GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation

• URL: http://arxiv.org/abs/2405.13077v2
• Date: Tue, 15 Oct 2024 22:50:58 GMT
• Title: GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation
• Authors: Govind Ramesh, Yao Dou, Wei Xu,
• Abstract summary: We introduce Iterative Refinement Induced Self-Jailbreak (IRIS), a novel approach to jailbreaking with only black-box access. Unlike previous methods, IRIS simplifies the jailbreaking process by using a single model as both the attacker and target. We find that IRIS jailbreak success rates of 98% on GPT-4, 92% on GPT-4 Turbo, and 94% on Llama-3.1-70B in under 7 queries.
• Score: 9.377563769107843
• License:
• Abstract: Research on jailbreaking has been valuable for testing and understanding the safety and security issues of large language models (LLMs). In this paper, we introduce Iterative Refinement Induced Self-Jailbreak (IRIS), a novel approach that leverages the reflective capabilities of LLMs for jailbreaking with only black-box access. Unlike previous methods, IRIS simplifies the jailbreaking process by using a single model as both the attacker and target. This method first iteratively refines adversarial prompts through self-explanation, which is crucial for ensuring that even well-aligned LLMs obey adversarial instructions. IRIS then rates and enhances the output given the refined prompt to increase its harmfulness. We find that IRIS achieves jailbreak success rates of 98% on GPT-4, 92% on GPT-4 Turbo, and 94% on Llama-3.1-70B in under 7 queries. It significantly outperforms prior approaches in automatic, black-box, and interpretable jailbreaking, while requiring substantially fewer queries, thereby establishing a new standard for interpretable jailbreaking methods.

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