LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds

• URL: http://arxiv.org/abs/2412.05232v2
• Date: Mon, 10 Feb 2025 16:22:08 GMT
• Title: LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds
• Authors: James Beetham, Souradip Chakraborty, Mengdi Wang, Furong Huang, Amrit Singh Bedi, Mubarak Shah,
• Abstract summary: LIAR (Leveraging Inference time Alignment to jailbReak) is a fast and efficient best-of-N approach tailored for jailbreak attacks. Our results demonstrate that a best-of-N approach is a simple yet highly effective strategy for evaluating the robustness of aligned LLMs.
• Score: 98.20826635707341
• License:
• Abstract: Traditional jailbreaks have successfully exposed vulnerabilities in LLMs, primarily relying on discrete combinatorial optimization, while more recent methods focus on training LLMs to generate adversarial prompts. However, both approaches are computationally expensive and slow, often requiring significant resources to generate a single successful attack. We hypothesize that the inefficiency of these methods arises from an inadequate characterization of the jailbreak problem itself. To address this gap, we approach the jailbreak problem as an alignment problem, leading us to propose LIAR (Leveraging Inference time Alignment to jailbReak), a fast and efficient best-of-N approach tailored for jailbreak attacks. LIAR offers several key advantages: it eliminates the need for additional training, operates in a fully black-box setting, significantly reduces computational overhead, and produces more human-readable adversarial prompts while maintaining competitive attack success rates. Our results demonstrate that a best-of-N approach is a simple yet highly effective strategy for evaluating the robustness of aligned LLMs, achieving attack success rates (ASR) comparable to state-of-the-art methods while offering a 10x improvement in perplexity and a significant speedup in Time-to-Attack, reducing execution time from tens of hours to seconds. Additionally, We also provide sub-optimality guarantees for the proposed LIAR. Our work highlights the potential of efficient, alignment-based jailbreak strategies for assessing and stress-testing AI safety measures.

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