AISafetyLab：一个用于AI安全评估与提升的综合性框架

AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

February 24, 2025

作者: Zhexin Zhang, Leqi Lei, Junxiao Yang, Xijie Huang, Yida Lu, Shiyao Cui, Renmiao Chen, Qinglin Zhang, Xinyuan Wang, Hao Wang, Hao Li, Xianqi Lei, Chengwei Pan, Lei Sha, Hongning Wang, Minlie Huang

cs.AI

摘要

随着AI模型在多样化现实场景中的广泛应用，确保其安全性仍是一项关键但尚未充分探索的挑战。尽管在评估和提升AI安全方面已投入大量努力，但标准化框架与全面工具集的缺失，为系统性研究和实际应用带来了显著障碍。为弥合这一差距，我们推出了AISafetyLab，一个集成了代表性攻击、防御及评估方法的统一框架与工具包。AISafetyLab具备直观界面，使开发者能够无缝应用多种技术，同时保持代码库结构清晰、易于扩展，以支持未来进步。此外，我们在Vicuna上进行了实证研究，分析不同攻防策略，为比较其有效性提供了宝贵见解。为促进AI安全领域的持续研究与开发，AISafetyLab已在https://github.com/thu-coai/AISafetyLab公开，我们承诺将持续维护与改进该平台。

English

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.