SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters
Shanghai University of Finance and Economics（上海财经大学）；Southern University of Science and Technology（南方科技大学）；Ant Group（蚂蚁集团）；The Chinese University of Hong Kong（香港中文大学）
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release, LLMs remain vulnerable to various malicious attacks. In this paper, we adopt a red-teaming strategy to enhance LLM safety and introduce SeqAR, a simple yet effective framework to design jailbreak prompts automatically. The SeqAR framework generates and optimizes multiple jailbreak characters and then applies sequential jailbreak characters in a single query to bypass the guardrails of the target LLM. Different from previous work which relies on proprietary LLMs or seed jailbreak templates crafted by human expertise, SeqAR can generate and optimize the jailbreak prompt in a cold-start scenario using open-sourced LLMs without any seed jailbreak templates. Experimental results show that SeqAR achieves attack success rates of 88% and 60% in bypassing the safety alignment of GPT-3.5-1106 and GPT-4, respectively. Furthermore, we extensively evaluate the transferability of the generated templates across different LLMs and held-out malicious requests, while also exploring defense strategies against the jailbreak attack designed by SeqAR.
RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent
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）；School of Cyber Science and Technology, Zhejiang University（浙江大学网络科学与技术学院）；Palo Alto Networks（派拓网络）；University of North Texas（北得克萨斯大学）
AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；NVIDIA（NVIDIA公司）；Cornell University（康奈尔大学）；Washington University, St. Louis（圣路易斯华盛顿大学）；University of Michigan, Ann Arbor（安娜堡大学）；The Ohio State University（俄亥俄州立大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
ForgeDAN: An Evolutionary Framework for Jailbreaking Aligned Large Language Models
iFLYTEK Security Laboratory（iFLYTEK安全实验室）；Anhui SparkShield Intelligent Technology Co., Ltd.（安徽SparkShield智能科技有限公司）；Peking University（北京大学）；School of Automation, University of Electronic Science and Technology of China（电子科技大学自动化学院）；Northwest University（西北大学）；University of New South Wales（新南威尔士大学）；National Computer Network Emergency Response Technical Team/Coordination Center of China（中国国家计算机网络应急技术处置协调中心）
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）
Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models
School of Cyber Science and Technology, Shandong University（山东大学网络科学与技术学院）；Netflix Eyeline Studios；State Key Laboratory of Cryptography and Digital Economy Security, Shandong University（山东大学密码与数字经济安全国家重点实验室）；Shandong Key Laboratory of Artificial Intelligence Security, Shandong University（山东省人工智能安全重点实验室）
Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs
AI Thrust（AI 部）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）；The Hong Kong University of Science and Technology（香港科技大学）
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）
SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents
Nanyang Technological University（南洋理工大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beihang University（北京航空航天大学）；Sun Yat-sen University（中山大学）；Beijing Institute of Technology（北京理工大学）；National University of Singapore（新加坡国立大学）
Hide Your Malicious Goal Into Benign Narratives: Jailbreak Large Language Models through Carrier Articles
The Pennsylvania State University（宾夕法尼亚州立大学）；Northern Arizona University（北亚利桑那大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Carnegie Mellon University（卡内基梅隆大学）
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）
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）
Engineering Programme, NUS Graduate School, National University of Singapore（国立新加坡大学整合科学与工程计划）；Institute of Data Science (IDS), National University of Singapore（国立新加坡大学数据科学研究所）；Department of Computer Science, School of Computing, National University of Singapore（国立新加坡大学计算机科学系）
Ignore All Previous Instructions: Jailbreaking as a de-escalatory peace building practise to resist LLM social media bots
School of Engineering Maths & Technology University of Bristol（工程数学与科技学院 英国布里斯托尔大学）；Business School University of Bristol（商学院 英国布里斯托尔大学）；School of Computer Science University of Bristol（计算机科学学院 英国布里斯托尔大学）
