We expect that upcoming AI models will continue on this trajectory; in preparation, we are planning and evaluating as though each new model could reach ‘High’ levels of cybersecurity capability, as measured by our Preparedness Framework⁠(opens in a new window). By this, we mean models that can either develop working zero-day remote exploits against well-defended systems, or meaningfully assist with complex, stealthy enterprise or industrial intrusion operations aimed at real-world effects. This post explains how we think about safeguards for models that reach these levels of capability, and ensure they meaningfully help defenders while limiting misuse.

As these capabilities advance, OpenAI is investing in strengthening our models for defensive cybersecurity tasks and creating tools that enable defenders to more easily perform workflows such as auditing code and patching vulnerabilities. Our goal is for our models and products to bring significant advantages for defenders, who are often outnumbered and under-resourced.

Like other dual-use domains, defensive and offensive cyber workflows often rely on the same underlying knowledge and techniques. We are investing in safeguards to help ensure these powerful capabilities primarily benefit defensive uses and limit uplift for malicious purposes. Cybersecurity touches almost every field, which means we cannot rely on any single category of safeguards—such as restricting knowledge or using vetted access alone—but instead need a defense-in-depth approach that balances risk and empowers users. In practice, this means shaping how capabilities are accessed, guided, and applied so that advanced models strengthen security rather than lower barriers to misuse.

We see this work not as a one-time effort, but as a sustained, long-term investment in giving defenders an advantage and continually strengthening the security posture of the critical infrastructure across the broader ecosystem.