title: A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training
    and Deployment
summary: The remarkable success of Large Language Models (LLMs) has illuminated a
  promising pathway toward achieving Artificial General Intelligence for both
  academic and industrial communities, owing to their unprecedented performance
  across various applications. As LLMs continue to gain prominence in both
  research and commercial domains, their security and safety implications have
  become a growing concern, not only for researchers and corporations but also
  for every nation. Currently, existing surveys on LLM safety primarily focus on
  specific stages of the LLM lifecycle, e.g., deployment phase or fine-tuning
  phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs.
  To address this gap, this paper introduces, for the first time, the concept of
  "full-stack" safety to systematically consider safety issues throughout the
  entire process of LLM training, deployment, and eventual commercialization.
  Compared to the off-the-shelf LLM safety surveys, our work demonstrates several
  distinctive advantages: (I) Comprehensive Perspective. We define the complete
  LLM lifecycle as encompassing data preparation, pre-training, post-training,
  deployment and final commercialization. To our knowledge, this represents the
  first safety survey to encompass the entire lifecycle of LLMs. (II) Extensive
  Literature Support. Our research is grounded in an exhaustive review of over
  800+ papers, ensuring comprehensive coverage and systematic organization of
  security issues within a more holistic understanding. (III) Unique Insights.
  Through systematic literature analysis, we have developed reliable roadmaps and
  perspectives for each chapter. Our work identifies promising research
  directions, including safety in data generation, alignment techniques, model
  editing, and LLM-based agent systems. These insights provide valuable guidance
  for researchers pursuing future work in this field.
paper_authors._id: 6809c1f389b7cade55b32a6c
paper_authors.name: Kun Wang
paper_authors._id: 6809c1f389b7cade55b32a6d
paper_authors.name: Guibin Zhang
paper_authors._id: 6809c1f389b7cade55b32a6e
paper_authors.name: Zhenhong Zhou
paper_authors._id: 6809c1f389b7cade55b32a6f
paper_authors.name: Jiahao Wu
paper_authors._id: 6809c1f389b7cade55b32a70
paper_authors.name: Miao Yu
paper_authors._id: 6809c1f389b7cade55b32a71
paper_authors.name: Shiqian Zhao
paper_authors._id: 6809c1f389b7cade55b32a72
paper_authors.name: Chenlong Yin
paper_authors._id: 6809c1f389b7cade55b32a73
paper_authors.name: Jinhu Fu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:59:47.159Z
paper_authors._id: 66dedaae6c821863fc78637f
paper_authors.avatarUrl: /avatars/d1ef954f7669c16465243d426e414c4d.svg
paper_authors.fullname: Jinhu Fu
paper_authors.type: user
paper_authors.user: Fred456
paper_authors._id: 6809c1f389b7cade55b32a74
paper_authors.name: Yibo Yan
paper_authors._id: 6809c1f389b7cade55b32a75
paper_authors.name: Hanjun Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T11:59:57.410Z
paper_authors._id: 6635d16a46904d5cfdda21cb
paper_authors.avatarUrl: /avatars/6c6dcad65186b2eaedfabb9a484a9641.svg
paper_authors.fullname: Hanjun Luo
paper_authors.type: user
paper_authors.user: Atarogic
paper_authors._id: 6809c1f389b7cade55b32a76
paper_authors.name: Liang Lin
paper_authors._id: 6809c1f389b7cade55b32a77
paper_authors.name: Zhihao Xu
paper_authors._id: 6809c1f389b7cade55b32a78
paper_authors.name: Haolang Lu
paper_authors._id: 6809c1f389b7cade55b32a79
paper_authors.name: Xinye Cao
paper_authors._id: 6809c1f389b7cade55b32a7a
paper_authors.name: Xinyun Zhou
paper_authors._id: 6809c1f389b7cade55b32a7b
paper_authors.name: Weifei Jin
paper_authors._id: 6809c1f389b7cade55b32a7c
paper_authors.name: Fanci Meng
paper_authors._id: 6809c1f389b7cade55b32a7d
paper_authors.name: Junyuan Mao
paper_authors._id: 6809c1f389b7cade55b32a7e
paper_authors.name: Hao Wu
paper_authors._id: 6809c1f389b7cade55b32a7f
paper_authors.name: Minghe Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-24T12:41:08.753Z
paper_authors._id: 6482c303954578a9d1e66f91
paper_authors.avatarUrl: /avatars/7bccbd8b82f154a350de5011659ac341.svg
paper_authors.fullname: wang
paper_authors.type: user
paper_authors.user: minghewang
paper_authors._id: 6809c1f389b7cade55b32a80
paper_authors.name: Fan Zhang
paper_authors._id: 6809c1f389b7cade55b32a81
paper_authors.name: Junfeng Fang
paper_authors._id: 6809c1f389b7cade55b32a82
paper_authors.name: Chengwei Liu
paper_authors._id: 6809c1f389b7cade55b32a83
paper_authors.name: Yifan Zhang
paper_authors._id: 6809c1f389b7cade55b32a84
paper_authors.name: Qiankun Li
paper_authors._id: 6809c1f389b7cade55b32a85
paper_authors.name: Chongye Guo
paper_authors._id: 6809c1f389b7cade55b32a86
paper_authors.name: Yalan Qin
paper_authors._id: 6809c1f389b7cade55b32a87
paper_authors.name: Yi Ding
paper_authors._id: 6809c1f389b7cade55b32a88
paper_authors.name: Donghai Hong
paper_authors._id: 6809c1f389b7cade55b32a89
paper_authors.name: Jiaming Ji
paper_authors._id: 6809c1f389b7cade55b32a8a
paper_authors.name: Xinfeng Li
paper_authors._id: 6809c1f389b7cade55b32a8b
paper_authors.name: Yifan Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-25T08:35:13.704Z
paper_authors._id: 644078ea518271b0d1bf7ec6
paper_authors.avatarUrl: /avatars/61014a3fd45f74ce541bdbe53929e233.svg
paper_authors.fullname: Yifan Jiang
paper_authors.type: user
paper_authors.user: YifanJ
paper_authors._id: 6809c1f389b7cade55b32a8c
paper_authors.name: Dongxia Wang
paper_authors._id: 6809c1f389b7cade55b32a8d
paper_authors.name: Yihao Huang
paper_authors._id: 6809c1f389b7cade55b32a8e
paper_authors.name: Yufei Guo
paper_authors._id: 6809c1f389b7cade55b32a8f
paper_authors.name: Jen-tse Huang
paper_authors._id: 6809c1f389b7cade55b32a90
paper_authors.name: Yanwei Yue
paper_authors._id: 6809c1f389b7cade55b32a91
paper_authors.name: Wenke Huang
paper_authors._id: 6809c1f389b7cade55b32a92
paper_authors.name: Guancheng Wan
paper_authors._id: 6809c1f389b7cade55b32a93
paper_authors.name: Tianlin Li
paper_authors._id: 6809c1f389b7cade55b32a94
paper_authors.name: Lei Bai
paper_authors._id: 6809c1f389b7cade55b32a95
paper_authors.name: Jie Zhang
paper_authors._id: 6809c1f389b7cade55b32a96
paper_authors.name: Qing Guo
paper_authors._id: 6809c1f389b7cade55b32a97
paper_authors.name: Jingyi Wang
paper_authors._id: 6809c1f389b7cade55b32a98
paper_authors.name: Tianlong Chen
paper_authors._id: 6809c1f389b7cade55b32a99
paper_authors.name: Joey Tianyi Zhou
paper_authors._id: 6809c1f389b7cade55b32a9a
paper_authors.name: Xiaojun Jia
paper_authors._id: 6809c1f389b7cade55b32a9b
paper_authors.name: Weisong Sun
paper_authors._id: 6809c1f389b7cade55b32a9c
paper_authors.name: Cong Wu
paper_authors._id: 6809c1f389b7cade55b32a9d
paper_authors.name: Jing Chen
paper_authors._id: 6809c1f389b7cade55b32a9e
paper_authors.name: Xuming Hu
paper_authors._id: 6809c1f389b7cade55b32a9f
paper_authors.name: Yiming Li
paper_authors._id: 6809c1f389b7cade55b32aa0
paper_authors.name: Xiao Wang
paper_authors._id: 6809c1f389b7cade55b32aa1
paper_authors.name: Ningyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-24T09:10:18.968Z
paper_authors._id: 620b3bbb0668e435407c8d0a
paper_authors.avatarUrl: /avatars/e0fccbb2577d76088e09f054c35cffbc.svg
paper_authors.fullname: Ningyu Zhang
paper_authors.type: user
paper_authors.user: Ningyu
paper_authors._id: 6809c1f389b7cade55b32aa2
paper_authors.name: Luu Anh Tuan
paper_authors._id: 6809c1f389b7cade55b32aa3
paper_authors.name: Guowen Xu
paper_authors._id: 6809c1f389b7cade55b32aa4
paper_authors.name: Tianwei Zhang
paper_authors._id: 6809c1f389b7cade55b32aa5
paper_authors.name: Xingjun Ma
paper_authors._id: 6809c1f389b7cade55b32aa6
paper_authors.name: Xiang Wang
paper_authors._id: 6809c1f389b7cade55b32aa7
paper_authors.name: Bo An
paper_authors._id: 6809c1f389b7cade55b32aa8
paper_authors.name: Jun Sun
paper_authors._id: 6809c1f389b7cade55b32aa9
paper_authors.name: Mohit Bansal
paper_authors._id: 6809c1f389b7cade55b32aaa
paper_authors.name: Shirui Pan
paper_authors._id: 6809c1f389b7cade55b32aab
paper_authors.name: Yuval Elovici
paper_authors._id: 6809c1f389b7cade55b32aac
paper_authors.name: Bhavya Kailkhura
paper_authors._id: 6809c1f389b7cade55b32aad
paper_authors.name: Bo Li
paper_authors._id: 6809c1f389b7cade55b32aae
paper_authors.name: Yaodong Yang
paper_authors._id: 6809c1f389b7cade55b32aaf
paper_authors.name: Hongwei Li
paper_authors._id: 6809c1f389b7cade55b32ab0
paper_authors.name: Wenyuan Xu
paper_authors._id: 6809c1f389b7cade55b32ab1
paper_authors.name: Yizhou Sun
paper_authors._id: 6809c1f389b7cade55b32ab2
paper_authors.name: Wei Wang
paper_authors._id: 6809c1f389b7cade55b32ab3
paper_authors.name: Qing Li
paper_authors._id: 6809c1f389b7cade55b32ab4
paper_authors.name: Ke Tang
paper_authors._id: 6809c1f389b7cade55b32ab5
paper_authors.name: Yu-Gang Jiang
paper_authors._id: 6809c1f389b7cade55b32ab6
paper_authors.name: Felix Juefei-Xu
paper_authors._id: 6809c1f389b7cade55b32ab7
paper_authors.name: Hui Xiong
paper_authors._id: 6809c1f389b7cade55b32ab8
paper_authors.name: Xiaofeng Wang
paper_authors._id: 6809c1f389b7cade55b32ab9
paper_authors.name: Shuicheng Yan
paper_authors._id: 6809c1f389b7cade55b32aba
paper_authors.name: Dacheng Tao
paper_authors._id: 6809c1f389b7cade55b32abb
paper_authors.name: Philip S. Yu
paper_authors._id: 6809c1f389b7cade55b32abc
paper_authors.name: Qingsong Wen
paper_authors._id: 6809c1f389b7cade55b32abd
paper_authors.name: Yang Liu
paper_title: A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training
    and Deployment
paper_summary: The remarkable success of Large Language Models (LLMs) has illuminated a
  promising pathway toward achieving Artificial General Intelligence for both
  academic and industrial communities, owing to their unprecedented performance
  across various applications. As LLMs continue to gain prominence in both
  research and commercial domains, their security and safety implications have
  become a growing concern, not only for researchers and corporations but also
  for every nation. Currently, existing surveys on LLM safety primarily focus on
  specific stages of the LLM lifecycle, e.g., deployment phase or fine-tuning
  phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs.
  To address this gap, this paper introduces, for the first time, the concept of
  "full-stack" safety to systematically consider safety issues throughout the
  entire process of LLM training, deployment, and eventual commercialization.
  Compared to the off-the-shelf LLM safety surveys, our work demonstrates several
  distinctive advantages: (I) Comprehensive Perspective. We define the complete
  LLM lifecycle as encompassing data preparation, pre-training, post-training,
  deployment and final commercialization. To our knowledge, this represents the
  first safety survey to encompass the entire lifecycle of LLMs. (II) Extensive
  Literature Support. Our research is grounded in an exhaustive review of over
  800+ papers, ensuring comprehensive coverage and systematic organization of
  security issues within a more holistic understanding. (III) Unique Insights.
  Through systematic literature analysis, we have developed reliable roadmaps and
  perspectives for each chapter. Our work identifies promising research
  directions, including safety in data generation, alignment techniques, model
  editing, and LLM-based agent systems. These insights provide valuable guidance
  for researchers pursuing future work in this field.
paper_ai_summary: This paper introduces the concept of full-stack safety to address the entire lifecycle of Large Language Models (LLMs) from data preparation to commercialization, providing comprehensive insights and promising research directions.
paper_ai_keywords: Large Language Models full-stack safety LLM lifecycle data preparation pre-training post-training deployment commercialization safety survey data generation alignment techniques model editing LLM-based agent systems