VulCrosser: Smart Contract Vulnerability Detection Based on Complex Network Analysis in Interactive Scenarios

Abstract

Smart contract vulnerabilities have led to massive losses in digital assets. While researchers have proposed numerous detection methods utilizing static analysis, fuzzing, and deep learning, most are limited to identifying vulnerabilities within individual contracts. Consequently, these approaches fail to effectively analyze cross-contract interactions via external function calls, resulting in false negatives and positives. To address these limitations, we present VulCrosser, a deep learning method tailored for vulnerability detection in contract interaction scenarios. VulCrosser enables comprehensive risk assessment by analyzing function call chain traces. Specifically, it constructs a Cross-Contract Dependency Graph (CCDG) to effectively model inter-contract dependencies, network dynamics, and interaction semantics. It then employs a heterogeneous graph neural network with a two-level attention mechanism to extract and integrate complex features from the graph, ultimately achieving accurate risk assessment. We evaluated VulCrosser on three common vulnerabilities: reentrancy, timestamp dependency, and transaction state dependency. Experimental results show that VulCrosser outperforms all baseline methods, improving detection accuracy by 5.04%, 4.39%, and 5.09%, and $F 1$ scores by $4.93 \%, 4.60 \%$, and 4.99%, respectively.