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  3. Dive: A Multi-label Smart Contract Vulnerability Dataset.
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  3. Dive: A Multi-label Smart Contract Vulnerability Dataset.

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DIVE: A Multi-Label Smart Contract Vulnerability Dataset.

Shikah J Alsunaidi1, Hamoud Aljamaan2,3, Mohammad Hammoudeh1,4

  • 1Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.

Scientific Data
|March 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces DIVE, a new dataset for detecting smart contract (SC) vulnerabilities. DIVE offers a large, diverse collection of real-world SCs with comprehensive features to improve machine learning model reliability.

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Area of Science:

  • Computer Science
  • Software Engineering
  • Cybersecurity

Background:

  • Smart Contract (SC) vulnerabilities pose significant risks, leading to financial losses and functional failures.
  • Existing datasets for SC vulnerability detection are often limited by size, imbalance, inconsistent labeling, and non-standardized features, hindering reliable machine learning (ML) model development.
  • Current feature representations often neglect different contract lifecycle stages, impacting model generalization and benchmark accuracy.

Purpose of the Study:

  • To introduce DIVE, a novel multi-label dataset designed to overcome the limitations of existing SC vulnerability datasets.
  • To provide a comprehensive resource for training and evaluating ML models for SC vulnerability detection.
  • To enable more reliable and generalizable vulnerability detection across different stages of the smart contract lifecycle.

Main Methods:

  • DIVE comprises 22,330 real-world SCs deployed between 2016 and 2024, covering major Solidity compiler versions.
  • SCs are annotated for eight vulnerability types based on the Decentralized Application Security Project (DASP) Top 10 taxonomy.
  • A standardized multi-tool labeling pipeline utilizing Power-based voting and post-hoc filtering was employed, correcting significant false positives in Denial of Service (DoS) and Time Manipulation vulnerabilities.

Main Results:

  • The DIVE dataset provides 221 pre-deployment and 176 post-deployment features, offering lifecycle-specific feature sets.
  • The labeling pipeline successfully corrected 14.3% of false positives in DoS and 24.9% in Time Manipulation vulnerabilities.
  • The dataset supports reproducible benchmarking through an open-source framework, facilitating periodic reconstruction aligned with evolving vulnerability patterns.

Conclusions:

  • DIVE addresses critical structural and feature-level limitations in existing SC vulnerability datasets.
  • The dataset's comprehensive nature and lifecycle-specific features enhance the reliability and generalizability of ML-based SC vulnerability detection.
  • DIVE promotes reproducible research and adaptable vulnerability detection methodologies in the evolving landscape of smart contract security.