ScrawlD: A Dataset of Real World Ethereum Smart Contracts Labelled with Vulnerabilities
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Abstract (arXiv)
Smart contracts on Ethereum handle millions of U.S. Dollars and other financial assets. In the past, attackers have exploited smart contracts to steal these assets. The Ethereum community has developed plenty of tools to detect vulnerable smart contracts. However, there is no standardized data set to evaluate these existing tools, or any new tools developed. There is a need for an unbiased standard benchmark of real-world Ethereum smart contracts. We have created ScrawlD: an annotated data set of real-world smart contracts taken from the Ethereum network. The data set is labelled using 5 tools that detect various vulnerabilities in smart contracts, using majority voting.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2022.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2.
4 papers.
- 2023HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Modelcited by 2
- 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilitiescited by 2
- 2025ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detectioncited by 1
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigationcited by 0
- 2025ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection20 Oct 2025
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation15 Oct 2025
- 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities15 Sep 2024
- 2023HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model25 Apr 2023