Definition and Detection of Centralization Defects in Smart Contracts

• URL: http://arxiv.org/abs/2411.10169v1
• Date: Fri, 15 Nov 2024 13:16:16 GMT
• Title: Definition and Detection of Centralization Defects in Smart Contracts
• Authors: Zewei Lin, Jiachi Chen, Jiajing Wu, Weizhe Zhang, Zibin Zheng,
• Abstract summary: Security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. This paper introduces six types of centralization defects in smart contracts by manually analyzing 597 Stack Exchange posts and 117 audit reports. We introduce a tool named CDRipper (Centralization Defects Ripper) designed to identify the defined centralization defects.
• Score: 30.24160537607527
• License:
• Abstract: In recent years, security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. A centralization defect refers to any error, flaw, or fault in a smart contract's design or development stage that introduces a single point of failure. Such defects allow a specific account or user to disrupt the normal operations of smart contracts, potentially causing malfunctions or even complete project shutdowns. Despite the significance of this issue, most current smart contract analyses overlook centralization defects, focusing primarily on other types of defects. To address this gap, our paper introduces six types of centralization defects in smart contracts by manually analyzing 597 Stack Exchange posts and 117 audit reports. For each defect, we provide a detailed description and code examples to illustrate its characteristics and potential impacts. Additionally, we introduce a tool named CDRipper (Centralization Defects Ripper) designed to identify the defined centralization defects. Specifically, CDRipper constructs a permission dependency graph (PDG) and extracts the permission dependencies of functions from the source code of smart contracts. It then detects the sensitive operations in functions and identifies centralization defects based on predefined patterns. We conduct a large-scale experiment using CDRipper on 244,424 real-world smart contracts and evaluate the results based on a manually labeled dataset. Our findings reveal that 82,446 contracts contain at least one of the six centralization defects, with our tool achieving an overall precision of 93.7%.

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