Vision foundation models

Source Document

Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety

Ma, Xingjun; Gao, Yifeng; Wang, Yixu; Wang, Ruofan; Wang, Xin; Sun, Ye; Ding, Yifan; Xu, Hengyuan; Chen, Yunhao; Zhao, Yunhan; Huang, Hanxun; Li, Yige; Wu, Yutao; Zhang, Jiaming; Zheng, Xiang; Bai, Yang; Wu, Zuxuan; Qiu, Xipeng; Zhang, Jingfeng; Li, Yiming; Han, Xudong; Li, Haonan; Sun, Jun; Wang, Cong; Gu, Jindong; Wu, Baoyuan; Chen, Siheng; Zhang, Tianwei; Liu, Yang; Gong, Mingming; Liu, Tongliang; Pan, Shirui; Xie, Cihang; Pang, Tianyu; Dong, Yinpeng; Jia, Ruoxi; Zhang, Yang; Ma, Shiqing; Zhang, Xiangyu; Gong, Neil Zhenqiang; Xiao, Chaowei; Erfani, Sarah; Baldwin, Tim; Li, Bo; Sugiyama, Masashi; Tao, Dacheng; Bailey, James; Jiang, Yu-Gang (2025)

Large Language Models (LLMs) are now com- monplace in conversation applications. How- ever, their risks of misuse for generating harm- ful responses have raised serious societal con- cerns and spurred recent research on LLM con- versation safety. Therefore, in this survey, we provide a comprehensive overview of recent studies, covering three critical aspects of LLM conversation safety: attacks, defenses, and eval- uations. Our goal is to provide a structured sum- mary that enhances understanding of LLM con- versation safety and encourages further investi- gation into this important subject. For easy ref- erence, we have categorized all the studies men- tioned in this survey according to our taxonomy, available at: https://github.com/niconi19/LLM- conversation-safety.