Is poisoning a real threat to LLM alignment? Maybe more so than you think

Pankayaraj Pathmanathan, Souradip Chakraborty, Xiangyu Liu, Yongyuan Liang, Furong Huang

Keywords: RLHF Poisoning, LLM Alignment

Abstract:

Recent advancements in Reinforcement Learning with Human Feedback (RLHF)
have significantly impacted the alignment of Large Language Models (LLMs).
The sensitivity of reinforcement learning algorithms such as Proximal Policy
Optimization (PPO) has led to new line work on Direct Policy Optimization (DPO),
which treats RLHF in a supervised learning framework. The increased practical
use of these RLHF methods warrants an analysis of their vulnerabilities. In this
work, we investigate the vulnerabilities of DPO to poisoning attacks under different
scenarios and compare the effectiveness of preference poisoning, a first of its kind.
We comprehensively analyze DPO’s vulnerabilities under different types of attacks,
i.e., backdoor and non-backdoor attacks, and different poisoning methods across a
wide array of language models, i.e., LLama 7B, Mistral 7B, and Gemma 7B. We
find that unlike PPO-based methods, which, when it comes to backdoor attacks,
require at least 4% of the data to be poisoned to elicit harmful behavior, we exploit
the true vulnerabilities of DPO more simply so we can poison the model with
only as much as 0.5% of the data. We further investigate the potential reasons
behind the vulnerability and how well this vulnerability translates into backdoor vs
non-backdoor attacks

Submission Number: 47