AtomGraph: Tackling Atomicity Violation in Smart Contracts using Multimodal GCNs
Xiaoqi Li, Zongwei Li, Wenkai Li, Zeng Zhang, Lei Xie
TL;DR
The paper tackles atomicity violations in smart contracts, a security risk not well-handled by existing tools. It presents AtomGraph, a multimodal GCN framework that fuses CFG structural features with opcode-sequence semantics via an adaptive weighted fusion mechanism for graph-level detection. Key contributions include the first systematic study of atomicity-violation detection in smart contracts, the adaptive fusion method, and state-of-the-art performance on a real-world dataset (96.88% accuracy, 96.97% F1) with efficient per-contract processing. The work demonstrates a practical, scalable approach to automated smart-contract security analysis and sets a path for robust vulnerability detection.
Abstract
Smart contracts are a core component of blockchain technology and are widely deployed across various scenarios. However, atomicity violations have become a 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 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 contract's 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 violation 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 concatenation fusion model, AtomGraph improves the F1 score by 6.4%, proving its potential in smart contract security detection.