Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs
Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised fine-tuning (SFT) can reduce refusal behavior, deployed fine-tuning pipelines increasingly support preference-based objectives, whose safety risks remain less understood. We show that Direct Preference Optimization (DPO) introduces a stronger and harder-to-audit failure mode. We propose a truly benign DPO attack using only 10 harmless preference pairs, the minimum data scale accepted by OpenAI's fine-tuning service. Each pair contains a benign prompt, a normal helpful answer as the preferred response, and a refusal as the dispre...
| Authors | Sangyeon Yoon, Wonje Jeung, Yoonjun Cho et al. |
|---|---|
| Primary category | cs.CR |
| Published | 9 May 2026 |
| Last updated at source | 14 October 2026 |
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