Smart Contract Vulnerability Detection Based on Symbolic Execution and Graph Neural Networks

Since the advent of smart contracts, security vulnerabilities have remained a persistent challenge, compromsing both the reliability of contract execution and the overall stability of the virtual currency market. Consequently, the academic community has devoted increasing attention to these security risks. However, conventional approaches to vulnerability detection frequently exhibit limited accuracy. To address this limitation, the present study introduces a novel vulnerability detection framework called GNNSE that integrates symbolic execution with graph neural networks (GNNs). The proposed method first constructs semantic graphs to comprehensively capture the control flow and data flow dependencies within smart contracts. These graphs are subsequently processed using GNNs to efficiently identify contracts with a high likelihood of vulnerabilities. For these high-risk contracts, symbolic execution is employed to perform fine-grained, path-level analysis, thereby improving overall detection precision. Experimental results on a dataset comprising 10,079 contracts demonstrate that the proposed method achieves detection precisions of 93.58% for reentrancy vulnerabilities and 92.73% for timestamp-dependent vulnerabilities.