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Due to the immutable and decentralized nature of Ethereum (ETH) platform,smart contracts are prone to security risks that can result in financial loss.While existing machine learning-based vulnerability detection algorithmsachieve high accuracy at the contract level, they require developers tomanually inspect source code to locate bugs. To this end, we present G-Scan,the first end-to-end fine-grained line-level vulnerability detection systemevaluated on the first-of-its-kind real world dataset. G-Scan first convertssmart contracts to code graphs in a dependency and hierarchy preserving manner.Next, we train a graph neural network to identify vulnerable nodes and assesssecurity risks. Finally, the code graphs with node vulnerability predictionsare mapped back to the smart contracts for line-level localization. We trainand evaluate G-Scan on a collected real world smart contracts dataset withline-level annotations on reentrancy vulnerability, one of the most common andsevere types of smart contract vulnerabilities. With the well-designed graphrepresentation and high-quality dataset, G-Scan achieves 93.02% F1-score incontract-level vulnerability detection and 93.69% F1-score in line-levelvulnerability localization. Additionally, the lightweight graph neural networkenables G-Scan to localize vulnerabilities in 6.1k lines of code smart contractwithin 1.2 seconds.

G-Scan: Graph Neural Networks for Line-Level Vulnerability Identification in Smart Contracts