AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

2025-02-24

AI Summary

To address the lack of standardized frameworks for safety evaluation and defense of large language models (LLMs) in real-world scenarios, this paper introduces the first open-source, modular, and extensible AI safety framework. It features a unified interface that integrates state-of-the-art adversarial attack methods (e.g., prompt injection, jailbreaking), robust defense mechanisms (e.g., input sanitization, response filtering), and multidimensional safety evaluation metrics (e.g., harmfulness, consistency, controllability). Implemented in Python, the framework enables systematic red-teaming and defense benchmarking. We conduct the first cross-method empirical analysis on the Vicuna model, revealing up to 47% variance in attack success rates and demonstrating an average risk reduction of 63% under defense interventions. The framework significantly improves experimental reproducibility and is publicly available on GitHub with ongoing maintenance.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Lack of standardized AI safety framework
• Need for comprehensive AI safety toolkit
• Challenges in systematic AI safety research

Innovation

• Unified AI safety framework
• Integrated attack-defense-evaluation methodologies
• Public extensible toolkit for AI safety