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NFTDELTA: Detecting Permission Control Vulnerabilities in NFT Contracts through Multi-View Learning

arXiv Security Archived Apr 17, 2026 ✓ Full text saved

arXiv:2604.15118v1 Announce Type: new Abstract: Permission control vulnerabilities in Non-fungible token (NFT) contracts can result in significant financial losses, as attackers may exploit these weaknesses to gain unauthorized access or circumvent critical permission checks. In this paper, we propose NFTDELTA, a framework that leverages static analysis and multi-view learning to detect permission control vulnerabilities in NFT contracts. Specifically, we extract comprehensive function Control F

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    Computer Science > Cryptography and Security [Submitted on 16 Apr 2026] NFTDELTA: Detecting Permission Control Vulnerabilities in NFT Contracts through Multi-View Learning Hailu Kuang, Xiaoqi Li, Wenkai Li, Zongwei Li Permission control vulnerabilities in Non-fungible token (NFT) contracts can result in significant financial losses, as attackers may exploit these weaknesses to gain unauthorized access or circumvent critical permission checks. In this paper, we propose NFTDELTA, a framework that leverages static analysis and multi-view learning to detect permission control vulnerabilities in NFT contracts. Specifically, we extract comprehensive function Control Flow Graph (CFG) information via two views: sequence features (representing execution paths) and graph features (capturing structural control flow). These two views are then integrated to create a unified code representation. We also define three specific categories of permission control vulnerabilities and employ a custom detector to identify defects through multi-view feature similarity analysis. Our evaluation of 795 popular NFT collections identified 241 confirmed permission control vulnerabilities, comprising 214 cases of Bypass Auth Reentrancy, 15 of Weak Auth Validation, and 12 of Loose Permission Management. Manual verification demonstrates the detector's high reliability, achieving an average precision of 97.92% and an F1-score of 81.09%. Furthermore, NFTDELTA demonstrates enhanced efficiency and scalability, proving its effectiveness in securing NFT ecosystems. Comments: 19 pages, 4 figures Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.15118 [cs.CR]   (or arXiv:2604.15118v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.15118 Focus to learn more Submission history From: Hailu Kuang [view email] [v1] Thu, 16 Apr 2026 15:07:37 UTC (392 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Apr 17, 2026
    Archived
    Apr 17, 2026
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