AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

TL;DR

AISafetyLab is a comprehensive, extensible framework and toolkit designed to evaluate and improve AI safety through integrated attack, defense, and evaluation methods, supporting systematic research and practical deployment.

Contribution

It introduces a unified, user-friendly platform that consolidates AI safety evaluation techniques and provides empirical insights through studies on Vicuna models.

Findings

Analysis of attack and defense strategies on Vicuna

Insights into the effectiveness of various safety techniques

A publicly available toolkit for ongoing AI safety research

Abstract

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.