Large Language Models (LLMs) are now commonplace in conversation applications. However, their risks of misuse for generating harmful responses have raised serious societal concerns and spurred recent research on LLM conversation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM conversation safety: attacks, defenses, and evaluations. Our goal is to provide a structured summary that enhances understanding of LLM conversation safety and encourages further investigation into this important subject. For easy reference, we have categorized all the studies mentioned in this survey according to our taxonomy, available at: https://github.com/niconi19/LLM-conversation-safety.
FRACTURED-SORRY-Bench: Framework for Revealing Attacks in Conversational Turns Undermining Refusal Efficacy and Defenses over SORRY-Bench (Automated Multi-shot Jailbreaks)
Scalable Defense against In-the-wild Jailbreaking Attacks with Safety Context Retrieval
School of EECS, Peking University（电子工程系，北京大学）；School of Mathematical Sciences, Peking University（数学系，北京大学）；State Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（通用人工智能国家重点实验室，智能科学与技术学校，北京大学）；Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）
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）
SAID: Safety-Aware Intent Defense via Prefix Probing for Large Language Models
Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Harbin Institute of Technology, Shenzhen, Shenzhen, China（深圳视觉目标检测与识别重点实验室，哈尔滨工业大学，哈尔滨工业大学，深圳，深圳，中国）
TAF-MED: Multi-Turn Safety Refusal Collapse in LLMs Under Declared Self-Treatment Intent
Independent Researcher（独立研究者）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg（弗莱堡大学医学中心医学院普通实践研究所）
Safety Instincts: LLMs Learn to Trust Their Internal Compass for Self-Defense
Beijing Institute of AI Safety and Governance（北京人工智能安全与治理研究院）；Beijing Key Laboratory of Safe AI and Superalignment（北京安全人工智能与超对齐重点实验室）；BrainCog Lab, Institute of Automation, Chinese Academy of Sciences（脑认知实验室，中国科学院自动化研究所）
Unified Defense for Large Language Models against Jailbreak and Fine-Tuning Attacks in Education
Shanghai Institute of Artificial Intelligence for Education, East China Normal University, Shanghai 200062, China（教育人工智能研究所，华东师范大学，上海200062，中国）；School of Computer Science and Technology, East China Normal University, Shanghai 200062, China（计算机科学与技术学院，华东师范大学，上海200062，中国）；School of Computer Science（计算机科学学院）
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Chinese University of Hong Kong（香港中文大学）；Beijing Institute of Technology（北京理工大学）；The Hong Kong Polytechnic University（香港理工大学）
Pharmacist: Safety Alignment Data Curation for Large Language Models against Harmful Fine-tuning
School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Computer Science at Georgia Institute of Technology（佐治亚理工学院计算机科学系）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络科学与技术学院）；Second Affiliated Hospital of Guangzhou University of Chinese Medicine（广州中医药大学第二附属医院）；China and Pengcheng Laboratory（中 Pengcheng 实验室）
Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
Nanyang Technological University（南洋理工大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；University of Central Florida（佛罗里达大学）；Tsinghua University（清华大学）
armasuisse S+T（瑞士国防采购与技术局 科学与技术部）；HES-SO Valais-Wallis（瑞士西部高等专业学院 瓦莱州立分校）；Effixis SA（Effixis公司）；University of Lausanne（洛桑大学）；EPFL（洛桑联邦理工学院）
Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point of public attention has been such a refinement of the GPT3 model -- the ChatGPT and its subsequent integration with auxiliary capabilities, including search as part of Microsoft Bing. Despite extensive prior research invested in their development, their performance and applicability to a range of daily tasks remained unclear and niche. However, the…
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models
Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Kuaishou Technology（快手科技）；School of Computer Science and Technology, Beihang University（北航计算机科学与技术学院）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Chongqing University（重庆大学）；Wuhan University（武汉大学）
