Graph Retrieval-Augmented Generation (GraphRAG) has made substantial progress in multi-hop reasoning, but typically assumes that constructed knowledge graphs (KGs) provide sufficient structural support. In practice, cost and computing constraints often leave constructed KGs imperfect, raising the key challenge of achieving robust multi-hop retrieval. To address this challenge, we empirically analyze KG extraction across builders and find similar error patterns despite differences in error proportions, motivating a retrieval strategy targeted at these shared errors. We further summarize these patterns into two issue modes, spurious noise and incomplete information, which induce retrieval drift and retrieval hallucination by misleading evidence selection and weakening evidential support, respectively. Based on this, we propose CS-RAG, a robust GraphRAG framework that mitigates the impact of imperfect KGs during retrieval. CS-RAG first plans each query as an ordered sequence of executable atomic constraints and performs fine-grained anchor- and relation-aware retrieval to constrain evidence acquisition around the intended hop semantics. It then applies a sufficiency check to decide whether retrieved entities should serve as anchors for subsequent retrieval hops and activates textual recovery for unresolved constraints, thereby reducing hallucinated structural continuation. Experiments on three multi-hop QA benchmarks show that CS-RAG is less sensitive to builder choice and remains stable under controlled KG issue injection. Code is available at https://github.com/myz12138/CS-RAG.

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