TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation
This addresses security vulnerabilities in RAG systems for users relying on LLMs with external knowledge, though it appears incremental as it builds on existing defense mechanisms.
The paper tackles the problem of corpus poisoning attacks in Retrieval-Augmented Generation (RAG) systems, which impair LLM performance, by proposing TrustRAG, a robust framework that filters malicious content, resulting in substantial improvements in retrieval accuracy, efficiency, and attack resistance.
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user queries. These systems, however, remain susceptible to corpus poisoning attacks, which can severely impair the performance of LLMs. To address this challenge, we propose TrustRAG, a robust framework that systematically filters malicious and irrelevant content before it is retrieved for generation. Our approach employs a two-stage defense mechanism. The first stage implements a cluster filtering strategy to detect potential attack patterns. The second stage employs a self-assessment process that harnesses the internal capabilities of LLMs to detect malicious documents and resolve inconsistencies. TrustRAG provides a plug-and-play, training-free module that integrates seamlessly with any open- or closed-source language model. Extensive experiments demonstrate that TrustRAG delivers substantial improvements in retrieval accuracy, efficiency, and attack resistance.