SmartGuard: Making Prediction Verifiable Through Transaction Sequences for Smart Contract Vulnerability Detection

Deep learning-based detectors have been widely proposed to predict vulnerabilities in smart contracts, yet their unreliable predictions pose severe security risks to financial transactions, making it critical to verify the reliability of vulnerability predictions. However, existing methods only produce prediction results, failing to provide an evidence chain to check whether these predicted vulnerabilities genuinely exist and deliver further guidance for fixing the vulnerabilities. Thus, making these vulnerability predictions verifiable remains an unexplored problem. In this paper, we propose SmartGuard, a novel verifiable vulnerability prediction framework for deep learning-based detectors and specifically designed for smart contracts. It integrates a deep learning-based detector with a symbolic prediction validator, where the latter acts as the backend formal engine to verify vulnerability predictions. Specifically, we present a graph-sequence multi-task learning model to detect vulnerabilities while generating transaction sequences that serve as evidence chains, explicitly revealing the triggering logic behind vulnerabilities. To bridge the gap between deep learning-based detectors and symbolic validators, we symbolically execute the generated transaction sequences against the verification conditions of vulnerability predictions. Furthermore, we propose a new metric, Vulnerability Prediction Suspiciousness (VPS), to evaluate the reliability of the predicted results. We implement SmartGuard on three representative types of vulnerabilities (Reentrancy, Ether-leaking, and Suicidal) to evaluate its performance in real-world scenarios. Our experimental results show that SmartGuard can effectively verify doubtful vulnerability predictions in real-world scenarios. It also outperforms state-of-the-art baselines by consistently reducing false reports by at least 15% across various Solidity versions. Case studies on complex contracts and DApps further demonstrate SmartGuard’s effectiveness in practice.