SmartAuditFlow: A Dynamic Plan-Execute Framework for Advanced Smart Contract Security Analysis
Zhiyuan Wei, Jing Sun, Zhé Hóu, Zijian Zhang, Zixiao Zhao, Chunmiao Li, Mingchao Wan, Jin Dong · ACM Transactions on Software Engineering and Methodology · 2025
Large Language Models (LLMs) have demonstrated significant potential in smart contract auditing. However, they are still susceptible to hallucinations and limited context-aware reasoning. In this article, we propose SmartAuditFlow, a dynamic Plan-Execute framework that customizes audit strategies based on the unique characteristics of each smart contract. Unlike static, rule-based workflows, our approach iteratively generates and refines audit plans in response to intermediate outputs and newly detected vulnerabilities. To improve reliability, the framework incorporates structured reasoning, prompt optimization, and external tools such as static analyzers and Retrieval-Augmented Generation (RAG). This multi-layered design reduces false positives and enhances the accuracy of vulnerability detection. Experimental results show that SmartAuditFlow achieves 100% accuracy on common vulnerability benchmarks and successfully identifies 13 additional CVEs missed by existing methods. These findings underscore the framework’s adaptability, precision, and practical utility as a robust solution for automated smart contract security auditing. The source code is available at: https://github.com/JimmyLin-afk/SmartAuditFlow .