mlsanityData to 7 Oct 2026

How Effective are Smart Contract Analysis Tools? Evaluating Smart Contract Static Analysis Tools Using Bug Injection

arXiv 2005.11613 ↗First posted 23 May 2020cs.SEcited by 20

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Abstract (arXiv)

Security attacks targeting smart contracts have been on the rise, which have led to financial loss and erosion of trust. Therefore, it is important to enable developers to discover security vulnerabilities in smart contracts before deployment. A number of static analysis tools have been developed for finding security bugs in smart contracts. However, despite the numerous bug-finding tools, there is no systematic approach to evaluate the proposed tools and gauge their effectiveness. This paper proposes SolidiFI, an automated and systematic approach for evaluating smart contract static analysis tools. SolidiFI is based on injecting bugs (i.e., code defects) into all potential locations in a smart contract to introduce targeted security vulnerabilities. SolidiFI then checks the generated buggy contract using the static analysis tools, and identifies the bugs that the tools are unable to detect (false-negatives) along with identifying the bugs reported as false-positives. SolidiFI is used to evaluate six widely-used static analysis tools, namely, Oyente, Securify, Mythril, SmartCheck, Manticore and Slither, using a set of 50 contracts injected by 9369 distinct bugs. It finds several instances of bugs that are not detected by the evaluated tools despite their claims of being able to detect such bugs, and all the tools report many false positives

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2020.

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Citing papers per year: 2022: 1; 2023: 5; 2024: 4; 2025: 7; 2026: 3.

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  1. 2025LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insightscited by 44
  2. 2023Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decodingcited by 14
  3. 2025Exploring the Security Threats of Knowledge Base Poisoning in Retrieval-Augmented Code Generationcited by 9
  4. 2025Large Language Models for Security Operations Centers: A Comprehensive Surveycited by 9
  5. 2022MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilitiescited by 4
  6. 2023VulnSense: Efficient Vulnerability Detection in Ethereum Smart Contracts by Multimodal Learning with Graph Neural Network and Language Modelcited by 4
  7. 2023An Automated Vulnerability Detection Framework for Smart Contractscited by 2
  8. 2023Usenix'23 Extended Version: Smart Learning to Find Dumb Contractscited by 2
  9. 2024Evaluation of ChatGPT's Smart Contract Auditing Capabilities Based on Chain of Thoughtcited by 2
  10. 2024Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysiscited by 2
  11. 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilitiescited by 2
  12. 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contractscited by 1
8 more
  1. 2025AI-Based Vulnerability Analysis of NFT Smart Contractscited by 1
  2. 2025UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysiscited by 1
  3. 2023Schooling to Exploit Foolish Contractscited by 0
  4. 2025Towards Understanding Deep Learning Model in Image Recognition via Coverage Testcited by 0
  5. 2025LLMs as verification oracles for Soliditycited by 0
  6. 2026ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichmentcited by 0
  7. 2026CHAINTRIX: A multi-pipeline LLM-augmented framework for automated smart-contract security auditingcited by 0
  8. 2026Automated Vulnerability Injection in Smart Contracts Using Large Language Modelscited by 0