DAppSCAN: Building Large-Scale Datasets for Smart Contract Weaknesses in DApp Projects
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Abstract (arXiv)
The Smart Contract Weakness Classification Registry (SWC Registry) is a widely recognized list of smart contract weaknesses specific to the Ethereum platform. Despite the SWC Registry not being updated with new entries since 2020, the sustained development of smart contract analysis tools for detecting SWC-listed weaknesses highlights their ongoing significance in the field. However, evaluating these tools has proven challenging due to the absence of a large, unbiased, real-world dataset. To address this problem, we aim to build a large-scale SWC weakness dataset from real-world DApp projects. We recruited 22 participants and spent 44 person-months analyzing 1,199 open-source audit reports from 29 security teams. In total, we identified 9,154 weaknesses and developed two distinct datasets, i.e., DAPPSCAN-SOURCE and DAPPSCAN-BYTECODE. The DAPPSCAN-SOURCE dataset comprises 39,904 Solidity files, featuring 1,618 SWC weaknesses sourced from 682 real-world DApp projects. However, the Solidity files in this dataset may not be directly compilable for further analysis. To facilitate automated analysis, we developed a tool capable of automatically identifying dependency relationships within DApp projects and completing missing public libraries. Using this tool, we created DAPPSCAN-BYTECODE dataset, which consists of 6,665 compiled smart contract with 888 SWC weaknesses. Based on DAPPSCAN-BYTECODE, we conducted an empirical study to evaluate the performance of state-of-the-art smart contract weakness detection tools. The evaluation results revealed sub-par performance for these tools in terms of both effectiveness and success detection rate, indicating that future development should prioritize real-world datasets over simplistic toy contracts.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 May 2023.
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Citing papers per year: 2024: 2; 2025: 5; 2026: 2.
9 papers.
- 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learningcited by 8
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMscited by 8
- 2025PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contractscited by 7
- 2025SoK: Security and Privacy of AI Agents for Blockchaincited by 3
- 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contractscited by 1
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigationcited by 0
- 2025Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detectioncited by 0
- 2026Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contractscited by 0
- 2026ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichmentcited by 0
- 2026ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment30 Mar 2026
- 2026Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contracts17 Feb 2026
- 2025Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection15 Nov 2025
- 2025PoCo: Agentic Proof-of-Concept Exploit Generation for Smart Contracts4 Nov 2025
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation15 Oct 2025
- 2025SoK: Security and Privacy of AI Agents for Blockchain8 Sep 2025
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMs4 Feb 2025
- 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning9 Sep 2024
- 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts25 Mar 2024