Smart Contract Vulnerability Detection Using CodeBERT for Token-Level Semantics Understanding

Abstract

Smart contracts are self-executing digital agreements deployed on blockchain platforms, where ensuring security is crucial due to their immutable nature. Understanding token-level semantics plays a key role in identifying potential vulnerabilities in these contracts. However, existing methods often rely on rule-based or syntax-level analysis, which struggle to capture the deeper semantic patterns that lead to complex vulnerabilities. To address these limitations, this study proposes a framework called Fine-Tuning CodeBERT (FTC-BERT), which integrates CodeBERT's pretrained transformer capabilities with task-specific fine-tuning to automatically detect and highlight vulnerabilities in smart contract Integrated Development Environments (IDEs). This method analyzes token-level semantics, enabling precise detection and contextual understanding of vulnerabilities. Developers can use this framework directly within IDEs for real-time vulnerability alerts and suggestions. Experimental results demonstrate that FTC-BERT significantly improves detection accuracy and recall over traditional methods, offering an efficient, automated, and semantic-aware solution for smart contract vulnerability detection.