GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation
<|reference_start|>GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation: 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 IRIS achieves jailbreak success rates of 98% on GPT-4 and 92% on GPT-4 Turbo 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.<|reference_end|>
@article{ramesh2024gpt-4,
    title={GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation},
    author={Govind Ramesh, Yao Dou, Wei Xu},
    journal={arXiv preprint arXiv:2405.13077},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13077},
    primaryClass={cs.CR cs.AI cs.CL}
}