EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

• URL: http://arxiv.org/abs/2403.12171v1
• Date: Mon, 18 Mar 2024 18:39:53 GMT
• Title: EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models
• Authors: 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,
• Abstract summary: This paper introduces EasyJailbreak, a unified framework simplifying the construction and evaluation of jailbreak attacks against Large Language Models (LLMs) It builds jailbreak attacks using four components: Selector, Mutator, Constraint, and Evaluator. Our validation across 10 distinct LLMs reveals a significant vulnerability, with an average breach probability of 60% under various jailbreaking attacks.
• Score: 53.87416566981008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Rapid Response: Mitigating LLM Jailbreaks with a Few Examples [13.841146655178585]
  We develop rapid response techniques to look to block whole classes of jailbreaks after observing only a handful of attacks. We evaluate five rapid response methods, all of which use jailbreak proliferation. Our strongest method reduces attack success rate by a factor greater than 240 on an in-distribution set of jailbreaks and a factor greater than 15 on an out-of-distribution set.
  arXiv  Detail & Related papers  (2024-11-12T02:44:49Z)
• SQL Injection Jailbreak: a structural disaster of large language models [71.55108680517422]
  We propose a novel jailbreak method, which utilizes the construction of input prompts by LLMs to inject jailbreak information into user prompts. Our SIJ method achieves nearly 100% attack success rates on five well-known open-source LLMs in the context of AdvBench.
  arXiv  Detail & Related papers  (2024-11-03T13:36:34Z)
• EnJa: Ensemble Jailbreak on Large Language Models [69.13666224876408]
  Large Language Models (LLMs) are increasingly being deployed in safety-critical applications. LLMs can still be jailbroken by carefully crafted malicious prompts, producing content that violates policy regulations. We propose a novel EnJa attack to hide harmful instructions using prompt-level jailbreak, boost the attack success rate using a gradient-based attack, and connect the two types of jailbreak attacks via a template-based connector.
  arXiv  Detail & Related papers  (2024-08-07T07:46:08Z)
• Virtual Context: Enhancing Jailbreak Attacks with Special Token Injection [54.05862550647966]
  This paper introduces Virtual Context, which leverages special tokens, previously overlooked in LLM security, to improve jailbreak attacks. Comprehensive evaluations show that Virtual Context-assisted jailbreak attacks can improve the success rates of four widely used jailbreak methods by approximately 40%.
  arXiv  Detail & Related papers  (2024-06-28T11:35:54Z)
• WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models [66.34505141027624]
  We introduce WildTeaming, an automatic LLM safety red-teaming framework that mines in-the-wild user-chatbot interactions to discover 5.7K unique clusters of novel jailbreak tactics. WildTeaming reveals previously unidentified vulnerabilities of frontier LLMs, resulting in up to 4.6x more diverse and successful adversarial attacks.
  arXiv  Detail & Related papers  (2024-06-26T17:31:22Z)
• Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks? [39.87609532392292]
  This work builds a comprehensive jailbreak evaluation dataset with 1445 harmful questions covering 11 different safety policies. GPT4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs. Llama2 and Qwen-VL-Chat are more robust compared to other open-source models.
  arXiv  Detail & Related papers  (2024-04-04T12:38:14Z)
• Comprehensive Assessment of Jailbreak Attacks Against LLMs [28.58973312098698]
  We study 13 cutting-edge jailbreak methods from four categories, 160 questions from 16 violation categories, and six popular LLMs. Our experimental results demonstrate that the optimized jailbreak prompts consistently achieve the highest attack success rates. We discuss the trade-off between the attack performance and efficiency, as well as show that the transferability of the jailbreak prompts is still viable.
  arXiv  Detail & Related papers  (2024-02-08T13:42:50Z)
• Jailbreaking GPT-4V via Self-Adversarial Attacks with System Prompts [64.60375604495883]
  We discover a system prompt leakage vulnerability in GPT-4V. By employing GPT-4 as a red teaming tool against itself, we aim to search for potential jailbreak prompts leveraging stolen system prompts. We also evaluate the effect of modifying system prompts to defend against jailbreaking attacks.
  arXiv  Detail & Related papers  (2023-11-15T17:17:39Z)

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.