activities leading up to the Hugging Face breach, and accused the company of withholding important details from a technical report it released in late August.

The Chair of the Subcommittee on Disaster Management kicked off an investigation into the incident “in light of new, disturbing evidence,” he wrote in a letter Tuesday to OpenAI CEO Sam Altman.

“My investigation will probe this AI hacking incident, along with growing allegations of the existential risk of new AI products,” Hawley added. 

“The Hugging Face incident was an important moment for AI safety and a warning about the risks that can come with increasingly capable AI across the industry,” a spokesperson for OpenAI told CyberScoop. “We conducted an extensive investigation and published a detailed report on what happened, what we learned, and how we’re strengthening our security and alignment practices.”

The lawmaker is seeking detailed internal communications, exhaustive technical information and reasoning behind OpenAI leaders’ decisionmaking and activities surrounding the hack by Oct. 1.

“The American people deserve to know the details of what went on in the Hugging Face incident and other incidents of AI models going rogue,” Hawley wrote. 

He accused the company for not providing more details and resources to the third-party auditors who published an independent report on the breach, adding “they had limited visibility into the circumstances leading to the attack and its aftermath.”

Hawley sent his letter to Altman amid a seeming internal chasm within the ranks of AI’s top proprietors over the ways they are allowing the technology to advance mostly unrestrained. He referenced some of these latest warnings in his letter.

Jacob Coxon publicly quit his job as a researcher at Anthropic earlier this week, claiming the company and his previous employer OpenAI are acting irresponsibly and “gambling with our lives.” His social media missive went viral for insisting “the people building AI earnestly believe that it could kill us all by the end of the decade.”

Evan Hubinger, alignment science lead at Anthropic, responded to Coxon’s post in the affirmative, adding that guardrails for superintelligence are lacking and he believes there’s a greater than 10% chance AI could kill all humans within the next decade.

Using those posts as fuel for his inquiry, Hawley questioned what might happen if AI agents hack into critical infrastructure, banks or utilities. Ultimately, he asked Altman: “Who is held liable when AI goes rogue?”

You can read Hawley’s full letter and requested details below.

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Artificial intelligence has removed the skill advantage that once set state-sponsored hackers apart from lone criminals, according to a threat report Anthropic published Thursday that documents misuse of its Claude models across seven areas of harm.

The report, which details activity observed between December 2025 and August 2026, covers cyber operations, influence operations, surveillance, scams and fraud, biological misuse, conventional weapons development and distillation. Anthropic said it disrupted each operation, strengthened safeguards and shared intelligence with authorities and industry partners where appropriate. 

“The cases we share here aren’t typical misuse, but rather examples of the most notable and novel threat activity we’ve identified to date,” the report reads. “We’re publishing this work because we believe we have a responsibility to disclose malicious misuse of our services. As models become increasingly capable, their risks will increase, unless AI developers and society’s defenders act to make them safer.”

The cyber operations the company detailed were a Russian-aligned espionage campaign that hit more than 20 government and defense organizations across Ukraine and Europe, two Chinese undergraduates who ran an automated exploit foundry that produced more than a dozen potential zero-days in a single month, affiliates of the ShinyHunters crime collective who dumped 2,100 cloud access tokens across 40 corporate tenants in 34 hours, and a lone hacktivist who targeted European political parties via stolen API keys. 

For decades, cybersecurity researchers and investigators have pointed to sophisticated operations as a signature of state-sponsored tradecraft, while crude intrusions suggested amateurs or petty criminals. Anthropic posits in the report that AI has erased that conventional thinking, especially since a “majority of the operations described in this report were enabled by AI via direct execution or orchestration.”

“For threat intelligence investigators, sophistication has stopped being a reliable signal of who is behind an operation,” the report said, adding that a hacktivist on stolen API keys, scattered criminals and a state espionage operator each ran campaigns that a year earlier “would have required many skilled operators and specialist knowledge.”

The most extensive case involved a malicious actor using the handle “JackPoterz” whose actions aligned with Russian state espionage, matching behaviors linked to Midnight Blizzard. 

According to the report, the actor employed a custom toolkit composed of two families of Windows-based implants, a mobile exploitation kit, a credential stealing tool that targets browser password stores, a phishing platform designed to mimic priority targets like government organizations, and an administrative console used to manage compromised accounts. Targets included military intelligence bodies in Ukrainian and European governments, diplomatic and defense organizations, and people connected to U.S. foreign policy.

According to the report, AI monitored whether security products flagged the actor’s malware. When a detection occurred, “agents would then set about the process of autonomously modifying and rebuilding the malware to evade the existing detections,” the report said.

The same actor bulk-exported mailboxes at drone component manufacturers and stole a complete software development kit for a drone vision system, then spent days recovering its architecture and details of an unannounced product. The actor also compromised hotel Wi-Fi vendors to reach guests through DNS hijacking, took over WhatsApp accounts with headless browsers, and stole more than 300,000 national identity records from a North African government agency, along with registry data on more than half a million companies.

The Chinese-speaking operators, which the company says were partly carried out by undergraduates at a Chinese university, put Claude to work on vulnerability research around the clock. One workflow iterating on network appliance firmware “yielded more than a dozen possible zero day findings in a single month.” It ran “agent swarms,” in which a lead agent divided work among parallel subagents, and kept campaign memory between sessions. 

Clusters linked to ShinyHunters showed how AI shortens criminal timelines. One supply-chain breach ended with a dump of more than 2,100 Azure access tokens spanning more than 40 corporate tenants in about 34 hours. “AI agents performed nearly all of the work,” the report said. Another compromise moved from a single stolen developer token to full control of a victim’s cloud environment in roughly three hours.

The report also has a section dedicated to distillation attacks that Anthropic claims were carried out since February by seven labs based in China, including Alibaba, DeepSeek, Moonshot AI, Xiaomi and Zhipu. Operators affiliated with Alibaba ran the largest attack Anthropic has measured, peaking “at nearly 3 million exchanges per day launched from more than 3,500 fraudulent accounts” to harvest the outputs of Claude Opus models for training its Qwen systems.

The outputs were culled from users who never knew they were involved. The report said Moonshot and DeepSeek silently forwarded their own customers’ requests to Claude and returned its answers as their own, exposing data users had not agreed to share, including surveillance footage of a tracked individual pulled by a user likely affiliated with the People’s Liberation Army. Those practices are “likely inconsistent with privacy laws and the labs’ own terms of service,” the report said.

Earlier this week, a joint cybersecurity advisory from the National Security Agency, the Cybersecurity and Infrastructure Security Agency and the FBI accused Chinese AI companies of engaging in a deliberate and “systematic” effort to illegally distill U.S. frontier AI models and their capabilities.

Anthropic said it published the cases to give outsiders a view of how these threats form, framing the disclosures as an early look at a shifting landscape. 

“As models become increasingly capable, their risks will increase, unless AI developers and society’s defenders act to make them safer,” the report said. The old idea of “security through obscurity,” it added, “is no longer viable in this new AI-assisted world: everything connected to the internet is a potential target for exploitation.”

You can read the full report on Anthropic’s website.

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The United States and allied governments are “buying time for our systems to become more secure” as artificial intelligence advances and spreads, National Cyber Director Sean Cairncross said Thursday.

“That is a big deal to be ahead of this, to be ahead of this race, and because it’s an exponential  equation,” he said at the Billington CyberSecurity Summit. “Once you fall behind, it is much more difficult to make it up, and so that is the context in which all this is taking place. We are trying to balance the innovation side of running at speed with securing our systems and handling this technology responsibly, which is to say, not letting it fall into the hands of people who would do us harm, our adversaries.”

Earlier this week, U.S. security agencies accused Chinese AI companies of trying to illegally distill U.S. frontier AI models. Also this week, Anthropic disclosed a fourth AI hacking incident where one of its models broke into third-party systems.

“We all face the same threat picture, and it’s vital that we’re working closely together to secure those systems before that technological cycle catches up on the back end,” Cairncross said.

AI has further exposed long-standing cybersecurity problems that have gone unaddressed, he said.

“In AI development, particularly on the vulnerability discovery side and the coding side, it hasn’t created a new set of problems,” Cairncross said. “What it’s done is it’s dragged to the surface problems that have been latent in this space for decades. There’s been under-resourcing and deprioritization of basic cyber hygiene and cybersecurity.”

Cairncross’s messages echoed those of other Trump administration cyber officials speaking this week at the summit.

A top FBI official, Jason Bilnoski, said the solutions to the difficulties AI poses aren’t new; basic cyber hygiene is key.

And the director of the Cybersecurity and Infrastructure Security Agency, Nick Andersen, said historical neglect of cybersecurity fundamentals poses a serious threat that requires a speedy answer.

“We know the worst that can happen, and if we don’t make some very serious, very significant changes in quick succession … you all are going to have to go home and look your family, look your friends in the eye and explain to them how you knew the worst that could happen and why we didn’t do enough,” he said.

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Artificial intelligence is souping up the speed and capabilities of malicious hackers, a top FBI official said Tuesday. And the speed of vulnerability discoveries is forcing organizations to patch more frequently, said another top FBI official.

The officials made their remarks one day before the release of a new FBI cyber strategy Wednesday, which touches on AI, relief and justice for victims and other bureau priorities.

Speaking to both CyberScoop and at the Billington CyberSecurity Summit, Jason Bilnoski, deputy assistant director of the FBI’s cyber division, said AI is “taking actors to the next level.” 

“You’re going to have additional offensive actions coming at your environment, targeting the network at speed and capability,” he said. And he expects it to keep getting worse, with AI-enabled attacks already having a measurable impact, as demonstrated by the numbers in a new section of the annual FBI report on digital crimes.

“The wave is coming. I don’t think we’ve hit the crest yet,” Bilnoski said. “We see an exponential increase in the use of AI, whether it’s nation-state or criminal.”

Still, AI isn’t doing anything that attention to cybersecurity basics wouldn’t prevent, Bilnoski said. It’s something the FBI sees again and again when it conducts investigations, even those with an AI element.

“The adversaries are still [exploiting] basic principles or basic cyber hygiene principles that we are not following,” he said, referring to a recent FBI emphasis on 10 fundamental defensive measures like multifactor authentication. “If we can harden up those top 10 controls that we talked about, it would certainly reduce the risk of both criminal and nation-state targeting of our environment.”

“What will prevent the attacks in the next 18 months are the same things that would have prevented the attacks of yesterday,” he said.

The FBI, meanwhile, will “continue to pursue AI in a way that will help us defend at scale,” Bilnoski said.

Patching pacing

The speed at which AI models are uncovering vulnerabilities means organizations need to rethink their approach to patching, another FBI official said at the Billington event.

“We no longer can essentially do the quarterly patching,” said Colleen Ferranti, assistant section chief, cyber engagement and intelligence section. “We have to evolve with the time, and we have to do more risk-based type patching, and we have to be doing that continuously.”

“So, from our perspective, the day-to-day or quarterly or Patch Tuesday — this needs to be a patch-all-of-the-time, and making sure that we are tracking our systems to also be engaging with that type of technology and at that speed and that level,” she continued.

AI now in FBI cyber strategy

The FBI strategy also has a section devoted to artificial intelligence.

“FBI Cyber will deploy AI-enabled tools to triage large datasets, surface relationships, accelerate malware analysis, prioritize victim notifications, map adversary infrastructure, support attribution, and identify patterns that no human analyst could process at the required pace,” it states. “Consistent with President Trump’s Cyber Strategy for America, FBI Cyber will rapidly adopt agentic AI in ways that securely scale defense and disruption, and will implement AI-enabled tools to detect, divert, and deceive threat actors where operationally appropriate.”

The FBI likewise wants to develop additional tools and techniques, according to the strategy.

“The FBI will continue to develop its Computer Network Operations (CNO) program, providing investigative teams with the court-authorized or otherwise lawfully authorized technical operations tools to remotely collect, conduct surveillance, and disrupt the activities of nation-state and cybercriminal actors when traditional investigative techniques will not achieve the required outcome,” it reads.

The strategy largely reflects a number of existing practices at the agency, such as a focus on disrupting attackers. But one emphasis is on relief and justice for victims.

It contains a “pledge” in support of them: “Pursuing our mission, we recognize that we will encounter unique and novel issues related to privacy and the handling of sensitive data. We will always treat victims with dignity and respect, protect their privacy and data, and rigorously adhere to the U.S. Constitution; applicable laws, regulations, and policies; and the FBI’s Core Values.”

It also promises to quickly share threat intelligence, swiftly respond after incidents and expand “its Industrial Control Systems (ICS) Coordinator program to designate dedicated personnel in every field office.” 

It’s the latest document of the Trump administration to focus on cyber strategy, following the release earlier this year of its overall cyber strategy and the Defense Department’s version expected to publish soon as well.

Clarified 9/9/2026: A quote from Colleen Ferranti has been edited for clarificiation.

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 The U.S. government is accusing Chinese AI companies of engaging in a deliberate and “systematic” effort to illegally distill U.S. frontier AI models and their capabilities. 

According to a joint cybersecurity advisory from the National Security Agency, the Cybersecurity and Infrastructure Security Agency and the FBI, the sheer scale of these efforts since 2024 indicate that distillation is a critical part of China’s AI industrial policy.

“China-based artificial intelligence companies are conducting systematic extraction of proprietary functionalities and capabilities of U.S. AI companies’ models through industrial-scale knowledge distillation campaigns that form the core—not merely a supplement—of their AI development strategy,” the agencies wrote. 

The advisory names Chinese companies like DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI, saying they spent billions of tokens across millions of exchanges and requests with frontier U.S. AI models like Anthropic’s Claude, OpenAI’s ChatGPT, Google Gemini, and xAI’s Grok, since at least late 2024.

The U.S. agencies said the companies used data culled from these interactions to strengthen their own domestic models, a practice that is tacitly encouraged but not directed by political leaders in Beijing.

DeepSeek, for example, distilled frontier U.S. models to generate synthetic training data for its R1 and R3 models, including four different versions of Claude, two versions of Gemini, five versions of ChatGPT and Grok 4. Those models helped train DeepSeek’s capabilities in areas like agentic functioning, question and answer optimization, creative and occupational writing and others.

Another Chinese company, Moonshot AI, allegedly distilled 18 different U.S. models – including Fable 5, Anthropic’s current, most advanced commercially available model – to train its Kimi-K2 and Kimi K3 models. The company used millions of queries meant to extract enhanced capabilities in areas like agentic reasoning, coding and data analysis, computer vision, larger logical frameworks, visual processing and others.

Chinese AI companies manage a sophisticated set of tools and systems that route requests and prompts through multiple pathways to avoid detection.

The advisory lists common tactics observed by Chinese companies, including spreading requests across different accounts, models and platforms, using native APIs, remote cloud providers, and third-party aggregators to obfuscate user metadata, and leveraging proxies and gray tech markets to get around geographic restrictions, terms of use and safeguards built into frontier models.

“Addressing industrial-scale distillation merits a coordinated response across the AI ecosystem, including effective information-sharing, spanning the U.S. Government, private industry, and allied nations,” the advisory stated.

For decades, U.S. national security officials and western business leaders have accused China of leveraging cyberattacks, insider threats and other forms of economic espionage to pilfer proprietary or sensitive technologies from U.S. businesses.

In June, Michael Kratsios, White House head of Office of Science and Technology Policy, made a similar accusation about MoonshotAI of distilling Fable 5 to train its own models, and described a similar “sophisticated” system for evading guardrails and restrictions on usage.

The warning Tuesday levies similar charges about Chinese theft of American tech, but for frontier AI companies that are facing lawsuits themselves from artists, authors, media organizations and other parties who say AI companies illegally trained their models on copyrighted or trademarked work.

Even within the competitive AI industry, companies and open-source organizations commonly share weights and measures for AI systems, or distill other AI systems in the course of legitimate work or research.

The agencies acknowledge this reality, but claim that Chinese companies are engaged in “aggressive, malicious, and targeted distillation activities at an industrial scale.”

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CIA Deputy Director Michael Ellis said Tuesday that the agency’s role in Operation Absolute Resolve is an example of how it has moved to put cyber operations at the center of intelligence collection and field missions, rather than treat them as a separate technical service.

Speaking at the Billington Cybersecurity Conference, Ellis said the agency’s cyber teams built the intelligence picture that supported the operation, which he said allowed U.S. special operations forces to locate and apprehend Nicolás Maduro.

“That operation was only made possible by a flawless intelligence picture, and that flawless intelligence picture was built on cyber operations enabled by our [Center of Cyber Intelligence] team,” Ellis said.

Ellis said the intelligence enabled U.S. forces “to identify the location of Nicolás Maduro and apprehend him within four minutes of landing on the ground.”

He provided no details about the methods, systems or sources involved. He also did not explain how cyber intelligence established Maduro’s location or how the agency confirmed the information before the mission. 

Reports from the operation, including statements made by President Donald Trump, stated that power outages during the operation were the result of a cyberattack. Experts told CyberScoop in the aftermath that cyber operations may have been involved, but the visible physical attacks that were also part of the operation alone could plausibly explain the outages.

Still, Ellis used the operation to support the agency’s decision to elevate the Center for Cyber Intelligence to a full mission center. Earlier this year, CIA Director John Ratcliffe said the agency reorganized several of its key acquisition and tech directorates to better embrace emerging technologies like artificial intelligence and quantum computing as they reshape “the reality of conflict and asymmetric warfare.”

Ellis reiterated Tuesday that new status gives cyber work a more direct place in the structure the agency uses to organize people, money and technology around major intelligence goals.

“By elevating our Center for Cyber Intelligence to a mission center in our CIA organizational construct, it’s allowed us to both focus priority on the cyber mission as well as to better align resources around that mission,” Ellis said.

The change could reduce the distance between cyber specialists and the officers who plan and carry out intelligence missions. Cyber teams can collect information from foreign digital systems, while analysts compare that material with reporting from human sources, satellite imagery and other forms of intelligence. Mission planners can then turn the combined findings into information that operators can use.

Operation Absolute Resolve, as Ellis described it, shows that process in practice. The cyber component provided direct operational support by producing intelligence about a specific target within the limited period available for U.S. forces to act.

“Without that kind of operational picture being driven by cyber, we wouldn’t have been able to help enable U.S. Special Forces,” Ellis said.

Ellis further said the CIA’s work in Absolute Resolve was an example of why its reorganization needed to be mission-based, which, given the cyber center’s independent status, allows the agency to direct staff funding and technical support toward operations that require close coordination among cyber specialists, analysts and field officers.

The CIA also created a Directorate of Mission Systems to speed the delivery of technology across the agency. Ellis said its guiding purpose is “to deliver tech to our workforce to enable mission rapidly and efficiently.”

The two changes address linked parts of the same process. The cyber center organizes technical collection around intelligence targets, while the new directorate helps provide the tools needed for that work. Their value depends in part on how quickly the agency can acquire and deploy technology as software, security systems and foreign networks change.

Ellis said the CIA previously took an average of two to three years to bring new technology into use. The agency has since set a six-month acquisition target and completed more than 400 purchases within that period, he said.

“Waiting two or three years is simply too long,” Ellis said.

Artificial intelligence has increased the pressure to shorten that timeline. Ellis said AI can reduce tasks that once required hundreds of hours of work to hours. It can also help analysts process large sets of intelligence, identify patterns and connect information that would be difficult for people to examine at the same speed.

“In cyber operations, it brings speed and scale that would be unimaginable without these AI tools,” Ellis said.

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AI is getting better at much of what security teams have long spent time on: analyzing information, identifying patterns, and providing technically sound recommendations quickly. As those capabilities become more routine, they are changing what security practitioners spend their time on.

Reaching a technically sound recommendation is also getting easier, which puts more weight on the judgment about what to do with it. A recommendation can make complete sense from a security perspective and still carry consequences for the systems, people and business around it that change what the right decision is.

Experienced practitioners bring context an AI system usually lacks: how systems are actually used, which parts of the business depend on them, what happened during previous incidents, and what an action is likely to set off. That context often changes what a team decides to do next.

This matters for security leaders as they hand AI a larger role in operations. They are the ones deciding where it can act with more freedom and where human judgment stays in the loop. Some of the hardest calls start with analysis that is technically sound, because the information available to the AI may not include enough context about that particular environment.

Security teams face this daily. For example, a critical vulnerability with a public exploit may need to be patched immediately. But if it affects a line controller or a medical device running under vendor certification, an unscheduled reboot could stop production or create a regulatory issue. The environment determines how and when the team should respond.

The same applies to suspicious infrastructure. An IP address tied to malicious activity may also belong to shared cloud infrastructure or a content delivery network that business services depend on, and blocking it would take those services down with it.

Context changes the decision

Experienced practitioners know things about their environments that never made it into an asset inventory, a runbook, or any dataset AI can reach. They know the unimportant server still supports a critical business process. They remember that isolating one network segment during a previous incident took down another service. They can also tell that activity which looks hostile is really an authorized red team, a security test, or scheduled vendor work.

In one case, for instance, a service account showed authentication activity far above its baseline, baseline was connecting from an unfamiliar host at 3 a.m. The recommendation was to disable it pending investigation. An experienced analyst checked the account’s activity and noticed the same spike, host and timing four times a year, during the quarterly close. The activity was statistically unusual and completely normal for that particular business process. Disabling the account would have stopped financial settlement mid-run and cost the team days of manual reconciliation.

This is one of the decisions CISOs now face as they expand AI’s role. How much autonomy to grant a system should not rest mainly on model confidence or threat severity, since neither tells you what happens once the recommended action is taken. Reversibility and blast radius are the better test, and they need to be assessed separately. Isolating a domain controller is reversible by reconnecting it and doing it at the wrong moment can cause an organization-wide outage.

Low-impact, reversible actions are better candidates for greater autonomy, with safeguards in place. More scrutiny makes sense when actions are difficult to reverse. They have a broad potential impact, cross legal or trust boundaries, affect systems beyond the evidence available, or reduce the organization’s ability to investigate what happened.

AI models and their capabilities will keep changing. Security leaders still need to understand the potential impact of the actions they allow them to take.

Look at what people actually do

AI can leave an analyst with dozens of recommendations to review in the time they once spent investigating a handful of cases. Each analyst now has more decisions to make. Organizations need to measure what happens to those decisions.

The KPI you choose determines the behavior you get. Make automation rate the focus, people have an incentive to approve more. Make mean time to resolution the focus and people close cases faster. Neither measures whether the decisions improved. A 90 percent automation rate tells a CISO very little on its own. What matters is what happened in the 10 percent of cases where someone stepped in.

Leaders should look at what happens when a recommendation reaches a person. Whether the analyst approves, edits, or rejects it can tell you more than the automation rate alone. The time spent on the review matters too, along with whether the analyst’s intervention changed the outcome.

AI recommendations can be harder to review because they may arrive already looking well supported. The explanation is fluent, uses the right terminology, and points to evidence that looks credible, even when it does not fully support the conclusion. The signals experienced practitioners relied on to spot weak analysis can become much harder to see.

Under-reliance deserves attention, too. An analyst who second-guesses correct recommendations without adding anything reduces the efficiency AI was meant to provide. Approval latency is a useful signal here. A long queue of recommendations approved almost instantly, especially when people are under pressure, should prompt leaders to check how much review is actually happening.

AI recommendations can be harder to review because they can look convincing. They may use the right language and point to real evidence. The reviewer still needs to check whether the evidence actually supports the recommendation.

For CISOs expanding AI in security operations, a human approval step in front of every automated action is not enough. Leaders need to know what happened during the review, not just that someone approved the recommendation.

Build autonomy policies around reversibility and blast radius. Track what people actually do with AI recommendations, and test whether oversight works by deliberately introducing known-wrong recommendations into controlled workflows.

False negatives need particular attention. A false positive generates something the team can investigate. A confident false negative generates nothing, and the absence of a finding can feel reassuring. An AI-generated all-clear should be treated as a claim requiring evidence, particularly when the consequences of missing something are significant.

As AI takes on more of the initial analysis, practitioners will face more decisions that require context and experience. Security leaders need to make sure that judgment remains part of how their teams work. Getting to a technically sound recommendation faster only helps if the action that follows makes sense for the environment.

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Last week, more than 100 companies and organizations published an open letter calling for a rapid acceleration of cyber defense capabilities to combat the capabilities of AI. The list reads like a procurement catalog. Microsoft, Google, AWS, Cisco, IBM, CrowdStrike, Cloudflare, Anthropic, Okta and Fortinet are on it, alongside buyers like Mastercard, Visa and Capital One. The public signatory page has since passed 200 companies and organizations, with some such as 1Password, Sophos and Prophet Security, have already published posts of their own detailing their commitment to defenders.

The explanations are worth sitting with. Letters turn into marketing assets faster than they become company concrete action. The buyers decide which one becomes reality.

The diagnosis is correct

I want to be careful about how the skepticism below reads, because the letter has the substance right. It opens by arguing there is a limited window to strengthen defenses before AI-enabled attacks become widespread. There is data to back that up. CrowdStrike’s 2026 Threat Hunting Report, covering January through June 2026, found that 88 percent of the exploitation it observed against vulnerabilities with a public proof of concept occurred within 48 hours of that proof of concept being published. In the four days after the React2Shell disclosure, the same team logged more than 800 hunting leads across over 80 victim organizations.

Forty-eight hours is shorter than most change windows. Anyone who has sat through a Thursday patch approval meeting knows what that does to a quarterly remediation cycle.

The shrinking timeframe is real.

What the document does not contain

The letter lays out three principles and addresses four audiences: cybersecurity companies, governments, frontier AI companies, and every other organization. While it reads well, it carries no deadlines, dollar figures, measurable targets or expiration date. There is nothing to measure, therefore, there is no way to determine the initiative’s failure.

There is something else worth calling out. Several of the firms warning about AI-enabled attacks are selling AI-enabled defense into the same budget cycle. Both OpenAI and Anthropic have been touting their cybersecurity-focused models since the spring, and most of the large security companies on the signatory page sell their own AI defense product. Those are commercial products competing for the same security budget the letter is asking you to expand. The letter’s warning doesn’t suddenly become false, and I don’t think it was written in bad faith. However, both the warning and the pitch arrived in the same envelope, and a buyer who reads only one of those messages will overpay.

The one line worth extracting

Buried in the section addressed to cybersecurity companies is the only sentence that behaves like a standard. The letter asks those companies to “share threat intelligence and tested playbooks, and measure progress by how many organizations are protected, how quickly attacks are contained, and whether fixes work.”

That is three metrics. Coverage, containment speed, and verified remediation. Every security vendor on the signatory list endorsed them in public, under its own logo, in a document it chose to promote.

The section addressed to every organization gives buyers the matching instruction—raise the security bar for what you buy, build and deploy, including AI-generated code.

Put those together and the rubric was already in the room. It just came in through public affairs instead of procurement.

Five questions for your next renewal

Take the letter to the vendor that signed it. Ask for evidence against its own asks.

What share of your installed base is actually running the AI-enabled defenses described here? What does that capability cost above the current contract? Coverage claimed in a letter and coverage sold in a SKU are rarely the same number, and the gap between them is where the upsell lives.

What is your median and 95th percentile time to contain, measured your own telemetry, this year versus last? The letter says to measure containment speed, so any vendor that signed it has already agreed the question is fair.

What is your retest rate, and how many remediations failed verification on the first attempt? “Whether fixes work” is the third metric in that sentence, and the one almost nobody reports.

The letter commits signatories to making AI-powered defense deployable for critical infrastructure operators with hands-on help. What does that program cost a 200-bed rural hospital, and how many are enrolled today?

Finally, turn the buyer instruction back on the seller. What proportion of your own product is model-generated code, and who reviews it before it reaches my environment?

All of the signatories endorsed every idea across all five of these questions.

The honest read

None of this argues against the letter. Coordination documents do real work: they create a public position people can be held to eighteen months later, and the diagnosis in this one is more candid than most vendor marketing on the subject. Signing cost nothing as of Aug. 27, which is why more than a hundred organizations were willing to do it.

The cost shows up at renewal, and only if someone on the buying side treats the signature as a commitment. Otherwise, it is a logo on a webpage and a line in a blog post nobody reopens.

Two hundred companies agreed to measure progress. Put the question in your next renewal and one meeting will tell you which of them meant it.

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The Trump administration rolled out a six-month test program in Texas on Monday that will draw on volunteer expertise and technology from cyber and artificial intelligence companies to protect the water sector.

“Project Watershed 250” is the first of the state-based, industry-centric pilot projects to cross the finish line that the Office of the National Cyber Director has previewed since the publication of its national cybersecurity strategy earlier this year. Water systems have long been viewed as among the most vulnerable and neediest critical infrastructure sectors, and in recent months the sector has been the victim of a spree of attacks.

“Project Watershed 250 is a commitment from the states, industry and federal government that we will continue to prioritize our nation’s safety and deliver on America-first policies for the American people,” National Cyber Director Sean Cairncross said at a rollout event in San Antonio Monday.

“U.S. companies are providing world-class cyber capabilities, red teaming that tests utilities’ current defenses, system hardening using the latest private sector cyber tools and AI tooling that helps utilities’ frontier cyber defenders to protect Texas water systems and scale proven solutions across the country,” he said. “This six-month pilot program is designed to make our water and wastewater critical infrastructure more resilient and resistant to cyber attacks by proactively finding and fixing system weaknesses.”

The pilot program, featuring collaboration between federal and state governments, stands in contrast to how the Biden administration tried to tackle the issue, with audit requirements that some GOP states challenged in court, forcing Biden’s Environmental Protection Agency to withdraw its rule.

“For too long, at least on the federal level, the government has admired the problem of cybersecurity in water systems,” Cairncross said. “We are going to find out what works. We’re going to target that, and we are going to scale off of this and learn lessons.”

A dozen companies — Parsons, Microsoft, Fortinet, Google Cloud, Palo Alto Networks, Amazon Web Services, Reflection AI, Cloudflare, Zscaler, Forescout, Abnormal AI and Dragos — appeared at the rollout Monday to praise the initiative and tout their contributions to it.

Not everyone praised the initiative elsewhere, however. One cyber professional who works on water security issues, speaking on condition of anonymity, said the program was “all smoke” and that “there’s no real money behind it.”

“The White House did what it always does — reached out to industry with their hands out asking for industry to pay for things the government should be doing, at least in part,” the person said.

Texas Gov. Greg Abbott said the program would be overseen by Cairncross’s office and Texas Cyber Command, which was established just last year. Abbott cited the need for the program by mentioning “an Iranian-backed cyberattack” on 30 water systems across 12 states and a 2024 attack on the water system in Muleshoe, Texas, suspected to be the work of Russian hackers.

“The need for cyber resilience is overwhelming,” Abbott said. “Many rural providers simply don’t have the resources they need to be able to protect themselves.”

Watershed 250 isn’t the only federal effort to bolster water cybersecurity, with lawmakers introducing legislation in the aftermath of the recent attacks. Past legislation that Congress has enacted also sought to tackle the problem.

Updated 8/31/26: with comment from cyber professional.

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Unit 42’s top brass has seen enough from internal frontier AI model testing and malicious in-the-wild use of commercially available AI tools to be genuinely concerned.

“I can tell you without exaggeration that we believe that this is a generational shift in cybersecurity,” Sam Rubin, senior vice president of Palo Alto Networks’ threat intelligence arm, said in a media briefing Wednesday. 

A period of relative balance between security and exposure has been broken by frontier AI model capabilities that could allow attackers to find and exploit network weaknesses with speed, Rubin said.

Unit 42 warned that capabilities demonstrated by readily available agentic AI models, and those unlocked by frontier AI models that remain gated for defense, have shifted the balance of power from defenders to attackers.

“The defenses that we’ve had built up over years weren’t necessarily built for or prepared for these machine-speed attacks,” Rubin said. “Organizations are ill-equipped to detect and to respond quickly in the face of these attacks.

Back in April, when Anthropic brought Palo Alto Networks and other major technology companies together to form Project Glasswing, an initiative to find and address security defects with its Mythos model, Unit 42 estimated the same capabilities would be in the hands of attackers within a year. 

“Well, here we are five months later, and we’re starting to see the early waves of this threat in the wild,” Rubin said. 

Unit 42 is actively investigating an attack on one of its customers where an attacker used an agentic framework to exploit 50 applications and other weaknesses across the enterprise in less than 10 hours. Rubin estimates AI allowed the attacker to accomplish in 10 hours what would have taken at least 10 days in a pre-AI era. 

Attackers are already using AI across the entire attack chain, said Sherrod DeGrippo, vice president of threat intelligence at Unit 42. “We are not far from fully agentic attacks across all at once, but right now it’s piece by piece by piece,” she said. 

“AI has seeped into every part of what threat actors do,” including malware development at scale, delegation, social engineering and ransomware negotiations, DeGrippo added.

As such, she sees the threat landscape shifting in four areas. AI is a force multiplier, identity is the primary compromise vector, attackers are burrowing into foundational libraries and software supply chains “baked into the fabric of our digital world,” and nation-sponsored threat groups are learning more about points of weakness in enterprise systems, DeGrippo said.

Nobody is fully prepared for what’s coming and it would be naive or a bad defender mindset to think otherwise, she said. “This is a transformative period, and how organizations navigate that transformation is going to be make-or-break for a lot of them.”

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