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VulFinder: Exploring Chaincode Vulnerabilities More Effectively and Efficiently Using Knowledge Graph Based Defect Pattern Matching
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DOI：10.1109/TSE.2025.3605379.png)
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Abstract
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Hyperledger Fabric is an open-source project of Linux Foundation, it is a modular blockchain framework and has become an unofficial standard for enterprise blockchain platforms. In Hyperledger Fabric, smart contract is also known as chaincode, which are usually written using general-purpose languages, including Go, Java or Node.js etc. Although there are some vulnerability detection methods for Java and Node.js, there are very few vulnerability detection methods for Go, especially when it is used as a smart contract programming language in Hyperledger Fabric. In this article, we propose a knowledge graph based defect pattern matching method and develop a tool called VulFinder to detect vulnerabilities in chaincode, i.e. the smart contracts written using Go language. Knowledge graph is used because it can fully retain the syntax and logic information of smart contracts. The method consists of two key steps: a knowledge graph is constructed from Go language specification and chaincode source code, including the definition of ontology layer and the construction of instance layer; the defect patterns are defined and SPARQL query statements are used to match and locate vulnerabilities on the knowledge graph. To evaluate the detection effectiveness and efficiency of VulFinder, we construct two datasets through manual analysis and vulnerabilities injection due to the lack of public datasets. Experimental results show that VulFinder can detect 22 kinds of typical vulnerabilities of chaincode effectively, and the recall is as high as 98.87%, while the false negative rate is as low as 1.13%.
Keywords:
Hyperledger fabric
chaincode
vulnerability detection
knowledge graph
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Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection
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