AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and
  Improvement
Authors: name: Zhexin Zhang, name: Leqi Lei, name: Junxiao Yang, name: Xijie Huang, name: Yida Lu, name: Shiyao Cui, name: Renmiao Chen, name: Qinglin Zhang, name: Xinyuan Wang, name: Hao Wang, name: Hao Li, name: Xianqi Lei, name: Chengwei Pan, name: Lei Sha, name: Hongning Wang, name: Minlie Huang

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.