ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning
Retrieval-Augmented Generation (RAG) improves large language model applications by grounding generation in retrieved evidence, but also introduces corpus poisoning as a new attack surface. In this setting, an adversary injects or edits passages so that they enter the Top-$K$ results for target queries and influence downstream generation. Existing defences often rely on content filtering, auxiliary models, or generator-side reasoning, which complicates deployment. We propose ProGRank, a post hoc, training-free retriever-side defence for dense-retriever RAG. ProGRank stress-tests each query--passage pair under mild randomized perturbations, extracts probe gradients from a small fixed parame...
| Authors | Xiangyu Yin, Yi Qi, Chih-Hong Cheng |
|---|---|
| Primary category | cs.AI |
| Published | 24 March 2026 |
| Last updated at source | 10 June 2026 |
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