Empirical Review of Automated Analysis Tools on 47,587 Ethereum Smart Contracts
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Abstract (arXiv)
Over the last few years, there has been substantial research on automated analysis, testing, and debugging of Ethereum smart contracts. However, it is not trivial to compare and reproduce that research. To address this, we present an empirical evaluation of 9 state-of-the-art automated analysis tools using two new datasets: i) a dataset of 69 annotated vulnerable smart contracts that can be used to evaluate the precision of analysis tools; and ii) a dataset with all the smart contracts in the Ethereum Blockchain that have Solidity source code available on Etherscan (a total of 47,518 contracts). The datasets are part of SmartBugs, a new extendable execution framework that we created to facilitate the integration and comparison between multiple analysis tools and the analysis of Ethereum smart contracts. We used SmartBugs to execute the 9 automated analysis tools on the two datasets. In total, we ran 428,337 analyses that took approximately 564 days and 3 hours, being the largest experimental setup to date both in the number of tools and in execution time. We found that only 42% of the vulnerabilities from our annotated dataset are detected by all the tools, with the tool Mythril having the higher accuracy (27%). When considering the largest dataset, we observed that 97% of contracts are tagged as vulnerable, thus suggesting a considerable number of false positives. Indeed, only a small number of vulnerabilities (and of only two categories) were detected simultaneously by four or more tools.
Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Oct 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 5; 2024: 6; 2025: 14; 2026: 8.
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- 2023Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decodingcited by 14
- 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generationcited by 9
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMscited by 8
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contractscited by 7
- 2025Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilitiescited by 5
- 2025A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detectioncited by 5
- 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilitiescited by 4
- 2023VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Modelcited by 4
- 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Qualitycited by 4
- 2025Prompt to Pwn: Automated Exploit Generation for Smart Contractscited by 4
- 2025SoK: Security and Privacy of AI Agents for Blockchaincited by 4
- 2025SmartLLM: Smart Contract Auditing using Custom Generative AIcited by 3
24 more
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
- 2023HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Modelcited by 2
- 2024Machine Learning for Blockchain Data Analysis: Progress and Opportunitiescited by 2
- 2024Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysiscited by 2
- 2025CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchaincited by 2
- 2025Adaptive Plan-Execute Framework for Smart Contract Security Auditingcited by 2
- 2025Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchaincited by 2
- 2023SourceP: Detecting Ponzi Schemes on Ethereum with Source Codecited by 1
- 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contractscited by 1
- 2024FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMscited by 1
- 2025UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysiscited by 1
- 2025Large Language Model based Smart Contract Auditing with LLMBugScannercited by 1
- 2021Vulnerability and Transaction behavior based detection of Malicious Smart Contractscited by 0
- 2024Vulnerability Detection in Smart Contracts: A Comprehensive Surveycited by 0
- 2024Knowledge Migration Framework for Smart Contract Vulnerability Detectioncited 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
- 2026Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Trainingcited by 0
- 2026CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditingcited by 0
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction poolingcited by 0
- 2026Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuningcited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contractscited by 0
- 2026Automated Vulnerability Injection in Smart Contracts Using Large Language Modelscited by 0
- 2026SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contractscited by 0
- 2026SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts13 Oct 2026
- 2026Automated Vulnerability Injection in Smart Contracts Using Large Language Models2 Sep 2026
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts17 Jul 2026
- 2026Repository-Level Solidity Code Generation with Large Language Models: From Prompting to Fine-Tuning18 Jun 2026
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling4 Jun 2026
- 2026CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing10 May 2026
- 2026Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training29 Mar 2026
- 2026Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contracts17 Feb 2026
- 2025A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detection18 Dec 2025
- 2025Large Language Model based Smart Contract Auditing with LLMBugScanner29 Nov 2025
- 2025Multi-Agent Collaborative Fuzzing with Continuous Reflection for Smart Contracts Vulnerability Detection15 Nov 2025
- 2025SoK: Security and Privacy of AI Agents for Blockchain8 Sep 2025
24 more
- 2025Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain1 Sep 2025
- 2025Prompt to Pwn: Automated Exploit Generation for Smart Contracts2 Aug 2025
- 2025UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis2 Aug 2025
- 2025Adaptive Plan-Execute Framework for Smart Contract Security Auditing21 May 2025
- 2025CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain28 Apr 2025
- 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality7 Mar 2025
- 2025SmartLLM: Smart Contract Auditing using Custom Generative AI17 Feb 2025
- 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generation5 Feb 2025
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMs4 Feb 2025
- 2025Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilities13 Jan 2025
- 2024Knowledge Migration Framework for Smart Contract Vulnerability Detection15 Dec 2024
- 2024FTSmartAudit: A Knowledge Distillation-Enhanced Framework for Automated Smart Contract Auditing Using Fine-Tuned LLMs17 Oct 2024
- 2024Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis26 Jul 2024
- 2024Vulnerability Detection in Smart Contracts: A Comprehensive Survey8 Jul 2024
- 2024Machine Learning for Blockchain Data Analysis: Progress and Opportunities28 Apr 2024
- 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts25 Mar 2024
- 2023Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding18 Sep 2023
- 2023VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Model15 Sep 2023
- 2023SourceP: Detecting Ponzi Schemes on Ethereum with Source Code2 Jun 2023
- 2023HyMo: Vulnerability Detection in Smart Contracts using a Novel Multi-Modal Hybrid Model25 Apr 2023
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts21 Apr 2023
- 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities28 Aug 2022
- 2021Vulnerability and Transaction behavior based detection of Malicious Smart Contracts25 Jun 2021
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contracts7 Jan 2021
- 2023Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decodingcited by 14
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMscited by 8
- 2021Eth2Vec: Learning Contract-Wide Code Representations for Vulnerability Detection on Ethereum Smart Contractscited by 7
- 2025Logic Meets Magic: LLMs Cracking Smart Contract Vulnerabilitiescited by 5
- 2025A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detectioncited by 5
- 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilitiescited by 4
- 2023VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Modelcited by 4
- 2025Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Qualitycited by 4
- 2025Prompt to Pwn: Automated Exploit Generation for Smart Contractscited by 4
- 2025SoK: Security and Privacy of AI Agents for Blockchaincited by 4
- 2025SmartLLM: Smart Contract Auditing using Custom Generative AIcited by 3
- 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
12 most cited of 31 All 31 (JSON)
- 2026SolBehave: Evaluating the Behavioural Correctness of LLM-Generated Smart Contracts13 Oct 2026
- 2026Automated Vulnerability Injection in Smart Contracts Using Large Language Models2 Sep 2026
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling4 Jun 2026
- 2026CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditing10 May 2026
- 2026Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training29 Mar 2026
- 2026Benchmarking Zero-Shot Reasoning Approaches for Error Detection in Solidity Smart Contracts17 Feb 2026
- 2025A Systematic Study of Code Obfuscation Against LLM-based Vulnerability Detection18 Dec 2025
- 2025Large Language Model based Smart Contract Auditing with LLMBugScanner29 Nov 2025
- 2025SoK: Security and Privacy of AI Agents for Blockchain8 Sep 2025
- 2025Detecting Rug Pulls in Decentralized Exchanges: Machine Learning Evidence from the TON Blockchain1 Sep 2025
- 2025Prompt to Pwn: Automated Exploit Generation for Smart Contracts2 Aug 2025
- 2025UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis2 Aug 2025
12 newest of 31 All 31 (JSON)