Knowledge Database or Poison Base? Detecting RAG Poisoning Attack through LLM Activations

As Large Language Models (LLMs) are progressively deployed across diverse fields and real-world applications, ensuring the security and robustness of LLMs has become ever more critical. Retrieval-Augmented Generation (RAG) is a cutting-edge approach designed to address the limitations of large language models (LLMs). By retrieving information from the relevant knowledge database, RAG enriches the input to LLMs, enabling them to produce responses that are more accurate and contextually appropriate. It is worth noting that the knowledge database, being sourced from publicly available channels such as Wikipedia, inevitably introduces a new attack surface. RAG poisoning involves injecting malicious texts into the knowledge database, ultimately leading to the generation of the attacker's target response (also called poisoned response). However, there are currently limited methods available for detecting such poisoning attacks. We aim to bridge the gap in this work. Particularly, we introduce RevPRAG, a flexible and automated detection pipeline that leverages the activations of LLMs for poisoned response detection. Our investigation uncovers distinct patterns in LLMs' activations when generating correct responses versus poisoned responses. Our results on multiple benchmark datasets and RAG architectures show our approach could achieve 98% true positive rate, while maintaining false positive rates close to 1%. We also evaluate recent backdoor detection methods specifically designed for LLMs and applicable for identifying poisoned responses in RAG. The results demonstrate that our approach significantly surpasses them.