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Defense Against Knowledge Poisoning Attack on GraphRAG
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
GraphRAG augments large language models with structured knowledge graphs, enabling graph-based context selection and a more integrated view of the knowledge space. However, recent work shows that GraphRAG exposes a new attack surface: corpus-level knowledge poisoning can inject spurious entities and relationships during graph construction, corrupting query-specific subgraphs and steering the generator toward incorrect answers. We propose Hop-wise Guard for GraphRAG (HoG-GRAG), a defense layer between retriever and generator that decomposes multi-hop questions into ordered subqueries, monitors hop-wise execution for poisoning-induced inconsistencies, and locally repairs the retrieved subgraph by pruning compromised entities and relationships and adding only minimal missing evidence. Experiments on multi-hop datasets and multiple GraphRAG configurations show that HoG-GRAG recovers a large fraction of the lost performance. The code is available at https://github.com/CyberScienceLab/HoG-GRAG.
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记录 #1 · 2026-07 · 最新观测版本
Defense Against Knowledge Poisoning Attack on GraphRAG
GraphRAG augments large language models with structured knowledge graphs, enabling graph-based context selection and a more integrated view of the knowledge space. However, recent work shows that GraphRAG exposes a new attack surface: corpus-level knowledge poisoning can inject spurious entities and relationships during graph construction, corrupting query-specific subgraphs and steering the generator toward incorrect answers. We propose Hop-wise Guard for GraphRAG (HoG-GRAG), a defense layer between retriever and generator that decomposes multi-hop questions into ordered subqueries, monitors hop-wise execution for poisoning-induced inconsistencies, and locally repairs the retrieved subgraph by pruning compromised entities and relationships and adding only minimal missing evidence. Experiments on multi-hop datasets and multiple GraphRAG configurations show that HoG-GRAG recovers a large fraction of the lost performance. The code is available at https://github.com/CyberScienceLab/HoG-GRAG.
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