MLCommons Jailbreak Benchmark v1.0 is a new methodology for assessing how well large language models resist single‑turn, text‑based jailbreak attacks. It evaluates eight open‑weight systems with 264 seed prompts across eleven hazard categories, using the AILuminate Assessment Standard v1.4 to measure the Resilience Gap—the difference in safety performance between baseline and adversarial conditions. The benchmark found that unsafe‑response rates rose from 11.08% to 18.65% under jailbreak conditions, yielding an average Resilience Gap of 7.57% and highlighting variability in attack effectiveness and evaluator reliability.
By Carsten Maple (Victor), Cagatay Yucel (Victor), Isaac Holeman (Victor), Chris Knotz (Victor), Peter Mattson (Victor), James Goel (Victor), Jonathan Petit (Victor), Sean McGregor (Victor), James Ezick (Victor), Abhishek Kumar (Victor), Alicia Parrish (Victor), Murali Emani (Victor), Kashyap Iyer (Victor), Faiza Khan Khattak (Victor), Washington Mbonu (Victor), Daniel Machlab (Victor), Eileen Long (Victor), Shaona Ghosh (Victor), Jibin Varghese (Victor), Roman Lutz (Victor), Andrew Gruen (Victor), Bennett Hillenbrand (Victor), Prabal Gupta (Victor), Mohammed Serrhini (Victor), Dhivya Nagasubramanian (Victor), Aakash Gupta (Victor), Jun (Victor), Lu, Kurt Bollacker, Chang Liu, Jonathan Petit, Cong Chen, Jean-Philippe Monteuuis, Brent Miller, Apurv Verma, Roman Eng, Armstrong Foundjem, Mohammed Serrhini
The paper introduces ADVERSA, an automated red‑teaming framework that evaluates large language model safety over multiple turns by tracking continuous compliance trajectories instead of binary jailbreak outcomes. Using a fine‑tuned 70B attacker model and a structured 5‑point rubric, the authors conduct controlled experiments on three frontier victim models, measuring guardrail degradation and judge reliability through a triple‑judge consensus. Results show a 26.7% jailbreak rate with most breaches occurring early, and the study documents inter‑judge agreement, attacker drift, and attacker refusals as key factors affecting safety assessment.
By Harry Owiredu-Ashley
arXiv:2608. 03070v1 Announce Type: cross Abstract: 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.
By 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
arXiv:2508. 10029v3 Announce Type: replace-cross Abstract: Safety-aligned large language models can still be manipulated through white-box interventions that modify their internal representations.
By Wenpeng Xing, Bohan Yang, Mohan Li, Chunqiang Hu, Haitao Xu, Ningyu Zhang, Bo Lin, Meng Han
arXiv:2510. 01359v2 Announce Type: replace-cross Abstract: Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-only settings.
By Shoumik Saha, Jifan Chen, Sam Mayers, Sanjay Krishna Gouda, Zijian Wang, Varun Kumar
arXiv:2606. 05609v1 Announce Type: cross Abstract: As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical.
By Seungwon Jeong, Jiwoo Jeong, Hyeonjin Kim, Yunseok Lee, Woojin Lee