GMVD: Smart Contract Vulnerability Detection Based on GAT-Mamba Framework

GMVD: Smart Contract Vulnerability Detection Based on GAT-Mamba Framework | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article GMVD: Smart Contract Vulnerability Detection Based on GAT-Mamba Framework Mingyan Liu, Changli Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8968309/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Smart contracts hold billions of dollars worth of digital currencies, and hacking attacks can not only cause users to lose their assets but also destabilize the blockchain ecosystem.Vulnerability detection in smart contracts remains a major challenge in blockchain security. Existing methods typically rely on a fixed expert mode, which leads to low accuracy. Moreover, GNN-based models fail to effectively differentiate the significance of various interaction information, while transformer models suffer from high computational complexity. To solve this problem, we propose the GAT-Mamba framework, named GMVD, to perform the smart contract vulnerability detection task. The approach first extracts expert-defined vulnerability patterns from smart contract functions. Then, the graph features are extracted by GAT. Finally, Mamba is used to model the high-dimensional vector expression of expert mode features to improve the calculation efficiency of the model. Subsequently, graph features are extracted through GAT, and Mamba is then employed to model the high-dimensional vector representation of expert pattern features, thereby enhancing computational efficiency. Experimental results on three common vulnerabilities, reentrancy, timestamp dependency, and infinite loop, demonstrate that our framework significantly outperforms existing cutting-edge technologies. Specifically, our method achieves 94.29% accuracy in detecting reentrancy, 93.71% in timestamp dependency, and 82.49% in infinite loop detection. Physical sciences/Engineering Physical sciences/Mathematics and computing smart contract vulnerability detection GAT Mamba Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 31 Mar, 2026 Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviews received at journal 21 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 05 Mar, 2026 Editor invited by journal 05 Mar, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 27 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8968309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602760796,"identity":"d89364c0-d379-4457-8c99-2382d1e1c82e","order_by":0,"name":"Mingyan Liu","email":"","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mingyan","middleName":"","lastName":"Liu","suffix":""},{"id":602760797,"identity":"3e9e9039-f8a2-4207-a9c3-f1db7c3511a2","order_by":1,"name":"Changli Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYFCCBCBmY5BhYGA+cODDDxK08ACJxIMze0jTwmN8mIONCA3y7enPpHnKDvPozsj5cBioU55f7AB+LQZnHqRJ85xL4zG7kbvhcIEFg+HM2QkEtEgkHJPmbbOBaJnBw5BgcJuAFvkZiW1ALRJALTkPDvOwEaGF4UYyG9SWHAbitBicecZsOQfklzPPDICBLEHYL8AQe3jjTdlhObPjyY8/fPhhI88vTchhDAwsUjwgSgCsUoKgchBg/ghOJvwHiFI9CkbBKBgFIxAAABQNRk0TqXXoAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Changli","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-02-25 13:26:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8968309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8968309/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780376,"identity":"5ef0070d-c55d-4ebc-8992-cea9ef23910b","added_by":"auto","created_at":"2026-03-17 07:52:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":572743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8968309/v1_covered_4cac5aa8-0fe5-4fb2-ab9c-f9d5928fd972.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GMVD: Smart Contract Vulnerability Detection Based on GAT-Mamba Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"smart contract, vulnerability detection, GAT, Mamba","lastPublishedDoi":"10.21203/rs.3.rs-8968309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8968309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmart contracts hold billions of dollars worth of digital currencies, and hacking attacks can not only cause users to lose their assets but also destabilize the blockchain ecosystem.Vulnerability detection in smart contracts remains a major challenge in blockchain security. Existing methods typically rely on a fixed expert mode, which leads to low accuracy. Moreover, GNN-based models fail to effectively differentiate the significance of various interaction information, while transformer models suffer from high computational complexity. To solve this problem, we propose the GAT-Mamba framework, named GMVD, to perform the smart contract vulnerability detection task. The approach first extracts expert-defined vulnerability patterns from smart contract functions. Then, the graph features are extracted by GAT. Finally, Mamba is used to model the high-dimensional vector expression of expert mode features to improve the calculation efficiency of the model. Subsequently, graph features are extracted through GAT, and Mamba is then employed to model the high-dimensional vector representation of expert pattern features, thereby enhancing computational efficiency. 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