ReDetect: Reentrancy Vulnerability Detection in Smart Contracts with High Accuracy

Abstract

Smart contracts are a landmark achievement of blockchain technology 2.0 and are widely adopted in various applications. However, smart contracts are not always secure and there are various vulnerabilities. The reentrancy vulnerability is one of most serious vulnerabilities, and it has caused huge economic losses. Although many methods have been proposed to detect reentrancy vulnerabilities, they all have high false positives. To deal with this problem, we propose a symbolic execution-based detection tool for reentrancy vulnerabilities of smart contracts at the EVM bytecode level. By analyzing a large number of real-world smart contracts, we conclude main patterns of false positives and design five effective path filters to eliminate false positives. We evaluate its performance on real-world datasets in comparison with the state-of-the-art works, and the results show that our tool is more effective in the detection of reentrancy vulnerabilities.