DIVER: Knowledge Theft from GraphRAG System via Structural Diversity-Aware Querying

Graph Retrieval-Augmented Generation (GraphRAG) systems organize knowledge as interconnected graphs. While this connected structure facilitates retrieval for reliable reasoning, it also puts the system’s internal knowledge at risk of theft. By continually issuing queries, an attacker can accumulate disclosed entities and relations and progressively recover the underlying knowledge graph. Although existing methods employ various strategies to improve knowledge extraction, identifying promising entities and effectively querying them to recover additional knowledge remains challenging. To this end, we introduce DIVER an efficient GraphRAG extraction framework based on Structural Diversity-Aware Querying. DIVER identifies entities with high remaining extraction potential by combining two complementary signals: (i) local structural diversity, which estimates the potential to reveal structurally distinct knowledge, and (ii) neighborhood query exposure, which accounts for how thoroughly an entity's local region has already been explored. Using these signals, DIVER prioritizes promising entities for targeted relational queries and shifts toward new topics when local extraction opportunities diminish, balancing local exploitation with global exploration. Across Medical, Novel, and Agriculture, DIVER outperforms all evaluated baselines in entity and relation recall and F1 score. These findings highlight the importance of fully exploiting remaining extraction potential for efficient knowledge theft from GraphRAG systems. Code is available at https://anonymous.4open.science/r/DIVER-93FE.