Secure Retrieval-Augmented Generation against Poisoning Attacks
This addresses security risks in RAG systems for users relying on external knowledge, though it is incremental as it builds on existing defenses.
The paper tackles the problem of data poisoning attacks in Retrieval-Augmented Generation (RAG) systems by introducing RAGuard, a detection framework that identifies poisoned texts through retrieval expansion and filtering methods, with experiments showing its effectiveness against advanced attacks.
Large language models (LLMs) have transformed natural language processing (NLP), enabling applications from content generation to decision support. Retrieval-Augmented Generation (RAG) improves LLMs by incorporating external knowledge but also introduces security risks, particularly from data poisoning, where the attacker injects poisoned texts into the knowledge database to manipulate system outputs. While various defenses have been proposed, they often struggle against advanced attacks. To address this, we introduce RAGuard, a detection framework designed to identify poisoned texts. RAGuard first expands the retrieval scope to increase the proportion of clean texts, reducing the likelihood of retrieving poisoned content. It then applies chunk-wise perplexity filtering to detect abnormal variations and text similarity filtering to flag highly similar texts. This non-parametric approach enhances RAG security, and experiments on large-scale datasets demonstrate its effectiveness in detecting and mitigating poisoning attacks, including strong adaptive attacks.