Study Reveals Vulnerabilities in Retrieval-Augmented Generation Systems to Corpus Poisoning Attacks
Researchers found that existing corpus poisoning attacks on Retrieval-Augmented Generation (RAG) systems often fail when tested against realistic multi-stage retrieval pipelines that include document chunking and reranking. The study identifies retrieval granularity mismatch—where adversarial signals fragment during chunking—as a key reason attacks degrade in practice. The findings highlight a significant gap between simplified security evaluations and real-world RAG system vulnerabilities, with implications for developing more robust AI safety measures.
A new arXiv study examines how corpus poisoning attacks—which inject malicious knowledge to manipulate AI outputs—perform against practical RAG systems. While previous research evaluated poisoning under simplified conditions, this work tests attacks against realistic pipelines involving document chunking, dense retrieval, reranking, and grounded generation. The researchers discovered that many existing attacks achieve high relevance at the retrieval stage but substantially degrade after reranking. They attribute this failure to retrieval granularity mismatch: adversarial signals embedded at the document level fragment during chunking, while rerankers prioritize locally coherent passages over globally optimized semantic similarity. To address this, the team proposes CRCP (Chunk-aware and Rerank-Consistent Poisoning), a framework that jointly optimizes for retrieval relevance, reranker consistency, and robustness across varying chunk sizes. Experiments demonstrate that CRCP achieves higher attack success rates and greater robustness than existing methods across multiple retrievers and rerankers.
What's missing
The study does not discuss potential defenses or mitigation strategies beyond identifying the vulnerability. Additionally, while the paper addresses RAG system security, it does not examine whether findings generalize to other retrieval-augmented architectures or non-English languages.
What different sources said
- arXiv cs.AICenter
When Poison Fails After Retrieval: Revisiting Corpus Poisoning under Chunking and Reranking Pipelines
- arXiv cs.AICenter
ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning
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