AI-Empowered Smart Contract Vulnerability Detection for Decentralized Blockchain Systems

Abstract

Blockchain technology is evolving toward next-generation blockchain systems, where AI-empowered optimization plays a key role in enhancing scalability, efficiency, and security. However, in the context of smart contract vulnerability detection, however, most vulnerabilities originate from a few critical code fragments, resulting in a severe signal-to-noise imbalance. This imbalance introduces representation noise and redundant information in complex contract code, obscuring the true vulnerability semantics and hindering the effectiveness of AI-based models such as graph neural networks (GNNs). To address this challenge, we propose CoTA, a feature augmentation framework that leverages two easily obtainable signals: code-cohort feature and task-shared feature. Code-cohort features capture the observation that contracts with similar structural/behavioral patterns often implement similar business functions, and thus share vulnerability priors (e.g., higher reentrancy risk in fund-transfer-heavy cohorts). Task-shared feature, on the other hand, learned via a multi-task setup that jointly trains coarse contract-level multi-label classification and fine-grained function-level labeling to distill shared representations. Extensive experiments on real-world dataset show that CoTA achieves consistent improvements in detection accuracy, offering a practical and effective solution for smart contract security analysis.