AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement
- URL: http://arxiv.org/abs/2502.16776v1
- Date: Mon, 24 Feb 2025 02:11:52 GMT
- Title: AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement
- Authors: 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,
- Abstract summary: We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
- Score: 73.0700818105842
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Comparative Analysis of AI-Driven Security Approaches in DevSecOps: Challenges, Solutions, and Future Directions [0.0]
This study conducts a systematic literature review to analyze and compare AI-driven security solutions in DevSecOps.
The findings reveal gaps in empirical validation, scalability, and integration of AI in security automation.
The study proposes future directions for optimizing AI-based security frameworks in DevSecOps.
arXiv Detail & Related papers (2025-04-27T08:18:11Z) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.
In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - Trustworthy, Responsible, and Safe AI: A Comprehensive Architectural Framework for AI Safety with Challenges and Mitigations [15.946242944119385]
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems.<n>Our goal is to promote advancement in AI safety research, and ultimately enhance people's trust in digital transformation.
arXiv Detail & Related papers (2024-08-23T09:33:48Z) - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.<n>We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
arXiv Detail & Related papers (2024-07-31T17:59:24Z) - The Art of Defending: A Systematic Evaluation and Analysis of LLM
Defense Strategies on Safety and Over-Defensiveness [56.174255970895466]
Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications.
This paper presents Safety and Over-Defensiveness Evaluation (SODE) benchmark.
arXiv Detail & Related papers (2023-12-30T17:37:06Z) - Cyber Security Requirements for Platforms Enhancing AI Reproducibility [0.0]
This study focuses on the field of artificial intelligence (AI) and introduces a new framework for evaluating AI platforms.
Five popular AI platforms; Floydhub, BEAT, Codalab, Kaggle, and OpenML were assessed.
The analysis revealed that none of these platforms fully incorporates the necessary cyber security measures.
arXiv Detail & Related papers (2023-09-27T09:43:46Z) - Towards Safer Generative Language Models: A Survey on Safety Risks,
Evaluations, and Improvements [76.80453043969209]
This survey presents a framework for safety research pertaining to large models.
We begin by introducing safety issues of wide concern, then delve into safety evaluation methods for large models.
We explore the strategies for enhancing large model safety from training to deployment.
arXiv Detail & Related papers (2023-02-18T09:32:55Z) - Evaluating Model-free Reinforcement Learning toward Safety-critical
Tasks [70.76757529955577]
This paper revisits prior work in this scope from the perspective of state-wise safe RL.
We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection.
To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
arXiv Detail & Related papers (2022-12-12T06:30:17Z) - TanksWorld: A Multi-Agent Environment for AI Safety Research [5.218815947097599]
The ability to create artificial intelligence capable of performing complex tasks is rapidly outpacing our ability to ensure the safe and assured operation of AI-enabled systems.
Recent simulation environments to illustrate AI safety risks are relatively simple or narrowly-focused on a particular issue.
We introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects.
arXiv Detail & Related papers (2020-02-25T21:00:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.