Hop-Decayed Influence: New Vulnerabilities of Structural Auxiliary Indexing in GraphRAG Pipelines with LLM
Organizations: Institute of Cybersecurity and Cryptology, University of Wollongong
Abstract
GraphRAG pipelines construct auxiliary structures during offline indexing--semantic summaries, hierarchical edges, and pre-computed scores--that determine how retrieval is prioritised at query time. Prior attacks target only instance-level components (nodes, edges, triples), overlooking these schema-level structures. We formalise Auxiliary Schema-Level Entity as a novel attack surface and propose the 3S Framework (Semantics, Structure, Scoring) for its systematic exploitation. Our Hop-Decayed Influence (HDI) attack identifies high-impact targets through query-aware influence propagation and corrupts their auxiliary structures post-indexing. Across two benchmarks (HotpotQA, 2WikiMultiHopQA) and two architectures (Microsoft GraphRAG, HippoRAG2), HDI achieves 88-94% attack success rate while modifying as few as 0.016% of auxiliary structures. Each modification affects up to 6.00 queries (Schema Leverage Ratio), demonstrating 1:N amplification unavailable to instance-level attacks. Manipulated structures evade perplexity and paraphrase defenses with over 99% evasion rate, as they remain linguistically coherent system-generated artifacts. These results reveal that auxiliary schema-level entities receive implicit trust without runtime validation, constituting a structural blind spot in current GraphRAG defenses. https://github.com/Jisung-Pacific/HDI-GraphRAG-Attack.
Figures & tables
| MSGraphRAG | HippoRAG2 | |||
|---|---|---|---|---|
| HotpotQA | 2WikiMultiHopQA | HotpotQA | 2WikiMultiHopQA | |
| Best Config | HDI-T | HDI-T | HDI-T | HDI-C |
| Our ASR (%) | 91.69 | 88.17 | 88.24 | 94.44 |
| PoisonedRAG (%) | 80.69 | 80.42 | 81.49 | 85.89 |
| Gragpoison (%) | 85.64 | 88.68 | 86.28 | 87.24 |
| MSGraphRAG | HippoRAG2 | |||
| HotpotQA | 2WikiMultiHopQA | HotpotQA | 2WikiMultiHopQA | |
| Best Config | HDI-T | HDI-T | HDI-T | HDI-C |
| Total Structures | 6,328 | 9,328 | 440,020 | 311,419 |
| # Modified | 300 | 450 | 330 | 49 |
| Mod. Ratio (%) | 4.74 | 4.82 | 0.075 | 0.016 |
| Our SLR | 4.57 | 3.41 | 5.80 | 6.00 |
| MSGraphRAG | HippoRAG2 | |||
| HotpotQA | 2WikiMultiHopQA | HotpotQA | 2WikiMultiHopQA | |
| Best Type | HDI-T | HDI-T | HDI-T | HDI-C |
| Perplexity Evasion (%) | 99.85 | 99.50 | 99.87 | 99.79 |
| Paraphrase Evasion (%) | 99.13 | 99.14 | 99.72 | 99.27 |
| Detection Rate (%) | 0.07 | 0.09 | 0.05 | 0.08 |
RAG Poisoning Attacks
Retrieval-augmented generation (RAG) poisoning attacks manipulate retrieved knowledge to steer, degrade, or disrupt model outputs.
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