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.

翻译：大语言模型在对话应用中已变得司空见惯，但其被滥用于生成有害内容的风险引发了严重的社会担忧，并推动了近期关于LLM对话安全的研究。因此，本综述对现有研究进行了全面梳理，涵盖LLM对话安全的三个关键方面：攻击、防御与评估。我们的目标是提供结构化总结，以增进对LLM对话安全的理解，并促进对该重要课题的进一步探索。为方便查阅，我们已根据分类法对本文涉及的所有研究进行了归类，详见：https://github.com/niconi19/LLM-conversation-safety。