New AI Security Leaderboard Ranks Model Robustness Against Jailbreaks

Researchers have unveiled an AI Security Leaderboard designed to rank frontier models by their robustness against jailbreak attacks. Unlike the abundant capability rankings that measure how well models perform on standard benchmarks, no comparable tool existed for evaluating model security — a gap the team sought to address. The leaderboard relies on an automated test suite that runs models through 1500 automatically generated jailbreak attempts. The core metric is the count of universal jailbreaks: prompts that elicit compliant, detailed responses to more than 75% of clearly harmful questions within a specific domain (such as offensive cybersecurity). According to the team's technical report, the testing revealed a significant gap between the most and least robust models. The timing of this tool reflects broader shifts in AI governance and risk management. The U.S. government has required developers to pull models for cybersecurity jailbreaks, and developers are increasingly reluctant to deploy AI agents due to concerns about adversarial attacks. This leaderboard is version 1.0, and the researchers are actively seeking feedback from the community on methodology and future directions. One area under consideration is adding open-weight models to the rankings, though the team notes the challenge of fairly comparing them to proprietary models.