Enhancing Jailbreak Attacks via Compliance-Refusal-Based Initialization

• URL: http://arxiv.org/abs/2502.09755v1
• Date: Thu, 13 Feb 2025 20:25:40 GMT
• Title: Enhancing Jailbreak Attacks via Compliance-Refusal-Based Initialization
• Authors: Amit Levi, Rom Himelstein, Yaniv Nemcovsky, Avi Mendelson, Chaim Baskin,
• Abstract summary: We propose textbfCompliance textbfRefusal textbfInitialization (CRI)<n>CRI is an attack-agnostic framework that efficiently initializes the optimization in the proximity of the compliance subspace of harmful prompts.<n>We evaluate CRI on the widely-used AdvBench dataset over the standard jailbreak attacks of GCG and AutoDAN.
• Score: 4.506537904404427
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Jailbreak attacks aim to exploit large language models (LLMs) and pose a significant threat to their proper conduct; they seek to bypass models' safeguards and often provoke transgressive behaviors. However, existing automatic jailbreak attacks require extensive computational resources and are prone to converge on suboptimal solutions. In this work, we propose \textbf{C}ompliance \textbf{R}efusal \textbf{I}nitialization (CRI), a novel, attack-agnostic framework that efficiently initializes the optimization in the proximity of the compliance subspace of harmful prompts. By narrowing the initial gap to the adversarial objective, CRI substantially improves adversarial success rates (ASR) and drastically reduces computational overhead -- often requiring just a single optimization step. We evaluate CRI on the widely-used AdvBench dataset over the standard jailbreak attacks of GCG and AutoDAN. Results show that CRI boosts ASR and decreases the median steps to success by up to \textbf{\(\times 60\)}. The project page, along with the reference implementation, is publicly available at \texttt{https://amit1221levi.github.io/CRI-Jailbreak-Init-LLMs-evaluation/}.

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