KidnapRAG: A Black-Box Attack for Hijacking Reasoning in Agentic Retrieval-Augmented Generation Systems
Black-box poisoning attack on agentic RAG systems bypasses iterative retrieval defences, showing that multi-step reasoning alone does not neutralise knowledge-base manipulation.
Summary written by editorial AI · Source link below
arXiv:2607.00422v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to poisoning attacks that inject malicious documents into the retrieval process to manipulate model outputs. Recent Agentic RAG systems are more robust to such attacks because they iteratively perform retrieval and reasoning, allowing them to ignore weakly relevant poisoned documents and preserve the reasoning chain induced by the user query. However, existing attacks on Agentic RAG syste
Editorial Analysis
Enterprises adopting RAG-based AI assistants often assume iterative retrieval adds robustness; this research demonstrates that assumption is unsafe, requiring additional integrity controls on knowledge bases.
Implement integrity verification and provenance tracking for all documents ingested into enterprise RAG knowledge bases.
RAG-powered AI assistants can be manipulated via poisoned knowledge bases even with multi-step reasoning — a risk for AI-enabled decision support.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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