After a year and a half spent downplaying calls for AI safety regulations, the Trump administration has sharply reversed course, embracing a level of government scrutiny of frontier AI systems before public release–a far stricter stance than the Biden administration took.

An executive order designed to be friendly to the AI industry was meant to let the federal government briefly review some new models on a voluntary basis.

When the Trump administration, suddenly and without much warning, slapped export controls on Anthropic’s Fable 5 and Mythos 5 in response to private sector threat intelligence reporting, the U.S. AI industry officially entered its regulatory era.

But key questions and gaps remain. It’s not clear why the administration drew the line where it did, or whether they will move it again in the future.

While newer models like Mythos and OpenAI’s Daybreak do have stronger cybersecurity capabilities, the private sector reports the administration relied on describe capabilities already available in older commercial, open-source and Chinese models that nearly anyone can access.

CyberScoop spoke with current users of the latest frontier models, including OpenAI’s ChatGPT 5.5 and Fable 5, to learn more about what these models are currently capable of in offensive and defensive cybersecurity.

Cybersecurity experts and former government officials say the administration may be playing catch up on threats that have been building for years as it has more fully realized the national security implications of the technology.

Are the models breaking new ground or just breaking things? 

Users of Chat GPT 5.5, introduced this past April, and Fable 5 tell CyberScoop those models have been largely helpful to their work, even as they complained about high token usage and safety guardrails that hinder,  but don’t meaningfully prevent, defensive cyber tasks.

Eyal Webber Zvik, chief strategy officer at Cato Networks, a cloud and cybersecurity network provider in OpenAI’s Trusted Access in Cyber program, said they use GPT 5.5 and later OpenAI models to scan and triage internal codebases for vulnerabilities, test new safeguards and provide “highly autonomized service” to their customers.

Zvik wouldn’t disclose how many bugs 5.5 has found but said the company’s view is that it helps both find bugs that humans missed and rank which ones to patch based on factors like each bug’s exploitability.

“It is now a native part of our development environment and cycles, and we use those models to scale our entire codebase and make sure what we release into the service that our customers use to run their networks and network security has the least likelihood of having any vulnerabilities that can be exploited,” said Zvik.

John Hopper, vice president of engineering at SpecterOps, an identity security company, said newer models like GPT 5.5 are sharper and more persistent in pursuing their tasks.

“That can be a good or bad thing,” he noted.

One metric that SpecterOps tracks is how long it can keep a particular agent working before it moves off task or fails. That metric “matters a lot” because the longer an agent works without human help , the more agents a single operator can run at once.

Hopper said this provides defenders with immense value, and pushed back on the idea that the offensive capabilities the models offer are automatically more beneficial to malicious hackers. There is “a modicum of grounding that the industry needs when we talk about these models.”

“Yes, AI frontier tools will lower the barrier of entry, but these problems have always existed,” he said. “I don’t actually believe that AI is going to remove the needle in the haystack problem, but by howdy, using my two hands to find that damn needle, compared to using a backhoe, I can tell you which one I’d rather be driving.”

Eran Kinsbruner, vice president of product marketing at software security firm Checkmarx, told CyberScoop that later models like OpenAI’s Codex Security and GPT 5.5 are noticeably easier to set up and run with local systems, even for less technical users. That alone gives them an edge over many cybersecurity tools where interoperability is a constant concern.

However, GPT 5.5 burns through tokens at a much faster rate. He recalled one instance of using it to scan a medium-sized repository in three different programming languages.

“After 26 minutes I almost ran out of tokens, and it didn’t provide anything, just created a threat model for me and told me you want to buy more tokens?” he said.

In other instances, some of the scan results he received were not comprehensive.

Further, he expressed frustration with some of the guardrails designed to prevent risk – like only allowing users to scan local files but not code repositories like GitHub – “makes not too much sense” given how often developers must work with remote code.

Those kinds of guardrails – which can prevent models or developers from injecting malicious code or prompting into their models – sit at the heart of the debate in Washington D.C. and around the world. Some users feel differently about their utility.

Kinsbruner said that doesn’t make sense for organizations like his, which work with thousands of different enterprise organizations with  thousands of different code repositories spread across the internet.

“I cannot imagine how large-scale developers could just jump into this solution and make it an enterprise-grade, enterprise-level, de facto cybersecurity solution” out of it, said Kinsbruner.

OpenAI did not respond to a request from CyberScoop for an interview on GPT 5.5. The company has since released another model, GPT 5.6, that they said is more efficient at token use.

The White House’s crash course in AI cyber risk 

 The White House keeps changing its line on whether and how the U.S. government should limit the release of commercial frontier models. The shift comes from lessons learned since coming into office in Jan. 2025. Trump threw out Biden-era regulations meant to steer the industry toward safer models. Top officials like Vice President JD Vance argued against restricting industry progress.

Less than two years later, administration officials worry about the impact of speed and scale – two things AI excels at – in cyberspace.

According to Will Loucks, senior director of intelligence at the Office of the National Cyber Director, over the past two years the number of exposed and known vulnerabilities has shot up. Threat actors exploit those flaws faster before defenders can fix them. Once inside, the time from initial access to full network control shrinks.

“So in other words, every stage of the cyber operations lifecycle that a threat actor has to move through to get to a victim network and achieve an outcome, they’re just moving through more quickly faster,” said Loucks at a July 16 event in Washington D.C.

Speaking about AI in particular, Loucks said one of the defining characteristics of the technology is its ability to lower barriers for threat actors.

“Sometimes speed and volume have a threatening aspect alone, even if sophistication isn’t quite increasing in the same way, and the reason for that is because it places pressure on defenders…to triage alerts more quickly,” he said.

Jordan Rae Kelly, former director for cyber and incident response on the White House’s National Security Council during Trump’s first term, told CyberScoop that the changes over the past two years reflect the lessons the White House has learned on the issue since returning to office.

In the early days of this administration, Kelly said, “there is a sense and a spirit that the Biden administration was limiting AI and there was a kind of a rip-it-all-off [attitude], everybody go and do whatever, we will be the biggest and boldest and brightest.”

“I love that talking point, but I think what you’ve seen is probably an education over the last 19 months, where people [in the White House] have said that’s a challenging premise to put into place, knowing about the potential downsides and capabilities,” she added.

Michael Daniel, former White House cyber coordinator under President Barack Obama, thinks the horse may already be out of the barn.

Daniel, now head of the Cyber Threat Alliance, a membership nonprofit group focused on cyber threat information sharing between industry and government, said his members report that AI is being used to do things “faster and at a slightly bigger scale” but aren’t yet seeing the flood of exploitation that analysts have warned about. Not yet.

“I think what we’re seeing right now [and] talking about is ‘okay, where are the step changes [in the cyber threat landscape] actually going to occur?” said Daniel. “Are we and when will we see the explosion in vulnerability reporting from these Mythos-like capabilities? That’s what’s really got their attention right now.”

But Mythos and OpenAI’s Daybreak models are restricted to select organizations, and neither has publicly released its most powerful cybersecurity models to the public. That dynamic won’t last.

The UK’s AI Security Institute estimates that open source and foreign LLM models are between 4-7 months behind frontier U.S. models. In that setting, it’s hard to stop the development of AI models worldwide through export controls or other limits.

“It’s not like we’re buying ourselves five to ten years on this,” he said. “We’re not, and so I’m not sure the impact on the defenders who are trying to obey the law is worth whatever small hiccup we cause for our adversaries.”

Kelly said there’s merit to the administration’s current position, even if it took time to get there. Many federal cybersecurity procedures that operated even a decade ago – such as a Vulnerabilities Equities Process that could take days or weeks to consider the pros and cons of keeping an exploit – are no longer practical.

“All of that work to some degree, is out the window, because you can’t meet with the regularity you would need to meet to adjudicate vulnerabilities that are being found in seconds and exploited in minutes,” said Kelly.

But Kelly and others say that’s also because AI capabilities in cybersecurity are developing faster than policymakers can react, even in the best of times.

Key questions remain and the administration’s balance between national security and backing domestic industry will likely shift  in response to new events.  The administration wants a framework that can predict and manage the risks of AI models today and tomorrow. That may be harder than it sounds.

“Do I think they’ve been clear? No,” said Kelly. “But I think it’s a place where clarity is really hard to achieve.”

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President Donald Trump said he would postpone the release of an executive order that would set up a 90-day testing and vetting regime for frontier AI models, hours before the White House was set to publicly announce the signing. 

Speaking to reporters in the Oval Office Thursday, Trump said he opted to delay the order “because I didn’t like certain aspects of it” and expressed concerns that it could harm U.S. AI industry competition with countries like China. 

According to multiple sources, a draft version of the order circulating in the last 24 hours would have set up a voluntary testing regime between the U.S. federal government and frontier AI companies that would allow the government to study new models for 90 days before they’re publicly released. In addition to the government, the draft order would also facilitate access to the models for cybersecurity testers in critical infrastructure sectors, like finance and healthcare.

The draft order empowered the National Security Agency to conduct classified evaluations of frontier AI models, while the Department of the Treasury would have set up a new information sharing agreement between AI companies and cybersecurity defenders in critical infrastructure.

Other agencies, like the Office of the National Cyber Director, the Cybersecurity and Infrastructure Security Agency and the National Institute for Standards and Technology, would also be involved in defining which models are covered under the vetting regime.

In some sense, the order would formalize an already cooperative relationship between AI companies and governments like the U.S. and UK, where tech-focused agencies and regulators have already been provided access to previous models ahead of their release for testing and evaluation. 

A former federal official who has seen the latest draft circulated before Thursday’s announcement told CyberScoop that based on their conversations with the administration, the order was intended to facilitate more robust testing from government agencies compared to evaluations conducted for previous models. They said that is in part a reflection of the federal government’s maturing understanding of AI technology over the past five years.

“In the past there has been containerized optionality for the intelligence community and others to take a look at things, but it was really a lot of hand holding [from AI companies] and self-explanation of what they expect this thing to do,” said the official, granted anonymity to discuss sensitive conversations with the administration. “And now the government is coming forward and saying now we feel we’re prepared enough for you to just give us your tool…and we’ll go from there.”

But it also represents a stark pivot by the Trump administration, which came into office openly dismissive of AI safety policies and arguing that they would inhibit U.S. industry. Trump’s latest comments in delaying the order echo those same attitudes. 

The former official said that while the Trump White House doesn’t view its mission as telling AI companies “don’t develop AI that can do X, which was perceived to be the previous administration’s role,” they also acknowledged the administration’s early rhetoric on AI regulation has painted them into a corner. 

“I think the biggest challenge the administration has is that their tone was ‘no institution of guardrails’ and they don’t have a better word for making sure that the capabilities of emergent frontier models don’t disrupt security than to say ‘let’s test it and institute guardrails,’” the official said.  

While debate about how best to regulate AI-related harms continues, most agree there are genuine national security concerns around the technology.

Ram Shankar Siva Kumar, founder of Microsoft’s AI red team, told CyberScoop that in 2019, his staff consisted of himself and a few other security and machine learning specialists. Now a much larger staff of technologists are supported by specialists in psychology, linguistics, bioweapons and other fields.

“Because of frontier harms, what we have done has really morphed,” Siva Kumar said.

The United States, along with Israel, Russia, Ukraine and others have already deployed AI in targeted military operations or integrated the technology into their larger command and control structure. AI is being used to supercharge drone warfare, global hacking campaigns, and sophisticated surveillance and targeting of military personnel and civilians, imbuing the engineering choices of frontier AI companies with life and death consequences.

Some congressional members who previously opposed allowing AI to make autonomous kill decisions on the battlefield have been reconsidering their position.

Rep. Don Beyer, D-Va., who co-chaired the Congressional AI Caucus and was appointed to a bipartisan AI task force in 2024. said that while he thinks “we need to guard against dehumanizing” those decisions, he also worries that adversarial countries will use the same technology against the United States.

“It’s like if we say that Americans have to have a human in the loop and the Chinese don’t have to have a human in a loop, the non-human one will beat the human one every time,” Beyer said at an AI conference in Washington D.C. earlier this month.  

Meanwhile, experts have been increasingly concerned about the technology’s impact on cybersecurity, as current models are remarkably good at finding software bugs and vulnerabilities, while newer models like Anthropic’s Mythos and OpenAI’s Daybreak are capable of chaining together multiple exploits to conduct more sophisticated attacks.

While state-sponsored hackers are experimenting with the technology and using it to gain targeted efficiencies in their hacking operations, cybersecurity experts in the private sector and law enforcement agencies say the technology has mostly benefitted cybercriminals and scammers.

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