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AI Security Leaderboard – comparing cyber and CBRN safeguards

Hacker News

AI Security Leaderboard – comparing cyber and CBRN safeguards

There's no shortage of leaderboards for model capabilities - but the security of models is becoming increasingly relevant, from the risk of an AI agent processing unsanitized input being hijacked to models being pulled due to cybersecurity jailbreaks. We developed an automated test suite that runs models through 1500 automatically generated jailbreak attempts and measures the number of universal jailbreaks: prompts that elicit compliant, detailed responses to >75% clearly harmful questions within a domain (like offensive cybersecurity). We find a big gap between the most robust models -- Fable 5 and GPT-5.6 Sol -- and other leading frontier models -- Gemini 3.1 Pro and Grok 4.5. This is v1.0 and we plan to update with new attacks and broader datasets in the future; we'd love to hear from HN what would be useful in your work!

Visit product View on Hacker NewsJul 29, 2026

Actual performance

4points

Did not reach leaderboard

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58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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