关键词: 深度学习, 漏洞检测, 抽象语法树, 序列结构, 大模型

Abstract: Aiming at the insufficient integration of local features and global features in existing smart contract vulnerability detection methods, as well as inadequate modeling of code contextual dependencies, this paper proposes a sequence feature-enhanced vulnerability detection model for smart contracts-SAFE-SCVDM. The model innovatively integrates multi-dimensional code representations through a three-stage feature extraction architecture to improve detection accuracy. First, we design a structured traversal based transformation strategy for abstract syntax trees , converting AST into sequential structures that preserve lexical and hierarchical features. Second, a node extraction script is developed to mine token-level information from smart contract code, retaining semantic characteristics. Subsequently, global contextual dependencies are captured by leveraging a large-scale code model to generate code comments from source code, thereby preserving cross-function semantic relationships. Finally, an enhanced LoRA detection model is proposed, utilizing a self-attention mechanism to jointly model textual sequences (code tokens), structural sequences (SBT-AST), and global annotations (code comments). Experimental results demonstrate that the proposed method achieves an accuracy of 88.41% in smart contract vulnerability detection, outperforming state-of-the-art baseline models by 9.8%.

Key words: deep learning, vulnerability detection, abstract syntax trees, sequence structures;large models