Building a Labeled Smart Contract Dataset for Evaluating Vulnerability Detection Tools’ Effectiveness - Achjian, Ryan Weege; Simplicio Junior, Marcos Antonio (Companion Proceedings of the Brazilian Symposium on Cybersecurity (SBSeg); 2025: Companion Proceedings of the 25th Brazilian Symposium on Cybersecurity, 2025)
In recent years, surveys on vulnerability detection tools for Solidity-based smart contracts have shown that many of them display poor capabilities. One of the causes for such deficiencies is the absence of quality benchmarking datasets, where bugs typically found in smart contracts are present in quantity and accurately labeled. VulLab’s main aim is to help tackle this issue as a framework that incorporates both, state-of-the-art vulnerability insertion and vulnerability detection tools. Such capabilities empower users to seamlessly generate benchmark capable datasets from collected contracts and employ them to validate novel analysis tool and obtain an accurate comparison with current state-of-the-art solutions. The framework was able to, from 50 smart contracts collected from the Ethereum mainnet, generate an annotated dataset more than 300 entries which included 20 unique vulnerabilities, and use them to compare 14 analysis tools in approximately 24 hours. VulLab is open-source and is available at https://github.com/lsRyan/vullab .