Triple-Threat Analysis: Measuring Mythril, Slither and Oyente Against Real-World Smart Contract Vulnerabilities

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 13 | 01 | Page :
    By

    Ranjit Haldar,

  • Himadri Sekhar Mondal,

  • Asim Kumar Panda,

  • Amartya Roy,

  • Shuvojit Das,

  1. Assistant Professor, Department of Information technology, B.P. Poddar Institute of Management and Technology, West Bengal, India
  2. Assistant Professor, Department of Information technology, B.P. Poddar Institute of Management and Technology, West Bengal, India
  3. Assistant Professor, Department of Information technology, B.P. Poddar Institute of Management and Technology, West Bengal, India
  4. Assistant Professor, Department of Information technology, B.P. Poddar Institute of Management and Technology, West Bengal, India
  5. Student, Department of AI and ML, BITS Pilani, Rajasthan, India

Abstract

Smart contracts have become fundamental building blocks of blockchain ecosystems, yet their immutable nature makes security vulnerabilities particularly devastating. This pa- per presents a comprehensive evaluation of three prominent static analysis tools—Mythril, Slither, and Oyente—for detecting vulnerabilities in Ethereum smart contracts. Through systematic experimentation with real-world contract categories (voting sys- tems, land registries, and crowdfunding platforms), we quantify the effectiveness of each tool across eight critical vulnerability types, including reentrancy, integer overflows, and transaction- ordering dependence. Our results reveal significant variations in tool capabilities: Slither demonstrates superior performance for syntactic vulner- abilities (95% detection rate for integer overflows) with faster analysis times (3× quicker than Mythril), while Mythril excels at detecting complex logical flaws (92% accuracy for reentrancy). Oyente shows consistently lower performance, particularly for gas-related vulnerabilities (45% detection rate). We identify key trade-offs between analysis depth and computational resources, with Mythril requiring 2.5× more memory than Slither for thorough symbolic execution. The study provides practical insights for developers, recom- mending Slither for development-phase scanning and Mythril for final audits. We also expose critical gaps in current tools, in- cluding poor cross-contract vulnerability detection (42% missed rate) and limited gas optimization analysis. These findings inform our proposed roadmap for future research directions, including hybrid analysis frameworks and machine learning-enhanced detection methods. Our work contributes to smarter tool selection strategies and highlights opportunities for advancing smart contract security analysis.

Keywords: Blockchain, Smart Contracts, Security Vulner- abilities, Static Analysis, Mythril, Slither, Oyente, Ethereum, Reentrancy Attacks, Integer Overflow, Formal Verification

How to cite this article:
Ranjit Haldar, Himadri Sekhar Mondal, Asim Kumar Panda, Amartya Roy, Shuvojit Das. Triple-Threat Analysis: Measuring Mythril, Slither and Oyente Against Real-World Smart Contract Vulnerabilities. Journal of Artificial Intelligence Research & Advances. 2026; 13(01):-.
How to cite this URL:
Ranjit Haldar, Himadri Sekhar Mondal, Asim Kumar Panda, Amartya Roy, Shuvojit Das. Triple-Threat Analysis: Measuring Mythril, Slither and Oyente Against Real-World Smart Contract Vulnerabilities. Journal of Artificial Intelligence Research & Advances. 2026; 13(01):-. Available from: https://journals.stmjournals.com/joaira/article=2026/view=237200


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Ahead of Print Subscription Original Research
Volume 13
01
Received 14/10/2025
Accepted 30/12/2025
Published 19/02/2026
Publication Time 128 Days
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