EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2024
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| author | Zhou, Weikang Wang, Xiao Xiong, Limao Xia, Han Gu, Yingshuang Chai, Mingxu Zhu, Fukang Huang, Caishuang Dou, Shihan Xi, Zhiheng Zheng, Rui Gao, Songyang Zou, Yicheng Yan, Hang Le, Yifan Wang, Ruohui Li, Lijun Shao, Jing Gui, Tao Zhang, Qi Huang, Xuanjing |
| author_facet | Zhou, Weikang Wang, Xiao Xiong, Limao Xia, Han Gu, Yingshuang Chai, Mingxu Zhu, Fukang Huang, Caishuang Dou, Shihan Xi, Zhiheng Zheng, Rui Gao, Songyang Zou, Yicheng Yan, Hang Le, Yifan Wang, Ruohui Li, Lijun Shao, Jing Gui, Tao Zhang, Qi Huang, Xuanjing |
| contents | Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibited outputs. However, due to significant differences among various jailbreak methods, there is no standard implementation framework available for the community, which limits comprehensive security evaluations. This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs. It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator. This modular framework enables researchers to easily construct attacks from combinations of novel and existing components. So far, EasyJailbreak supports 11 distinct jailbreak methods and facilitates the security validation of a broad spectrum of LLMs. Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks. Notably, even advanced models like GPT-3.5-Turbo and GPT-4 exhibit average Attack Success Rates (ASR) of 57% and 33%, respectively. We have released a wealth of resources for researchers, including a web platform, PyPI published package, screencast video, and experimental outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12171 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models Zhou, Weikang Wang, Xiao Xiong, Limao Xia, Han Gu, Yingshuang Chai, Mingxu Zhu, Fukang Huang, Caishuang Dou, Shihan Xi, Zhiheng Zheng, Rui Gao, Songyang Zou, Yicheng Yan, Hang Le, Yifan Wang, Ruohui Li, Lijun Shao, Jing Gui, Tao Zhang, Qi Huang, Xuanjing Computation and Language Artificial Intelligence Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibited outputs. However, due to significant differences among various jailbreak methods, there is no standard implementation framework available for the community, which limits comprehensive security evaluations. This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against LLMs. It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator. This modular framework enables researchers to easily construct attacks from combinations of novel and existing components. So far, EasyJailbreak supports 11 distinct jailbreak methods and facilitates the security validation of a broad spectrum of LLMs. Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks. Notably, even advanced models like GPT-3.5-Turbo and GPT-4 exhibit average Attack Success Rates (ASR) of 57% and 33%, respectively. We have released a wealth of resources for researchers, including a web platform, PyPI published package, screencast video, and experimental outputs. |
| title | EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.12171 |