🤖 AI Summary
Existing approaches to smart contract vulnerability detection are largely confined to syntactic analysis and struggle to identify deep logical flaws stemming from business logic defects. This work proposes a human-in-the-loop framework that integrates pattern-driven static analysis with visual abstraction, enabling developers to interactively explore logical attack surfaces through functional control flow graphs. By systematically incorporating visualization and expert judgment into the detection process, the approach overcomes the contextual understanding limitations inherent in purely automated tools. Evaluated on a large-scale dataset of real-world contracts and a user study involving 100 developers, the framework not only successfully reproduces high-severity vulnerabilities such as the SYFI rebase failure but also uncovers multiple logic flaws missed by mainstream detection tools, significantly improving both explainability and recall.
📝 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.