RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning

תקציר מקורי באנגליתarXiv:2607.26339v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence. We introduce RAGuard, a layered defense against \emph{factual} corpus-poisoning attacks on RAG pipelines. The first layer adversarially fine-tunes a dense retriever on synthetic poisoned documents (fabricated facts, contradictions, and reasoning traps), teaching it to downrank malicious passages before generation. The second layer, the Zero-Knowledge Inference Patch ZKIP, is a label-free, black-box filter: for each retrieved document, it performs a leave-one-out decode and scores the document by the semantic shift and output-e