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.