LLM Assisted Dual-View Awareness Framework for Smart Contract Vulnerability Detection

Abstract

Smart contract vulnerability detection is an important task in securing the blockchain. However, existing detection methods primarily extract single view features, such as semantic or structural features, which ignores the synergistic supplementation of them to smart contract, remaining room for improvement in feature representation. To this end, this paper proposes the LLM-assisted dual-view awareness framework for smart contract vulnerability detection, which incorporates significantly different semantic features and structural features. To address the limitation of large language model (LLM) in domain-specific expertise, we design semantic awareness module based on Retrieval-Augmented Generation (RAG), construct vulnerability knowledge base, and perform semantic reasoning on smart contracts. To capture crucial structural information, we propose structural awareness module based on Graph Neural Network (GNN), construct contract graphs, and perform structural analysis on smart contracts. We evaluated four types of vulnerabilities, and the experimental results show that our approach significantly outperforms state-of-the-art approaches, achieving 4.80% improvement in accuracy for timestamp dependence detection.