Bibliographic Information: Yue Liu, Xiaoxin He, Miao Xiong, Jinlan Fu, Shumin Deng, Bryan Hooi. (2024). FlipAttack: Jailbreak LLMs via Flipping. arXiv Pre-print.
Research Objective: This paper introduces FlipAttack, a novel black-box jailbreak attack method designed to circumvent safety measures in Large Language Models (LLMs) and manipulate them into producing harmful content. The research aims to demonstrate the effectiveness of FlipAttack against various state-of-the-art LLMs and highlight the vulnerabilities of existing safety alignment techniques.
Methodology: FlipAttack leverages the autoregressive nature of LLMs, exploiting their tendency to process text from left to right. The attack involves two key modules:
Attack Disguise Module: This module disguises harmful prompts by introducing "left-side noise" generated by flipping characters within the prompt itself. This technique hinders the LLM's ability to recognize and reject the harmful content, effectively bypassing safety guardrails. Four flipping modes are proposed: flipping word order, flipping characters within words, flipping characters within the entire sentence, and a "fool model mode" that intentionally misdirects the LLM's denoising process.
Flipping Guidance Module: Once the harmful prompt is disguised, this module guides the LLM to decode the flipped text, understand the underlying harmful intent, and ultimately execute the harmful instructions. This is achieved through various techniques like chain-of-thought reasoning, role-playing prompting, and few-shot in-context learning, enabling FlipAttack to manipulate even weaker LLMs.
The researchers evaluate FlipAttack's effectiveness on eight different LLMs, including GPT-3.5 Turbo, GPT-4, GPT-4o, Claude 3.5 Sonnet, LLaMA 3.1 405B, and Mixtral 8x22B. They measure the attack success rate (ASR) based on the LLM's ability to bypass safety measures and produce the desired harmful output.
Key Findings: FlipAttack demonstrates remarkable effectiveness in jailbreaking black-box LLMs, achieving an average attack success rate of 81.80% across the tested models. Notably, it achieves a 98.85% success rate on GPT-4 Turbo and 98.08% on GPT-4o. The research also highlights the vulnerability of existing guard models, with FlipAttack achieving a 98.08% average bypass rate.
Main Conclusions: FlipAttack's success underscores the vulnerability of current LLMs to jailbreak attacks, even when the attacker lacks access to model weights or gradients. The simplicity and effectiveness of FlipAttack raise concerns about the robustness of existing safety alignment techniques and emphasize the need for more sophisticated defense mechanisms.
Significance: This research significantly contributes to the field of LLM security by introducing a novel and potent black-box jailbreak attack method. The findings have important implications for the development and deployment of LLMs, particularly in security-critical domains, urging researchers and practitioners to prioritize the development of robust defense strategies against such attacks.
Limitations and Future Research: While highly effective, FlipAttack's reliance on character flipping as the primary disguise mechanism may limit its applicability against future LLMs trained on more diverse and potentially flipped datasets. Further research is needed to explore alternative disguise techniques and develop more robust defense strategies that can effectively mitigate the threat posed by FlipAttack and similar jailbreak attacks.
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by Yue Liu, Xia... at arxiv.org 10-07-2024
https://arxiv.org/pdf/2410.02832.pdfDeeper Inquiries