Evolving Jailbreaks: Automated Multi-Objective Long-Tail Attacks on Large Language Models
School of Artificial Intelligence（人工智能学院）；Brain-Inspired Technology Co.,Ltd（脑启发技术有限公司）；Department of Computer Science and Engineering（计算机科学与工程系）；Southern University of Science and Technology（南方科技大学）；Nanyang Technological University（南洋理工大学）
Wenjing Hong, Zhonghua Rong, Li Wang, Feng Chang, Jian Zhu, Ke Tang, Zexuan Zhu, Yew-Soon Ong
Large Language Models (LLMs) have been widely deployed, especially through free Web-based applications that expose them to diverse user-generated inputs, including those from long-tail distributions such as low-resource languages and encrypted private data. This open-ended exposure increases the risk of jailbreak attacks that undermine model safety alignment. While recent studies have shown that leveraging long-tail distributions can facilitate such jailbreaks, existing approaches largely rely on handcrafted rules, limiting the systematic evaluation of these security and privacy vulnerabilities. In this work, we present EvoJail, an automated framework for discovering long-tail distribution attacks via multi-objective evolutionary search. EvoJail formulates long-tail attack prompt generation as a multi-objective optimization problem that jointly maximizes attack effectiveness and minimizes output perplexity, and introduces a semantic-algorithmic solution representation to capture both high-level semantic intent and low-level structural transformations of encryption-decryption logic. Building upon this representation, EvoJail integrates LLM-assisted operators into a multi-objective evolutionary framework, enabling adaptive and semantically informed mutation and crossover for efficiently exploring a highly structured and open-ended search space. Extensive experiments demonstrate that EvoJail consistently discovers diverse and effective long-tail jailbreak strategies, achieving competitive performance with existing methods in both individual and ensemble level.
Test-Time Immunization: A Universal Defense Framework Against Jailbreaks for (Multimodal) Large Language Models
NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences（国家工程实验室与人工智能研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Red-teaming the Multimodal Reasoning: Jailbreaking Vision-Language Models via Cross-modal Entanglement Attacks
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；People’s Public Security University of China（中国人民公安大学）
The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
Department of Computer Science, University of Texas at El Paso（德克萨斯大学埃尔帕索分校计算机科学系）；School of Computing, Southern Illinois University Carbondale（南方伊利诺伊大学卡罗尔丹分校计算学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
PiCo: Jailbreaking Multimodal Large Language Models via Pictorial Code Contextualization
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of Artificial Intelligence, Beijing University of Aeronautics and Astronautics（北京航空航天大学人工智能研究院）
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Pengcheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；Jilin University（吉林大学）；Southwest University（西南大学）；University of Electronic Science and Technology of China（电子科技大学）；Shenzhen University（深圳大学）
PixJail: Self-Evolving Paper-to-Pipeline Reproduction for Text-to-Image Jailbreak Evaluation
The Hong Kong University of Technology and Science (Guangzhou)（香港科技与应用科技大学（广州））；East China Normal University（华东师范大学）；Shanghai Qi Zhi Institute（上海启智研究院）；Wuhan University（武汉大学）；Institute of Deep Perception Technology, JITRI（视觉感知技术研究院，JITRI）；CAIR, Hong Kong Institute of Science and Innovation (HKISI)（创新科技研究院，香港科学与创新研究院（HKISI））；MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）
Jailbreaking and Mitigation of Vulnerabilities in Large Language Models
Hunan University Changsha, PRC；Georgia Institute of Technology Atlanta, USA；Kyoto University Kyoto, Japan；Purdue University West Lafayette, USA；National Taiwan Normal University Taipei, ROC；University of Liverpool Suzhou, PRC；Hong Kong University of Science；University of Hawaii Honolulu, USA；The University of Texas at Dallas Dallas, USA；University of Wisconsin-Madison Madison, USA；Emory University Atlanta, USA；College of William \& Mary Williamsburg, USA
Arondight: Red Teaming Large Vision Language Models with Auto-generated Multi-modal Jailbreak Prompts
City University of Hong Kong（香港城市大学）；City University of Hong Kong (Dongguan)（香港城市大学（东莞））；University of Science and Technology Beijing（北京科技大学）；The University of Melbourne（墨尔本大学）
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全国家重点实验室）；State Key Laboratory of Internet Architecture, Tsinghua University（清华大学互联网架构国家重点实验室）；Huazhong University of Science and Technology（华中科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）
DARWIN: Evolving Jailbreak Adversary and Guardrail for LLM Safety Evaluation and Protection
The State Key Laboratory of Blockchain and Data Security, Zhejiang University（浙江大学区块链与数据安全全国重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术开发区（滨江）区块链与数据安全研究所）；Shanghai AI Laboratory（上海人工智能实验室）；East China Normal University（华东师范大学）
Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；NVIDIA（英伟达公司）；Penn State University（宾夕法尼亚州立大学）；Columbia University（哥伦比亚大学）；University of Missouri-Kansas City（密苏里大学堪萨斯分校）；Florida State University（佛罗里达州立大学）；Auburn University（奥本大学）
EvoCodeBench: A Human-Performance Benchmark for Self-Evolving LLM-Driven Coding Systems
Nanyang Technological University（南洋理工大学）；East China University of Science and Technology（东华大学）；Guangdong Ocean University（广东海洋大学）；City University of Hong Kong（香港城市大学）；Stanford University（斯坦福大学）
Personalized Large Language Model Assistant with Evolving Conditional Memory
The Hong Kong Polytechnic University（香港理工大学）；INF Technology (Shanghai) co., Ltd.（INF科技（上海）有限公司）；Institute of Artificial Intelligence, Soochow University, China（苏州大学人工智能研究院）
