Scoop: OpenAI plans staggered rollout of new model over cybersecurity risk
Axios
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OpenAI is finalizing a model with advanced cybersecurity capabilities that it plans to release only to a small set of companies, similar to Anthropic's limited roll out of https://www.axios.com/2026/04/08/anthropic-mythos-model-ai-cyberattack-warning" target="_blank">Mythos, a source familiar told Axios.
Why it matters: AI capabilities have reached a tipping point, at least in terms of autonomy and hacking capabilities.
Model-makers are now so worried about the havoc their own tools could cause that they're reluctant to release them into the wild.
Driving the news: Anthropic announced plans Tuesday to limit access of its https://www.axios.com/2026/04/07/anthropic-mythos-preview-cybersecurity-risks" target="_blank">new Mythos Preview model to a hand-picked group of technology and cybersecurity companies over fears of its advanced hacking capabilities.
- At the time, it was the first AI company to take such an approach with a new model.
- Now, OpenAI is planning a similar approach, according to the source.
Zoom in: OpenAI introduced its https://openai.com/index/trusted-access-for-cyber/" target="_blank">"Trusted Access for Cyber" pilot program in February after rolling out GPT-5.3-Codex, the company's most cyber-capable reasoning model.
- Organizations in the invite-only program are given access to "even more cyber capable or permissive models to accelerate legitimate defensive work," according to a https://openai.com/index/trusted-access-for-cyber/" target="_blank">blog post.
- At the time, OpenAI committed $10 million in API credits to participants.
The big picture: Former government officials and top security leaders have been https://www.axios.com/2026/02/24/cyberattack-risk-scenarios-experts" target="_blank">ringing alarm bells over the past year about https://www.axios.com/2025/05/13/mandiant-founder-artificial-intellience-cyberattack" target="_blank">AI models that — in the wrong hands — could one day autonomously disrupt water utilities, the electric grid, or financial systems.
- Those capabilities now appear to be here.
Threat level: Even if AI companies hold back their models for limited releases, top security experts all have the same message: There's no going back.
- "You can't stop models from doing code enumeration or finding flaws in older codebases," said Rob T. Lee, chief AI officer at the SANS Institute. "That capability exists now."
- It's only a matter of weeks or months before there's a new model with similar capabilities out in the wild, Wendi Whitmore, chief security intelligence officer at Palo Alto Networks, told Axios during a panel at the HumanX conference in San Francisco on Tuesday.
- Adam Meyers, senior vice president of counter adversary operations at CrowdStrike, called Mythos' capabilities a "wake-up call" for the entire industry.
Between the lines: Restricting the rollout of a new frontier model makes "more sense" if companies are concerned about models' ability to write new exploits — rather than about their ability to find bugs in the first place, Stanislav Fort, CEO of security firm Aisle, told Axios.
The intrigue: Staggering the release of new AI models looks a lot like how cybersecurity vendors currently handle the disclosure of security flaws in software, Lee added.
- "It's the same debate we've had for decades around responsible vulnerability disclosure," Lee said.
Yes, but: It's unclear if OpenAI will decide to release its forthcoming model more broadly at some point.
- Anthropic has said it won't ever release Mythos Preview to the public, but would consider releasing other Mythos models if there are strong guardrails.
Reality check: Widely available AI models are already capable of finding some of the vulnerabilities and exploits that Mythos uncovered, researchers at AISLE https://aisle.com/blog/ai-cybersecurity-after-mythos-the-jagged-frontier" target="_blank">found Wednesday.
Go deeper: https://www.axios.com/2026/04/08/mythos-system-card" target="_blank">The wildest things Anthropic's Mythos pulled off in testing
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