AI Bug-Finding Models Accelerate Zero-Day Discovery and Exploit Development

Anthropic disclosed Mythos Preview, an advanced AI model it says can identify and exploit zero-day vulnerabilities at a far higher rate than its Claude Opus 4.6 model, generating working exploits in 72.4 percent of attempts. The company said the system can find and chain flaws across major operating systems and web browsers, including remote code execution, sandbox escapes, local privilege escalation, and multi-bug exploit paths. Anthropic did not release the model publicly, instead restricting access through Project Glasswing for selected partners and organizations to support defensive vulnerability research and responsible disclosure; it said the model has already uncovered thousands of additional high- and critical-severity flaws.

At Black Hat Asia, RunSybil CEO and former OpenAI security engineer Ari Herbert-Voss said open source AI models can match Mythos-level bug-finding performance when paired with the right orchestration or "scaffolding." He said combining multiple open models can improve coverage because different systems surface different classes of flaws, offering a form of defense in depth, while also addressing the cost and limited availability of proprietary tools like Mythos. Herbert-Voss added that human experts remain necessary to coordinate model workflows and validate large volumes of AI-generated findings, but said economic pressure and operational advantages are likely to drive broader adoption of AI-assisted vulnerability discovery across security teams.