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I’m building an automated model that scans Solidity code on the Ethereum network and flags three critical weaknesses—reentrancy, arithmetic overflow/underflow, and access-control flaws. What I need from you is a working detection engine: it can be rule-based, machine-learning, formal-verification driven, or a hybrid, as long as it reliably pinpoints those issues in typical ERC-20/721 style contracts and produces a clear, human-readable report. I’ll provide sample contracts (both vulnerable and safe) so you can tune precision and recall; feel free to augment with publicly available datasets such as SWC Registry examples. Because this is Ethereum-only work, please focus on Solidity syntax quirks, common library usage (OpenZeppelin), and the nuances of the EVM. If you lean on tools like Slither, Mythril, Foundry, or custom AST parsers, mention how they fit into your pipeline. Deliverables • Source code of the detection model with setup instructions • A CLI or minimal web interface that accepts .sol files or contract addresses and returns the vulnerability report • README describing architecture, dependencies, and how to retrain or extend rules Acceptance criteria 1. For a held-out test set I supply, the model flags at least 90 % of known vulnerabilities with under 10 % false positives. 2. Runs on a standard Linux environment in less than 2 minutes per contract (average size ≈1 k lines). 3. Code is fully commented and ready for future extensions, e.g., to support additional vulnerability classes. If this sounds like a challenge you’d enjoy, tell me briefly which approach you prefer and any similar tooling you’ve built before.
Project ID: 40429211
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2 freelancers are bidding on average ₹13,500 INR for this job

Hi there, Blockchain security and Solidity development has been a major part of my work for years, and I have worked on multiple Ethereum based systems involving smart contracts, wallets, token logic, and contract analysis. My approach will be to combine tools like Slither/Mythril with custom parsing and validation logic so the detection remains practical, accurate, and easy to extend later for more vulnerability types. Then I will create a simple CLI or lightweight interface where you can upload .sol files or scan contract addresses directly and get a proper readable vulnerability report instead of raw technical logs. I also liked that you already have vulnerable and safe sample contracts ready because that will help a lot in tuning the detection quality and reducing false positives. I have a few questions regarding the preferred interface, reporting structure, and whether you want static-only analysis or partial runtime simulation as well, so I would request to connect once. Thanks, Rahul A.
₹12,000 INR in 20 days
8.4
8.4

As an Ethereum specialist with almost a decade of hands-on experience in mobile and web development, I am more than equipped to handle your project. I understand the specificities of Solidity's syntax quirks, common library usage(like OpenZeppelin), and studying the subtleties of EVM are among my core competencies. It's noteworthy that I also leverage a robust set of tools that encompasses Slither, Mythril, Foundry, and custom AST parsers in my work, which align perfectly with your requirements. This comprehensive stack has allowed me to build similar tooling that can meet and exceed the demands you've outlined. My most significant advantage is my versatility. I am capable of delivering rule-based, machine-learning driven, formal-verification driven strategies or curating a hybrid model depending on what uniquely suits your specific project requirements. My goal is to design a detection engine that not only meets but exceeds your expectations; one that not only flags vulnerabilities accurately but also fast - well below two minutes per contract scrutiny. In summary, trust goes hand-in-hand with experience and given my expansive background of 9+ years of professional coding experience complimented with a deep understanding of Ethereum, Solidity and everything it encompasses - I'm confident I'll be able to deliver exactly what you desire. Furthermore, this model will be meticulously engineered for future scalability and adaptability so that it can incorporate additi
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1.7
1.7

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