Abstract: The smart contract is a fundamental technology of blockchain, as it operates without the need for third-party authorities and can directly provide trusted customized services for users. It represents an important advancement in blockchain technology. As the application range of smart contracts continues to expand, ensuring their safe and reliable operation has become a pressing issue in the field of blockchain security. A research framework for smart contract vulnerability detection and repair is proposed, analyzing and summarizing the current research progress in four key aspects: vulnerability datasets, machine learning methods, vulnerability repair techniques, and patch deployment strategies. Firstly, this paper investigates machine learning-based smart contract vulnerability detection methods, comparing and summarizing 8 types of smart contract vulnerabilities, the current state of 15 open-source datasets, and the advantages and disadvantages of existing models, including traditional machine learning methods, deep learning approaches, and large models. Furthermore, a strategy for constructing high-quality smart contract vulnerability datasets is proposed, combining 5 types of vulnerability detection tools and confidence learning. The 5 types of vulnerability detection tools are symbolic execution, fuzz testing, taint analysis, formal verification, and integrated frameworks. Secondly, 3 categories of smart contract vulnerability repair solutions are systematically introduced: automated repair techniques, machine learning-based repair methods, and Ethereum enhancement technologies. A comprehensive comparison of different solutions is conducted, highlighting their respective advantages and limitations, along with an overview of relevant technologies that can be applied to smart contract vulnerability repair in the future. Finally, this paper analyzes existing security challenges in smart contracts and provides insights into future research directions.

Key words: blockchain, smart contract security, vulnerability detection, vulnerability repair, machine learning

摘要： 智能合约是区块链的关键技术之一，它不依赖第三方权威机构，能够直接为用户双方提供可信的定制化服务，是区块链2.0的重要标志。随着智能合约应用范围的不断扩大，保障其安全可靠运行成为区块链安全领域的迫切问题。提出智能合约漏洞检测与修复研究框架，分别从漏洞数据集、机器学习方法、漏洞修复技术和补丁部署策略这4个方面分析总结现有智能合约漏洞检测与修复研究进展。对基于机器学习的智能合约漏洞检测方法进行研究，对比总结了8种智能合约漏洞类型、15个开源数据集现状以及传统机器学习方法、深度学习方法和大模型方法等现有模型方法优缺点，并提出使用符号执行、模糊测试、污点分析、形式化验证和集成框架5类漏洞检测工具与置信学习相结合的智能合约高质量数据集制作思路；分类介绍了自动化修复技术、机器学习修复技术和以太坊增强技术3类智能合约漏洞修复方案，全面比较了不同方案的优缺点，并总结了未来可以用于智能合约漏洞修复领域的相关技术；分析了智能合约安全现存问题并展望了未来研究方向。

关键词: 区块链, 智能合约安全, 漏洞检测, 漏洞修复, 机器学习