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This paper introduces LibScan, a framework for detecting smart contract library misuse by combining LLM-based semantic reasoning with rule-based code analysis. LibScan uses iterative self-correction and a knowledge base of real-world misuse cases to improve detection reliability. Experiments on 662 real-world contracts show LibScan achieves 85.15% accuracy, a 16% improvement over existing tools, demonstrating the benefit of combining LLM and rule-based approaches.
LLMs, when combined with rule-based analysis and iterative refinement, can significantly outperform existing tools in detecting subtle smart contract vulnerabilities arising from library misuse.
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.
