Efficient Preference Poisoning Attack on Offline RLHF
Offline Reinforcement Learning from Human Feedback (RLHF) pipelines such as Direct Preference Optimization (DPO) train on a pre-collected preference dataset, which makes them vulnerable to preference poisoning attack. We study label flip attacks against log-linear DPO. We first illustrate that flipping one preference label induces a parameter-independent shift in the DPO gradient. Using this key property, we can then convert the targeted poisoning problem into a structured binary sparse approximation problem. To solve this problem, we develop two attack methods: Binary-Aware Lattice Attack (BAL-A) and Binary Matching Pursuit Attack (BMP-A). BAL-A embeds the binary flip selection problem i...
| Authors | Chenye Yang, Weiyu Xu, Lifeng Lai |
|---|---|
| Primary category | cs.LG |
| Published | 4 May 2026 |
| Last updated at source | 25 May 2026 |
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