Frontier LLMs can build working exploits from known bugs, benchmarks show

The emergence of these two benchmarks marks a critical juncture in AI security research. AI systems have already demonstrated the ability to discover novel vulnerabilities in widely deployed software—operating systems, browsers, and development tools. The bottleneck has been exploit construction: the technical work needed to turn a known vulnerability into an attack that actually works in real-world conditions with genuine defenses like sandboxing. ExploitGym and ExploitBench measure exactly this capability, filling a gap left by previous cybersecurity benchmarks that researchers note have become saturated.

The disparity between Mythos Preview's 18 successful Tier 1 exploits and GPT-5.5's single success indicates meaningful variation in frontier model capabilities, but both results point in the same direction. The researchers explicitly observe that publicly available AI today can only exploit a small fraction of known vulnerabilities—a constraint that the coming generation of frontier models may overcome. The implication is direct: if AI exploit-building capability reaches parity with AI vulnerability discovery, the preconditions for automated cyberattacks at scale will exist. This is not speculation about new attack methods but automation of an existing human process using tools already within reach.