arXiv:2607. 19424v1 Announce Type: cross Abstract: The assessment of jailbreak attacks against large language models currently suffers from inconsistent evaluation criteria and methods, leading to unreliable estimates of attack success rates.
By Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia
The paper investigates safety risks in model merging, showing that even when all constituent models are individually safety‑aligned, merging can expose a jailbreak vulnerability rooted in the pretrained foundation model. It introduces Basin‑Aware Jailbreak (BAJ), a min–max optimization method that generates adversarial suffixes transferable across merged models sharing the same backbone, without needing the exact merging coefficients or checkpoints. Experiments demonstrate BAJ’s high transfer success rates across diverse backbones and merging settings, and its resilience against existing defenses.
By Yu Zhe, Yixin Tan, Junhao Wei, Wang Chen
The paper presents a systematic study of combining defenses against jailbreak attacks on Large Language Models across different pipeline stages. It introduces a standardized evaluation framework that defines attack-success-rate, controls query budgets, and applies explicit fairness rules. Across 19 attacks and 15 defenses, the study finds that no single defense dominates, but carefully selected combinations can provide strong safety with minimal loss of utility, offering practical guidance for layered defense pipelines.
By Jiale Luo, Eric Han
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
arXiv:2606. 03486v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks that hide harmful intent behind seemingly ordinary requests such as role-play, translation, encoding, adversarial suffixes, and multi-turn buildup.
By Zhongyang Lin, Ziran Zhao, Feifei Zhai, Pengyuan Liu
The paper "Jailbreaking in the Haystack" introduces NINJA, a jailbreak technique that exploits long-context language models by appending benign, model-generated content to harmful user goals. It demonstrates that the position of harmful goals within the context is crucial for safety, and shows that NINJA significantly boosts attack success rates on models such as LLaMA, Qwen, Mistral, and Gemini. Unlike previous methods, NINJA is low-resource, transferable, less detectable, and compute‑optimal, revealing that carefully crafted benign long contexts can expose fundamental vulnerabilities in modern LMs.
By Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena, Ziqian Zhong, Alexander Robey, Aditi Raghunathan