Abstract
Smart Contract (SC) vulnerabilities are programming errors or design flaws that can lead to financial loss or functional failure, making accurate detection essential. Although Machine Learning (ML) is widely applied to SC vulnerability detection, existing datasets are often small, imbalanced, inconsistently labeled, or nonstandardized, and frequently rely on limited feature representations that do not account for different contract lifecycle stages, restricting generalization and degrading benchmark reliability. This study introduces DIVE, a multi-label dataset that addresses these structural and feature-level limitations. DIVE includes 22,330 real-world SCs deployed between 2016 and 2024, and spanning major Solidity compiler versions, annotated for eight vulnerability types aligned with the Decentralized Application Security Project (DASP) Top 10 taxonomy. It provides 221 pre-deployment and 176 post-deployment features and employs a standardized multi-tool labeling pipeline based on Power-based voting and post-hoc filtering, which corrected 14.3% false positives in DoS and 24.9% in Time Manipulation. Unlike prior datasets, DIVE offers two lifecycle-specific feature sets and an open-source framework enabling reproducible benchmarking and periodic reconstruction aligned with evolving vulnerability patterns.
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Data availability
The dataset generated using the DIVE framework is publicly available on the Zenodo25 under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The repository includes the dataset, the associated metadata registry, and data profiling information to support understanding and reuse.
Code availability
The open-source DIVE framework is available on Zenodo36 under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) license. The repository includes documentation and usage instructions.
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Acknowledgements
The authors would like to acknowledge the support of the King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, in the development of this work.
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Conceptualization: S.J.A., H.A., M.H., Data curation: S.J.A., Formal analysis: S.J.A., Investigation: S.J.A., Methodology: S.J.A., H.A., Resources: S.J.A., Software: S.J.A., Supervision: H.A., M.H., Validation: S.J.A., H.A., M.H., Visualization: S.J.A., Writing—original draft: S.J.A., Writing–review & editing: S.J.A., H.A., M.H.
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Alsunaidi, S.J., Aljamaan, H. & Hammoudeh, M. DIVE: A Multi-Label Smart Contract Vulnerability Dataset. Sci Data (2026). https://doi.org/10.1038/s41597-026-07025-5
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DOI: https://doi.org/10.1038/s41597-026-07025-5
