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

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. We propose ProGRank, a defense mechanism that operates at the retriever level without requiring retraining. The approach stress-tests each query–passage pair under mild randomized perturbations and extracts probe gradients from the retriever’s parameters to identify potentially poisoned content. By combining representational consistency and dispersion risk metrics in a reranking step, ProGRank provides stronger defence performance and a favorable robustness–utility trade-off, while remaining effective against adaptive attacks across multiple datasets and retriever architectures.