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ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search

Main:10 Pages
11 Figures
Bibliography:5 Pages
14 Tables
Appendix:19 Pages
Abstract

Retrieval-Augmented Generation (RAG) enhances Large Language Models by grounding their outputs in external documents. These systems, however, remain vulnerable to attacks on the retrieval corpus, such as prompt injection. RAG-based search systems (e.g., Google's Search AI Overview) present an interesting setting for studying and protecting against such threats, as defense algorithms can benefit from built-in reliability signals -- like document ranking -- and represent a non-LLM challenge for the adversary due to decades of work to thwart SEO.

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