Jailbreak Attacks

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5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 48

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Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking
May 9, 2026Zhida He, Xiaoyu Wen, Han Qi +7RL for Language Models Credit Assignment in RL
Shanghai AI Laboratory · Fudan University · Shanghai Jiao Tong University
Mitigating Many-shot Jailbreak Attacks with One Single Demonstration
May 8, 2026Kejia Chen, Jiawen Zhang, Boheng Li +6Jailbreak Attacks In-Context Learning
Zhejiang University · Nanyang Technological University · University of Science and Technology of China +2
Conceal, Reconstruct, Jailbreak: Exploiting the Reconstruction-Concealment Tradeoff in MLLMs
May 7, 2026Md Farhamdur Reza, Richeng Jin, Tianfu Wu +1Multimodal Large Language Models Jailbreak Attacks
1NC State University · 2Zhejiang University
ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming
May 4, 2026Mario Rodríguez Béjar, Francisco J. Cortés-Delgado, S. Braghin +1Evolutionary Optimization LLM Red Teaming
Universidad de Murcia · IBM Research
MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety
May 3, 2026Jialin Song, Xiaodong Liu, Weiwei Yang +4LLM Safety Benchmarks Adversarial Prompt Generation
Simon Fraser University · Microsoft Research
Jailbroken Frontier Models Retain Their Capabilities
Apr 30, 2026Daniel Zhu, Zihan Wang, Xuchan Bao +1Language Model Safety Evaluation Jailbreak Attacks
Anthropic
TwinGate: Stateful Defense against Decompositional Jailbreaks in Untraceable Traffic via Asymmetric Contrastive Learning
Apr 30, 2026Bowen Sun, Chaozhuo Li, Yaodong Yang +2Jailbreak Attacks LLM Security
Johns Hopkins University · Microsoft Research Asia · Peking University +1
Adaptive Prompt Embedding Optimization for LLM Jailbreaking
Apr 27, 2026Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2Adversarial Prompt Generation Adversarial Attacks on LLMs
School of Information Studies, McGill University, Montreal, QC, Canada · Department of Computer Science, Kean University, Union, NJ, United States · Mila - Quebec AI Institute, Montreal, QC, Canada +1
Jailbreaking Frontier Foundation Models Through Intention Deception
Apr 27, 2026Xinhe Wang, Katia Sycara, Yaqi XieJailbreak Attacks LLM Safety
Carnegie Mellon University
AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models
Apr 23, 2026Tanmay Gautam, Alireza Bahramali, Sandeep AtluriLarge Language Model-Guided Optimization Program Synthesis
Responsible AI, Microsoft
Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models
Apr 23, 2026Naheed Rayhan, Sohely JahanLanguage Model Safety Evaluation Adversarial Attacks on LLMs
Jagannath University · University of Barishal
Adversarial Humanities Benchmark: Results on Stylistic Robustness in Frontier Model Safety
Apr 20, 2026Marcello Galisai, Susanna Cifani, Francesco Giarrusso +5LLM Safety Benchmarks Adversarial Attacks on LLMs
DEXAI – Icaro Lab · Sapienza University of Rome · Sant’Anna School of Advanced Studies
Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing
Apr 17, 2026Zehao Wang, Lanjun WangAdversarial Prompt Generation Jailbreak Attacks
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China. · School of New Media and Communication, Tianjin University, Tianjin 300072, China. · Shanghai Key Laboratory of Data Science,[cs.LG] Shanghai 200438, China.
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
Mar 3, 2026Zhongxi Wang, Yueqian Lin, Jingyang Zhang +3LLM Safety Benchmarks Language Model Safety Evaluation
1Duke University · 2Virtue AI
When the Prompt Becomes Visual: Vision-Centric Jailbreak Attacks for Large Image Editing Models
Feb 10, 2026Jiacheng Hou, Yining Sun, Ruochong Jin +4Jailbreak Attacks Multimodal Jailbreak Attacks
Tsinghua University, China · Pengcheng Laboratory, Shenzhen, China · Central South University, Changsha, China.
NonTextual Target Attack
Oct 3, 2025Xinzhe Huang, Wenjing Hu, Tianhang Zheng +6Adversarial Prompt Generation Adversarial Attacks on LLMs
State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou 310007, China · Hangzhou High-Tech Zone, Binjiang Institute of Blockchain and Data Security, Hangzhou 310053, China · School of Cyberspace Security, Nanjing University of Science and Technology, Nanjing 210094, China +3
HauntAttack: When Attack Follows Reasoning as a Shadow
Jun 8, 2025Jingyuan Ma, Rui Li, Zheng Li +4Adversarial Attacks on LLMs Jailbreak Attacks
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University · StepFun · Department of Computing, The Hong Kong Polytechnic University +1
The Linear Geometry of Interpretable Tokens: Jailbreaking Attacks and Defenses for Unlearned Diffusion Models
Apr 30, 2025Siyi Chen, Yimeng Zhang, Sijia Liu +1Jailbreak Attacks Diffusion Model Unlearning
University of Michigan · Michigan State University

May 9, 2026, cs.AIJ/K move · Enter open · S save

Not All Turns Matter: Credit Assignment for Multi-Turn Jailbreaking

Zhida He, Xiaoyu Wen, Han Qi, Ziyuan Zhou+6

Shanghai AI Laboratory · Fudan University · Shanghai Jiao Tong University

RL for Language Models Credit Assignment in RL Step-Level Credit Assignment in RL Jailbreak Attacks LLM Jailbreak Attacks Multi-Turn Jailbreak Attacks+1

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Deploying LLMs in multi-turn dialogues facilitates jailbreak attacks that distribute harmful intent across seemingly benign turns. Recent training-based multi-turn jailbreak methods learn long-horizon attack strategies from interaction feedback, but often rely on coarse trajectory-level outcome signals that broadcast uniformly to every turn. However, we find that turn-level contributions in multi-turn jailbreaking are non-uniform, phase-dependent, and target-specific. Such coarse outcome supervision induces a credit assignment problem, leading to over-rewarding redundant turns in successful trajectories and under-crediting useful intermediate turns in failed ones. To address this, we propose TRACE, a turn-aware credit assignment framework for reinforcement learning (RL)-based multi-turn jailbreaking. For successful trajectories, TRACE estimates turn-level contributions via leave-one-turn-out semantic masking; for failed ones, TRACE assigns penalties based on prompt harmfulness and semantic relevance, with an additional local refusal-aware penalty. Furthermore, we reuse the attack-side credit signal for multi-turn defense alignment. Extensive experiments on open-source and closed-source targets show that TRACE achieves strong overall performance in effectiveness, transferability, and efficiency, yielding about a 25% relative improvement in attack success rate over the strongest RL baseline while also improving the safety-utility balance when reused for defense alignment.

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