AtomGraph: Tackling Atomicity Violation in Smart Contracts using Multimodal GCNs

Abstract

Smart contracts are a core component of blockchain technology and are widely deployed across various decentralized scenarios. However, atomicity violations have become a critical potential security risk. Existing analysis tools often lack the precision required to detect these issues effectively. To address this challenge, we introduce AtomGraph, an automated framework designed for detecting atomicity violations. This framework leverages Graph Convolutional Networks (GCN) to accurately identify atomicity violations through multimodal feature learning and fusion. Specifically, driven by a collaborative learning mechanism, the model simultaneously learns from two heterogeneous modalities: extracting structural topological features from the bytecode-based Control Flow Graph (CFG) and uncovering deep semantics from its opcode sequence. We designed an adaptive weighted fusion mechanism to dynamically adjust the weights of features from each modality to achieve optimal feature fusion. Finally, GCN detects graph-level atomicity violations on the contract. Comprehensive experimental evaluations demonstrate that AtomGraph achieves 96.88% accuracy and 96.97% F1 score, outperforming existing tools. Furthermore, compared to the baseline concatenation fusion model, AtomGraph improves the F1 score by 6.4%, proving its potential in smart contract security detection.