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LibScan: Smart Contract Library Misuse Detection with Iterative Feedback and Static Verification

arXiv Security Archived Apr 02, 2026 ✓ Full text saved

arXiv:2604.00657v1 Announce Type: cross Abstract: Smart contracts are self-executing programs that manage financial transactions on blockchain networks. Developers commonly rely on third-party code libraries to improve both efficiency and security. However, improper use of these libraries can introduce hidden vulnerabilities that are difficult to detect, leading to significant financial losses. Existing automated tools struggle to identify such misuse because it often requires understanding the

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    Computer Science > Software Engineering [Submitted on 1 Apr 2026] LibScan: Smart Contract Library Misuse Detection with Iterative Feedback and Static Verification Yishun Wang, Wenkai Li, Xiaoqi Li, Zongwei Li, Lei Xie, Yuqing Zhang Smart contracts are self-executing programs that manage financial transactions on blockchain networks. Developers commonly rely on third-party code libraries to improve both efficiency and security. However, improper use of these libraries can introduce hidden vulnerabilities that are difficult to detect, leading to significant financial losses. Existing automated tools struggle to identify such misuse because it often requires understanding the developer's intent rather than simply scanning for known code patterns. This paper presents LibScan, an automated detection framework that combines large language model (LLM)-based semantic reasoning with rule-based code analysis, identifying eight distinct categories of library misuse in smart contracts. To improve detection reliability, the framework incorporates an iterative self-correction mechanism that refines its analysis across multiple rounds, alongside a structured knowledge base derived from large-scale empirical studies of real-world misuse cases. Experiments conducted on 662 real-world smart contracts demonstrate that LibScan achieves an overall detection accuracy of 85.15\%, outperforming existing tools by a margin of over 16 percentage points. Ablation experiments further confirm that combining both analysis approaches yields substantially better results than either method used independently. Subjects: Software Engineering (cs.SE); Cryptography and Security (cs.CR) Cite as: arXiv:2604.00657 [cs.SE]   (or arXiv:2604.00657v1 [cs.SE] for this version)   https://doi.org/10.48550/arXiv.2604.00657 Focus to learn more Submission history From: Wenkai Li [view email] [v1] Wed, 1 Apr 2026 09:04:01 UTC (419 KB) Access Paper: HTML (experimental) view license Current browse context: cs.SE < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CR 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
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    ◬ AI & Machine Learning
    Published
    Apr 02, 2026
    Archived
    Apr 02, 2026
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