The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to $66 billion. Vulnerabilities arise when contracts interact with external protocols without verifying the results of the calls, leading to exploit entry points such as flash loan attacks and reentrancy attacks. In this paper, we propose UEChecker, a deep learning-based tool that utilizes a call graph and a Graph Convolutional Network to detect unchecked external call vulnerabilities. We design the following components: An edge prediction module that reconstructs the feature representation of nodes and edges in the call graph; A node aggregation module that captures structural information from both the node itself and its neighbors, thereby enhancing feature representation between nodes and improving the model's understanding of the global graph structure; A Conformer Block module that integrates multi-head attention, convolutional modules, and feedforward neural networks to more effectively capture dependencies of different scales within the call graph, extending beyond immediate neighbors and enhancing the performance of vulnerability detection. Finally, we combine these modules with Graph Convolutional Network to detect unchecked external call vulnerabilities. By auditing the smart contracts of 608 DApps, our results show that our tool achieves an accuracy of 87.59% in detecting unchecked external call vulnerabilities. Furthermore, we compare our tool with GAT, LSTM, and GCN baselines, and in the comparison experiments, UEChecker consistently outperforms these models in terms of accuracy.

翻译：DApp合约层攻击事件日益增多，已造成高达660亿美元的经济损失。当合约与外部协议交互时未验证调用结果，便会形成漏洞，从而为闪电贷攻击、重入攻击等利用手段提供入口点。本文提出UEChecker，一种基于深度学习的工具，利用调用图和图卷积网络检测未检查外部调用漏洞。我们设计了以下组件：边预测模块，用于重构调用图中节点与边的特征表示；节点聚合模块，从节点自身及其邻居节点中捕获结构信息，从而增强节点间的特征表示，并提升模型对全局图结构的理解；Conformer Block模块，集成多头注意力机制、卷积模块和前馈神经网络，以更有效地捕获调用图中不同尺度的依赖关系，其感知范围超越直接邻居节点，从而提升漏洞检测性能。最后，我们将这些模块与图卷积网络相结合，以检测未检查外部调用漏洞。通过对608个DApp的智能合约进行审计，实验结果表明，我们的工具在检测未检查外部调用漏洞方面达到了87.59%的准确率。此外，我们将本工具与GAT、LSTM和GCN基线模型进行比较，在对比实验中，UEChecker在准确率方面持续优于这些模型。