Published February 7, 2026 | Version v3

DIVE: A Multi-Label Smart Contract Vulnerability Dataset

  • 1. ROR icon King Fahd University of Petroleum and Minerals

Description

📄 Dataset Description

The DIVE dataset was constructed from raw data collected via the Etherscan API, using three main endpoints to capture:

  • 🧩 Contract-level metadata

  • 🧾 Account-level information

  • ⚙️ Opcodes

DIVE integrates a wide variety of smart contract features, organized into two separate datasets:

  • DIVE_PRE_Data (pre-deployment features)

  • DIVE_POST_Data (post-deployment features)

Each dataset is available in two formats: Raw and Preprocessed. Following multiple preprocessing steps, the final processed versions provide:

  • 22,330 smart contracts (samples)

  • 221 features in DIVE_PRE_Data

  • 176 features in DIVE_POST_Data

 

🏷️ Multi-Label Vulnerability Annotation

Since a single contract can exhibit multiple vulnerabilities, DIVE is structured as a multi-label dataset. Vulnerability labels were generated through the MultiTagging framework, which analyzes each contract’s source code using a suite of six established tools: MAIAN, Mythril, Semgrep, Slither, Solhint, and VeriSmart.  All tools were executed with consistent versions and configurations, aligned with the MultiTagging project specifications.

 

🛡️ Vulnerability Labels (DASP Top 10 Categories)

The dataset includes labels mapped to the first 8 categories of the DASP Top 10 vulnerability taxonomy:

  1. Reentrancy

  2. Access Control

  3. Arithmetic Issues (Integer Overflow/Underflow)

  4. Unchecked Call Return Values

  5. Denial of Service (DoS)

  6. Bad Randomness

  7. Front Running

  8. Time Manipulation (Timestamp Dependence)

🎯 Applications

The DIVE dataset is primarily intended for smart contract vulnerability detection through security analysis and machine learning.
Beyond this, its rich feature set and multi-label structure enable research across multiple domains, including:

  • Vulnerability Detection

  • Representation Learning

  • Transfer & Domain Adaptation

  • Feature Interpretability

  • Anomaly Detection

📦 Package Contents

  • DIVE_Raw_Data.zip – PRE- and POST-deployment unprocessed attributes.

  • DIVE_Processed_Data.zip – PRE- and POST-deployment data ready for ML tasks.

  • DIVE_Labels.zip

    • DIVE_Labels.csv: Final multi-label vulnerability annotations.

    • Tool_Results.csv: Per-tool vulnerability flags mapped to DASP categories.

  • Feature list.xlsx – Comprehensive feature catalog (name, type, description, category).

  • EDA_and_Profiling_Reports.zip – Exploratory data analysis, profiling reports, and feature distribution plots for both PRE and POST datasets.

Files

DIVE_Labels.zip

Files (1.2 GB)

Name Size
md5:ace7aaebd19e040ca78f578fb5ecc6cf
253.9 kB Preview Download
md5:03af6da3e7427ca2f780f6e9b4d10c0e
16.3 MB Preview Download
md5:23b9557e9de3fb6ce3e636a097c64bfd
546.7 MB Preview Download
md5:de4e30d8f219599a8030c558afa9dc2b
594.0 MB Preview Download
md5:7938743c46ef4301d3f6cb38cfd21b37
45.4 kB Download

Additional details

Related works

Is published in
Data paper: 10.1038/s41597-026-07025-5 (DOI)

Software

Repository URL
https://github.com/DIVE4Data/DIVE.git
Programming language
Python
Development Status
Active