打造AI安全评估与提升的统一工具框架，助力模型安全研究落地。
AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement
- 集成攻击、防御与评估方法，提供可扩展的统一代码框架。
- 在Vicuna模型上实证分析多种策略效果，揭示优劣对比。
- 开源易用，适合研究人员和开发者快速开展安全实验。
随着人工智能模型在多样化现实场景中的广泛应用，确保其安全性成为关键但尚未充分探索的挑战。尽管已有大量工作致力于评估和提升AI安全性，但缺乏标准化框架和综合性工具包，严重制约了系统性研究与实际应用。为此，我们提出AISafetyLab，一个整合代表性攻击、防御与评估方法的统一框架与工具集。该框架具备直观界面，支持开发者便捷调用各类技术，同时保持结构清晰、易于扩展的代码库，便于未来演进。我们还基于Vicuna模型开展了实证研究，分析不同攻击与防御策略的相对有效性，为方法选择提供参考。为促进持续研究与开发，AISafetyLab已开源至https://github.com/thu-coai/AISafetyLab，团队将持续维护与优化。
原文摘要 · Abstract (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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。