Members of Congress are pressing the Trump administration to expand the federal government’s role in securing artificial intelligence, wanting new safety standards and regulations, independent testing of models and more “aggressive” use of AI for defensive cybersecurity purposes.

In a letter, Sens. Maria Cantwell, D-Wash., and Mike Rounds, R-S.D., said that multiple incidents of frontier AI models escaping sandbox protections and breaching real companies, organizations and governments necessitate a stronger federal role in regulating the technology.

Recent revelations that hundreds, and possibly “tens of thousands,” of such incidents may have occurred for months without OpenAI’s staff detecting them makes the need for federal intervention even more urgent, the senators argued.

“These incidents come as AI systems are becoming dramatically more capable at discovering software vulnerabilities, writing and executing code, using digital tools, and operating autonomously,” the senators wrote.

The letter, addressed to Jay Clayton, director of national intelligence and new White House “AI czar” and Michael Kratsios, director of the Office of Science and Technology Policy, urges the federal government “to bring its full capacities to bear on this challenge, building a coordinated national response around standards, testing, auditing, and defensive AI.”

The Center for Advancing Innovation and Standards for Super Intelligence (CAISSI), housed within the Department of Commerce, has become the Trump administration’s main body for evaluating and testing AI models. In the letter, the senators are seeking to expand the center’s resources and mandate.

Under the Trump administration, testing of frontier models has been voluntary and focused on evaluating model capabilities, not restricting them. The administration has since said it has reached a safety testing framework with OpenAI, Anthropic and other AI companies to govern safety testing, but has not made the agreement public.

The senators call for CAISSI to spearhead the development of “technically grounded and measurable standards and risk management frameworks for advanced AI safety and security.” Those standards would address potential risks like cyberattacks, automated attacks on critical infrastructure and other threats.

“The standards should also address the security of the environments in which advanced AI systems are developed, trained, tested, and used internally, including protections against agents escaping testing environments, gaining unauthorized access to external systems, or taking actions beyond their intended permissions,” the senators wrote.

The letter argues the center and federal technology hubs like the National Institute of Standards and Technology should hire additional expertise in AI, cybersecurity, model evaluation and national security.

It also calls for a much broader community of independent and third-party testers. Currently, that testing is done almost entirely by AI companies and hand-selected auditors, like Model Evaluation and Threat Research (METR), Redwood Research and others.

Last month, Chris Painter, president of METR, told Congress that the firm’s investigation of the Hugging Face hacking incident was at the invitation of OpenAI, which tightly controls which organizations and testers get access to its unreleased models. While they are permitted to publicize some findings, such as those that relate to potentially catastrophic risks to humanity, researchers are otherwise restricted from discussing other parts of their investigation through nondisclosure agreements.

Additionally, Painter suggested that frontier companies are relying too heavily on their own technology, rather than technical controls, to monitor and restrain their models in testing.

“Because of the scale and speed at which AI agents are deployed, companies depend on AI monitoring and controls instead of human supervision, to prevent unwanted agent behavior,” Painter said. “That is, monitoring of AI is now in large part done by other AI systems.”

The senators call for CAISSI, national laboratories and the Department of Energy to become federal testing sites. There, the government would evaluate AI models for advanced cyber capabilities, biological or chemical threats, threats to critical infrastructure, sandbox evasion techniques and other autonomous rule-breaking behavior. They also want testing opened to universities, independent research organizations and “qualified third-party evaluators,” including private sector companies.

Other federal bodies, like the National Security Agency, the Defense Advanced Research Projects Agency and National Science Foundation can help develop new standards and testbeds for AI tools used in defensive cybersecurity that can then be shared across government and with the public.

“The recent incidents make clear that the security infrastructure surrounding advanced AI must advance as quickly as the technology itself,” the senators wrote. “The United States has an opportunity to lead both sides of this challenge: building the standards, testing, and auditing infrastructure needed for increasingly powerful AI, while harnessing those same capabilities to make American networks harder to attack.”

You can read the full letter here.

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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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