Certifiably Robust RAG against Retrieval Corruption Attacks
Keywords: Retrieval-augemented Generation, Retrieval Corruption Attack, Certifiable Robustness
TL;DR: We propose the first certifiably robust defense for retrieval-augmented generation (RAG) against retrieval corruption attacks
Abstract: Retrieval-augmented generation (RAG) has been shown vulnerable to retrieval corruption attacks: an attacker can inject malicious passages into retrieval results to induce inaccurate responses. In this paper, we propose RobustRAG as the first defense framework against retrieval corruption attacks. The key insight of RobustRAG is an isolate-then-aggregate strategy: we isolate passages into disjoint groups, generate LLM responses based on the concatenated passages from each isolated group, and then securely aggregate these responses for a robust output. To instantiate RobustRAG, we design keyword-based and decoding-based algorithms for securely aggregating unstructured text responses. Notably, RobustRAG can achieve certifiable robustness: we can formally prove and certify that, for certain queries, RobustRAG can always return accurate responses, even when an adaptive attacker has full knowledge of our defense and can arbitrarily inject a small number of malicious passages. We evaluate RobustRAG on open-domain QA and long-form text generation datasets and demonstrate its effectiveness and generalizability.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 4800
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