EasyJailbreak: A Unified Framework for Jailbreaking Large Language
  Models
Weikang Zhou Xiao Wang Limao Xiong Han Xia Yingshuang Gu Mingxu Chai Fukang Zhu Caishuang Huang Shihan Dou Zhiheng Xi Rui Zheng Songyang Gao Yicheng Zou Hang Yan Yifan Le Ruohui Wang Lijun Li Jing Shao Tao Gui Qi Zhang Xuanjing Huang
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.