SmartGraphical: A Human-in-the-Loop Framework for Detecting Smart Contract Logical Vulnerabilities via Pattern-Driven Static Analysis and Visual Abstraction

• URL: http://arxiv.org/abs/2603.08580v1
• Date: Mon, 09 Mar 2026 16:35:17 GMT
• Title: SmartGraphical: A Human-in-the-Loop Framework for Detecting Smart Contract Logical Vulnerabilities via Pattern-Driven Static Analysis and Visual Abstraction
• Abstract summary: This paper introduces SmartGraphical, a novel security framework specifically engineered to identify logical attack surfaces.<n>By synthesizing the context-dependent nature of logical bugs, SmartGraphical enables developers to interpret warnings within a visualized structural context.<n>Our findings indicate that this hybrid methodology significantly enhances the interpretability and detection rate of non-trivial logical security threats in smart contracts.
• Score: 0.30586855806896046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart contracts are fundamental components of blockchain ecosystems; however, their security remains a critical concern due to inherent vulnerabilities. While existing detection methodologies are predominantly syntax-oriented, targeting reentrancy and arithmetic errors, they often overlook logical flaws arising from defective business logic. This paper introduces SmartGraphical, a novel security framework specifically engineered to identify logical attack surfaces. By synthesizing automated static analysis with an interactive graphical representation of contract architectures, SmartGraphical facilitates a comprehensive inspection of a contract's functional control flow. To mitigate the context-dependent nature of logical bugs, the tool adopts a human-in-the-loop approach, empowering developers to interpret heuristic warnings within a visualized structural context. The efficacy of SmartGraphical was validated through a rigorous empirical evaluation involving a large dataset of real-world contracts and a large-scale user study with 100 developers of varying expertise. Furthermore, the framework's performance was demonstrated through case studies on high-profile exploits, such as the SYFI rebase failure and farming protocol flash swap attacks, proving that SmartGraphical identifies intricate vulnerabilities that elude state-of-the-art automated detectors. Our findings indicate that this hybrid methodology significantly enhances the interpretability and detection rate of non-trivial logical security threats in smart contracts.

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