Dual Knowledge Graph Retrieval-Augmented Smart Contract Vulnerability Detection

Abstract

Smart contract vulnerabilities remain a major source of financial loss in blockchain ecosystems. A large body of work has investigated automated detection using static analysis, symbolic execution, and more recently large language models (LLMs). While these approaches are effective for many syntactic and arithmetic issues, logic-centric vulnerabilities often require connecting dispersed evidence across code structure, security knowledge, and protocol assumptions. In this setting, promptonly LLM-based auditing can be brittle. Retrieval augmentation helps by supplying external knowledge, yet the retrieved evidence is not always well aligned with contract-specific code cues, which can still result in over-triggering and limited auditability. This paper presents GraphShield, a retrieval-augmented framework for smart contract vulnerability detection that grounds model judgments on complementary evidence. The key idea is to organize heterogeneous security knowledge and contract-structure cues into dual knowledge graphs, retrieve compact subgraph evidence for a target contract, and guide the model to produce an evidence-backed detection report that is suitable for auditor review. We evaluate GraphShield on a curated benchmark of audited contracts under a unified bug taxonomy. The results show that GraphShield improves detection effectiveness over representative prompting-based baselines, achieving a better balance between coverage and precision, and providing more verifiable rationales to support downstream manual inspection.