Jailbreaking as a Reward Misspecification Problem

Xie, Jiahui Gao, Lei Li, Zhenguo Li, Qi Liu, Lingpeng Kong

Jailbreaking as a Reward Misspecification Problem: 14 upvotes on Hugging Face Daily Papers, #9 of 27 papers on 2024-06-24. Day-by-day upvote history.

The widespread adoption of large language models (LLMs) has raised concerns about their safety and reliability, particularly regarding their vulnerability to adversarial attacks. In this paper, we propose a novel perspective that attributes this vulnerability to reward misspecification during the alignment process. We introduce a metric ReGap to quantify the extent of reward misspecification and demonstrate its effectiveness and robustness in detecting harmful backdoor prompts. Building upon these insights, we present ReMiss, a system for automated red teaming that generates adversarial prompts against various target aligned LLMs. ReMiss achieves state-of-the-art attack success rates on the AdvBench benchmark while preserving the human readability of the generated prompts. Detailed analysis highlights the unique advantages brought by the proposed reward misspecification objective compared to previous methods.

Paper page on Hugging Face · arXiv

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