GraphSteal: Structural Knowledge Stealing from Graph RAG via Traversal Reconstruction
Retrieval-Augmented Generation (RAG) enhances LLMs by grounding generation in query-relevant external evidence. Beyond unstructured text corpora, Graph RAG integrates knowledge graphs into the retrieval pipeline, enabling LLMs to access entities, relations, and multi-hop dependencies encoded in structured knowledge. However, the same structured knowledge that empowers Graph RAG also creates a new privacy attack surface. We demonstrate that Graph RAG systems can be turned into structural oracles: through adaptive black-box interactions, an adversary can elicit sufficient relational evidence to reconstruct substantial portions of the hidden knowledge graph. We propose a structure-oriented r...
| Authors | Jinze Gu, Qinghua Mao, Xi Lin et al. |
|---|---|
| Primary category | cs.CR |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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