# ScrawlD: A Dataset of Real World Ethereum Smart Contracts Labelled with Vulnerabilities

arXiv: https://arxiv.org/abs/2202.11409 · HTML: https://mlsanity.com/paper/2202.11409/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2022.

## Built on by (most cited first, 4 of 4)

- [HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model](https://mlsanity.com/paper/2304.13103/) (2023)
- [Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities](https://mlsanity.com/paper/2409.10574/) (2024)
- [ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection](https://mlsanity.com/paper/2510.17919/) (2025)
- [On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation](https://mlsanity.com/paper/2510.16024/) (2025)

## 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.