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arXiv · 2307.08549

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

Christoph Sendner·Ruisi Zhang·Alexander Hefter·Alexandra Dmitrienko·Farinaz Koushanfar
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Abstract

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 algorithms achieve high accuracy at the contract level, they require developers to manually inspect source code to locate bugs. To this end, we present G-Scan, the first end-to-end fine-grained line-level vulnerability detection system evaluated on the first-of-its-kind real world dataset. G-Scan first converts smart contracts to code graphs in a dependency and hierarchy preserving manner. Next, we train a graph neural network to identify vulnerable nodes and assess security risks. Finally, the code graphs with node vulnerability predictions are mapped back to the smart contracts for line-level localization. We train and evaluate G-Scan on a collected real world smart contracts dataset with line-level annotations on reentrancy vulnerability, one of the most common and severe types of smart contract vulnerabilities. With the well-designed graph representation and high-quality dataset, G-Scan achieves 93.02% F1-score in contract-level vulnerability detection and 93.69% F1-score in line-level vulnerability localization. Additionally, the lightweight graph neural network enables G-Scan to localize vulnerabilities in 6.1k lines of code smart contract within 1.2 seconds.

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Christoph Sendner, Ruisi Zhang, Alexander Hefter, Alexandra Dmitrienko, Farinaz Koushanfar. 2023-07-17. G-Scan: Graph Neural Networks for Line-Level Vulnerability Identification in Smart Contracts. https://arxiv.org/abs/2307.08549

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