When Poison Meets Structure: Topology-based Defense against Poisoning Attack on Graph-based Retrieval-Augmented Generation

Poisoning attacks against GraphRAG focus on knowledge pollution at the node and community levels, but conventional poisoning defenses mainly rely on sentence-level semantic features, which makes them less effective against such attacks. To address this issue, we analyze the poisoning strategy from a game-theoretic perspective and find that attackers prioritize fabricating query-related facts while ignoring supporting background knowledge, which in turn induces a systematic topological discrepancy between poisoned and clean subgraphs. Based on this insight, we propose the lightweight T opology-based D efense against P oisoning Attack on GraphRAG (TDP). TDP constructs a pair of conflicting candidate subgraphs from the retrieved evidence and trains a pairwise topology-ranking discriminator to distinguish clean evidence with dense cross-validation from poisoned evidence with sparse structural support, thereby removing poisoned subgraphs. Notably, the topological patterns captured by TDP reflect structural preferences induced by the poisoning game rather than dataset-specific distributional biases, allowing it to be transferred as a plug-and-play module after pretraining without end-to-end retraining. To the best of our knowledge, this is the first systematic work on poisoning defense for GraphRAG, and experiments show that TDP achieves state-of-the-art defense performance across multiple benchmarks and poisoning attacks, demonstrating strong generalization.