arXiv AI June 11, 2026 By Ge Shi, Jun Yin, Donglin Xie, Fangyi Liu, Yucan Li, Menglin Liu

JailbreakOPT: Tool-Assisted Iterative Jailbreak Prompt Optimization

arXiv:2606. 11425v1 Announce Type: cross Abstract: Jailbreak attacks expose persistent safety weaknesses in large language models (LLMs), but existing stateless single-turn methods face a trade-off: hand-crafted prompts are expressive but static, while iterative prompt optimization can adapt but often relies on low-level mutations that require many target queries.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 2

VERA: Variational Inference Framework for Jailbreaking Large Language Models

arXiv:2506. 22666v3 Announce Type: replace-cross Abstract: The rise of API-only access to state-of-the-art LLMs highlights the need for effective black-box jailbreak methods to identify model vulnerabilities in real-world settings.

By Anamika Lochab, Lu Yan, Patrick Pynadath, Xiangyu Zhang, Ruqi Zhang

llms benchmarks safety

arXiv AI

Jul 20

Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

arXiv:2602. 24009v4 Announce Type: replace-cross Abstract: Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols.

By Zhicheng Fang, Jingjie Zheng, Chenxu Fu, Wei Xu

llms agents multimodal benchmarks safety

arXiv AI

Jun 19

Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems

arXiv:2606. 20470v1 Announce Type: cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents.

By Reza Soosahabi, Vivek Namsani

agents multimodal benchmarks safety

arXiv AI

Jun 9

PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits

arXiv:2510. 17947v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are improving at an exceptional rate.

By Neeladri Bhuiya, Madhav Aggarwal, Diptanshu Purwar

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jun 3

Jailbreak Attack Initializations as Extractors of Compliance Directions

arXiv:2502. 09755v4 Announce Type: replace-cross Abstract: Safety-aligned LLMs respond to prompts with either compliance or refusal, each corresponding to distinct directions in the model's activation space.

By Amit Levi, Rom Himelstein, Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin

llms multimodal safety

arXiv AI

Jul 23

JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models

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

llms benchmarks safety

Related stories

VERA: Variational Inference Framework for Jailbreaking Large Language Models

Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems

PLAGUE: Plug-and-play framework for Lifelong Adaptive Generation of Multi-turn Exploits

Jailbreak Attack Initializations as Extractors of Compliance Directions

JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models