Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data

TL;DR

This paper demonstrates that injecting a small amount of poisoned preference data into RLHF training datasets can effectively manipulate language model outputs, highlighting vulnerabilities and potential defense strategies.

Contribution

The study introduces methods for creating poisonous preference pairs and evaluates their effectiveness in manipulating language models trained with RLHF.

Findings

Poisoning 1-5% of data can control model outputs.

Preference poisoning significantly impacts model alignment.

Strategies for defending against poisoning are discussed.

Abstract

Reinforcement Learning from Human Feedback (RLHF) is a popular method for aligning Language Models (LM) with human values and preferences. RLHF requires a large number of preference pairs as training data, which are often used in both the Supervised Fine-Tuning and Reward Model training and therefore publicly available datasets are commonly used. In this work, we study to what extent a malicious actor can manipulate the LMs generations by poisoning the preferences, i.e., injecting poisonous preference pairs into these datasets and the RLHF training process. We propose strategies to build poisonous preference pairs and test their performance by poisoning two widely used preference datasets. Our results show that preference poisoning is highly effective: injecting a small amount of poisonous data (1-5\% of the original dataset), we can effectively manipulate the LM to generate a target entity in a target sentiment (positive or negative). The findings from our experiments also shed light on strategies to defend against the preference poisoning attack.