Large Language Models (LLMs) have gained significant attention but also raised concerns due to the risk of misuse. Jailbreak prompts, a popular type of adversarial attack towards LLMs, have appeared and constantly evolved to breach the safety protocols of LLMs. To address this issue, LLMs are regularly updated with safety patches based on reported jailbreak prompts. However, malicious users often keep their successful jailbreak prompts private to exploit LLMs. To uncover these private jailbreak prompts, extensive analysis of large-scale conversational datasets is necessary to identify prompts that still manage to bypass the system's defenses. This task is highly challenging due to the immense volume of conversation data, diverse characteristics of jailbreak prompts, and their presence in complex multi-turn conversations. To tackle these challenges, we introduce JailbreakHunter, a visual analytics approach for identifying jailbreak prompts in large-scale human-LLM conversational datasets. We have designed a workflow with three analysis levels: group-level, conversation-level, and turn-level. Group-level analysis enables users to grasp the distribution of conversations and identify suspicious conversations using multiple criteria, such as similarity with reported jailbreak prompts in previous research and attack success rates. Conversation-level analysis facilitates the understanding of the progress of conversations and helps discover jailbreak prompts within their conversation contexts. Turn-level analysis allows users to explore the semantic similarity and token overlap between a singleturn prompt and the reported jailbreak prompts, aiding in the identification of new jailbreak strategies. The effectiveness and usability of the system were verified through multiple case studies and expert interviews.
FRACTURED-SORRY-Bench: Framework for Revealing Attacks in Conversational Turns Undermining Refusal Efficacy and Defenses over SORRY-Bench (Automated Multi-shot Jailbreaks)
PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
University of Toronto, Vector Institute（多伦多大学，向量研究所）；University of Oxford（牛津大学）；Polytechnique Montréal, Mila - Quebec AI Institute, University of Toronto（蒙特利尔理工学院，魁北克人工智能研究所，多伦多大学）
Conversational Complexity for Assessing Risk in Large Language Models
University of Cambridge（剑桥大学）；Spanish National Research Council（西班牙国家研究委员会）；Universitat Politècnica de València（瓦伦西亚理工大学）；Leverhulme Centre for the Future of Intelligence（勒沃休姆未来智能中心）；Center for Automation and Robotics（自动化与机器人中心）；Valencian Research Institute for Artificial Intelligence（瓦伦西亚人工智能研究所）
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
JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models
Kyoto University（京都大学）；Hohai University（河海大学）；The University of Tokyo（东京大学）；University of Science and Technology of China（中国科学技术大学）；Hong Kong Polytechnic University（香港理工大学）
Dual-Space Smoothness for Robust and Balanced LLM Unlearning
School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Department of Computer Science and Engineering, University of Notre Dame（圣母大学计算机科学与工程系）
