Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems

• URL: http://arxiv.org/abs/2601.14662v1
• Date: Wed, 21 Jan 2026 05:20:54 GMT
• Title: Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems
• Authors: Shuhua Yang, Jiahao Zhang, Yilong Wang, Dongwon Lee, Suhang Wang,
• Abstract summary: Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning.<n>We study a budget-constrained black-box setting where an adversary adaptively queries the system to steal its latent entity-relation graph.<n>We propose AGEA, a framework that leverages a novelty-guided exploration-exploitation strategy, external graph memory modules, and a two-stage graph extraction pipeline.
• Score: 29.89127594311822
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph-based retrieval-augmented generation (GraphRAG) systems construct knowledge graphs over document collections to support multi-hop reasoning. While prior work shows that GraphRAG responses may leak retrieved subgraphs, the feasibility of query-efficient reconstruction of the hidden graph structure remains unexplored under realistic query budgets. We study a budget-constrained black-box setting where an adversary adaptively queries the system to steal its latent entity-relation graph. We propose AGEA (Agentic Graph Extraction Attack), a framework that leverages a novelty-guided exploration-exploitation strategy, external graph memory modules, and a two-stage graph extraction pipeline combining lightweight discovery with LLM-based filtering. We evaluate AGEA on medical, agriculture, and literary datasets across Microsoft-GraphRAG and LightRAG systems. Under identical query budgets, AGEA significantly outperforms prior attack baselines, recovering up to 90% of entities and relationships while maintaining high precision. These results demonstrate that modern GraphRAG systems are highly vulnerable to structured, agentic extraction attacks, even under strict query limits.

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