How AI guardrails are impeding the work of offensive cybersecurity researchers

Published 24 July 2026 · tech

For months, AI giants have devised special vetted programs and strict guardrails to limit the use of their models by malicious hackers. But these limits

For months, AI giants have devised special vetted programs and strict guardrails to limit the use of their models by malicious hackers. But these limits are now hindering the work of legitimate network defenders, as well as that of offensive cybersecurity researchers. In June, the U.S. government slapped export control restrictions on Anthropic’s much-hyped AI models Mythos and Fable. The move was prompted at least in part by a report that claimed it was possible to bypass the models’ guardrails designed to prevent users from using them to build and execute malicious cyberattacks. Regardless of whether the incident was really motivated by fears of a jailbreak, the fact is that Anthropic has repeatedly marketed Mythos as some kind of doomsday cybermachine that can only be given to carefully vetted users, and even then with strict guardrails in place. (The export controls on Fable 5 and Mythos 5 have since been lifted. Fable 5 returned to general access on July 1; Mythos 5 has been reintroduced only to vetted U.S. organizations as part of the government’s review process.) That kind of gatekeeping isn’t unique to Mythos. Both Anthropic, with its other models, and OpenAI offer cybersecurity researchers programs they can apply to get vetted and — if approved — access models with fewer cybersecurity restrictions: OpenAI’s Trusted Access for Cyber and Anthropic’s Cyber Verification Program. These guardrails have been widely criticized, particularly by researchers whose job is to find unknown vulnerabilities in systems and devise ways to exploit them before criminals do. During a recent appearance on a cybersecurity podcast, Mark Dowd, a well-known security researcher, said that, “it’s not really comfortable to me that these random large companies are making arbitrary decisions about what is safe in security and what’s not.” Dowd has spent decades finding and selling “zero days” — previously unknown software flaws and the exploits that take advantage of them — to Western governments, rather than report them to the software makers so they get patched.

Governments pay a premium for vulnerabilities precisely because they stay open, which is useful for intelligence operations. Dowd admitted his work may make him biased, but he isn’t alone. Several people who work in offensive cybersecurity — they proactively probe systems for weaknesses — described to TechCrunch how they use AI tools and deal with their guardrails. Chris Anley, the chief scientist at security consulting giant NCC Group, said that asking an AI model to try to exploit a bug is a key step in confirming it’s a real vulnerability worth fixing. But if a guardrail prompts the model to refuse to answer the question outright, the guardrail hurts defenders, he said. “This is where the whole offensive versus defensive and guardrails part comes in, because ‘fix this code’ as a prompt is both an essential mechanism for defense but also a roadmap for finding critical vulnerabilities in the code base,” said Anley. “So at the same time, the same tool is both an offensive tool and a defensive tool, and the two can’t really be unpicked.” It’s “like a hammer,” he continued. “You can’t build a house without a hammer. It’s definitely a tool but it’s also irreducibly a weapon as well.” When he and his colleagues run into such a roadblock, they sometimes fall back on open-source AI models that come with no guardrails at all. Paolo Stagno, the chief technology officer at CrowdFense, a well-known company that develops, acquires, and sells unknown vulnerabilities to government agencies, agreed with Dowd, saying AI companies “essentially treat customers like children who need babysitting” with their vetted programs and guardrails. Stagno said he and his colleagues do use frontier models — but only for reverse engineering.