title: AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and
    Improvement
summary: As AI models are increasingly deployed across diverse real-world scenarios,
  ensuring their safety remains a critical yet underexplored challenge. While
  substantial efforts have been made to evaluate and enhance AI safety, the lack
  of a standardized framework and comprehensive toolkit poses significant
  obstacles to systematic research and practical adoption. To bridge this gap, we
  introduce AISafetyLab, a unified framework and toolkit that integrates
  representative attack, defense, and evaluation methodologies for AI safety.
  AISafetyLab features an intuitive interface that enables developers to
  seamlessly apply various techniques while maintaining a well-structured and
  extensible codebase for future advancements. Additionally, we conduct empirical
  studies on Vicuna, analyzing different attack and defense strategies to provide
  valuable insights into their comparative effectiveness. To facilitate ongoing
  research and development in AI safety, AISafetyLab is publicly available at
  https://github.com/thu-coai/AISafetyLab, and we are committed to its continuous
  maintenance and improvement.
paper_authors._id: 67bfd8d546083445aacb4605
paper_authors.name: Zhexin Zhang
paper_authors._id: 67bfd8d546083445aacb4606
paper_authors.name: Leqi Lei
paper_authors._id: 67bfd8d546083445aacb4607
paper_authors.name: Junxiao Yang
paper_authors.status: claimed_verified
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paper_authors.fullname: junxiao yang
paper_authors.type: user
paper_authors.user: yangjunxiao2021
paper_authors._id: 67bfd8d546083445aacb4608
paper_authors.name: Xijie Huang
paper_authors._id: 67bfd8d546083445aacb4609
paper_authors.name: Yida Lu
paper_authors._id: 67bfd8d546083445aacb460a
paper_authors.name: Shiyao Cui
paper_authors._id: 67bfd8d546083445aacb460b
paper_authors.name: Renmiao Chen
paper_authors._id: 67bfd8d546083445aacb460c
paper_authors.name: Qinglin Zhang
paper_authors._id: 67bfd8d546083445aacb460d
paper_authors.name: Xinyuan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-13T08:24:43.282Z
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paper_authors.fullname: Xinyuan Wang
paper_authors.type: user
paper_authors.user: buaa42wxy
paper_authors._id: 67bfd8d546083445aacb460e
paper_authors.name: Hao Wang
paper_authors._id: 67bfd8d546083445aacb460f
paper_authors.name: Hao Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-27T09:14:45.366Z
paper_authors._id: 653f1ef4aabbf15fc76a259c
paper_authors.avatarUrl: /avatars/94e569999d913e961266394ea2875965.svg
paper_authors.fullname: LLLeo Li
paper_authors.type: user
paper_authors.user: LLLeo612
paper_authors._id: 67bfd8d546083445aacb4610
paper_authors.name: Xianqi Lei
paper_authors._id: 67bfd8d546083445aacb4611
paper_authors.name: Chengwei Pan
paper_authors._id: 67bfd8d546083445aacb4612
paper_authors.name: Lei Sha
paper_authors._id: 67bfd8d546083445aacb4613
paper_authors.name: Hongning Wang
paper_authors._id: 67bfd8d546083445aacb4614
paper_authors.name: Minlie Huang
paper_title: AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and
    Improvement
paper_summary: As AI models are increasingly deployed across diverse real-world scenarios,
  ensuring their safety remains a critical yet underexplored challenge. While
  substantial efforts have been made to evaluate and enhance AI safety, the lack
  of a standardized framework and comprehensive toolkit poses significant
  obstacles to systematic research and practical adoption. To bridge this gap, we
  introduce AISafetyLab, a unified framework and toolkit that integrates
  representative attack, defense, and evaluation methodologies for AI safety.
  AISafetyLab features an intuitive interface that enables developers to
  seamlessly apply various techniques while maintaining a well-structured and
  extensible codebase for future advancements. Additionally, we conduct empirical
  studies on Vicuna, analyzing different attack and defense strategies to provide
  valuable insights into their comparative effectiveness. To facilitate ongoing
  research and development in AI safety, AISafetyLab is publicly available at
  https://github.com/thu-coai/AISafetyLab, and we are committed to its continuous
  maintenance and improvement.
paper_ai_summary: AISafetyLab provides a unified framework and toolkit for evaluating and enhancing AI safety through integrated attack, defense, and evaluation methodologies.
paper_ai_keywords: AI safety AISafetyLab attack methodologies defense methodologies evaluation methodologies codebase Vicuna comparative effectiveness