AI Security Leaderboard: Methodology, Results and Minimal Standard

Computer Science > Cryptography and Security [Submitted on 4 Aug 2026] AI Security Leaderboard: Methodology, Results and Minimal Standard Jasper Timm, Lukas Struppek, Ziwei Xu, Grace Cheong, Oscar Mata, Dan Zhao, Mick Yang, Isadora De Andrade, Xiaojun Jia, Yiming Li, Samuel Bauer, Heather McIntyre, Adam Gleave, Edward Yee, Kellin Pelrine Frontier AI model developers increasingly rely on layered safeguards to prevent catastrophic misuse, but little public evidence exists on how much protection these safeguards provide, or how consistently across developers. We introduce the this http URL Minimal Standard for Safeguards, Version 1.0: a taxonomy of 67 readily accessible static jailbreak techniques, a method for composing them into a very large attack space, and a benchmark of flagship models against a sample of it. We evaluate Claude Fable 5, GPT-5.6 Sol, Gemini 3.1 Pro, and Grok 4.5 on two complementary datasets totalling 360 attacker goals spanning chemical, biological, radiological/nuclear and explosive (CBRNE) threats and offensive cyber, using a three-stage funnel to identify universal jailbreaks: single prompt templates that elicit operationally compliant responses on over 75% of a domain's goals. We also introduce a cost-to-jailbreak metric that models attacker spend directly, with right-censored lower bounds where no universal jailbreak was found. Robustness is highly uneven: the cost to break these models varies over a hundredfold. Random search over our technique pool found 63 universal jailbreaks against Grok 4.5 and 18 against Gemini 3.1 Pro, at an average cost of roughly 58and 278 per jailbreak found; expert-guided composition raised these to 385 and 231. Neither Claude Fable 5 nor GPT-5.6 Sol yielded any universal jailbreak under either strategy. Because meeting the Minimal Standard requires only defenses already publicly described and deployed in production elsewhere, these gaps appear closable with current techniques. We recommend defense-in-depth combining reasoning, activation, and input/output monitoring. Results are maintained at this http URL. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2608.03070 [cs.CR]   (or arXiv:2608.03070v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.03070 Focus to learn more Submission history From: Jasper Timm [view email] [v1] Tue, 4 Aug 2026 03:32:19 UTC (388 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)