关键词: 智能合约, 漏洞检测, 动态特征融合, 轻量化部署

Abstract: Most vulnerability detection methods for smart contracts rely on a single modality or large models. The former suffers from insufficient extraction of key features, resulting in low detection accuracy, while the latter has a large parameter scale, which is not conducive to deployment in resourceconstrained environments. To address these issues, this paper proposes dualmodal conditional feature gating, a lightweight dynamic feature fusion method for smart contract vulnerability detection, which adaptively generates fusion weights via a lightweight gating network and constructs a lightweight deploymentoriented detection framework. Experiments on 17,554 reentrancy samples from SmartBugs Wild achieve an accuracy of 76.87%, an F1score of 0.788, and an AUC of 0.870, outperforming static fusion, concatenation fusion, and singlemodal baselines. Compared with reproduced MVul-L and CMRL, the proposed method maintains comparable performance while reducing the parameter count by about 45% and lowering inference latency.

Key words: smart contract, vulnerability detection, dynamic feature fusion, lightweight deployment