[2603.22934] ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning

Computer Science > Artificial Intelligence arXiv:2603.22934 (cs) [Submitted on 24 Mar 2026] Title:ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning Authors:Xiangyu Yin, Yi Qi, Chih-hong Cheng View a PDF of the paper titled ProGRank: Probe-Gradient Reranking to Defend Dense-Retriever RAG from Corpus Poisoning, by Xiangyu Yin and Yi Qi and Chih-hong Cheng View PDF HTML (experimental) Abstract:Retrieval-Augmented Generation (RAG) improves the reliability of large language model applications by grounding generation in retrieved evidence, but it also introduces a new attack surface: corpus poisoning. In this setting, an adversary injects or edits passages so that they are ranked into the Top-$K$ results for target queries and then affect downstream generation. Existing defences against corpus poisoning often rely on content filtering, auxiliary models, or generator-side reasoning, which can make deployment more difficult. 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 and extracts probe gradients from a small fixed parameter subset of the retriever. From these signals, it derives two instability signals, representational consistency and dispersion risk, and combines them with a score gate in a reranking step. ProGRank preserves the original passage content, requires no retraining, and also supports a surrogate-ba...