Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts

Author:

Wang Zexu1ORCID,Chen Jiachi2ORCID,Wang Yanlin2ORCID,Zhang Yu3ORCID,Zhang Weizhe3ORCID,Zheng Zibin4ORCID

Affiliation:

1. Sun Yat-sen University, Zhuhai, China / Peng Cheng Laboratory, Shenzhen, China

2. Sun Yat-sen University, Zhuhai, China

3. Harbin Institute of Technology, Harbin, China / Peng Cheng Laboratory, Shenzhen, China

4. Sun Yat-sen University, Zhuhai, China / GuangDong Engineering Technology Research Center of Blockchain, Zhuhai, China

Abstract

Reentrancy vulnerability as one of the most notorious vulnerabilities, has been a prominent topic in smart contract security research. Research shows that existing vulnerability detection presents a range of challenges, especially as smart contracts continue to increase in complexity. Existing tools perform poorly in terms of efficiency and successful detection rates for vulnerabilities in complex contracts. To effectively detect reentrancy vulnerabilities in contracts with complex logic, we propose a tool named SliSE. SliSE’s detection process consists of two stages: Warning Search and Symbolic Execution Verification . In Stage 1, SliSE utilizes program slicing to analyze the Inter-contract Program Dependency Graph (I-PDG) of the contract, and collects suspicious vulnerability information as warnings. In Stage 2, symbolic execution is employed to verify the reachability of these warnings, thereby enhancing vulnerability detection accuracy. SliSE obtained the best performance compared with eight state-of-the-art detection tools. It achieved an F1 score of 78.65%, surpassing the highest score recorded by an existing tool of 9.26%. Additionally, it attained a recall rate exceeding 90% for detection of contracts on Ethereum. Overall, SliSE provides a robust and efficient method for detection of Reentrancy vulnerabilities for complex contracts.

Publisher

Association for Computing Machinery (ACM)

Reference48 articles.

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2. Conditioned program slicing

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4. Stefanos Chaliasos, Marcos Antonios Charalambous, Liyi Zhou, Rafaila Galanopoulou, Arthur Gervais, Dimitris Mitropoulos, and Ben Livshits. 2023. Smart contract and defi security: Insights from tool evaluations and practitioner surveys. arXiv preprint arXiv:2304.02981.

5. Jiachi Chen Mingyuan Huang Zewei Lin Peilin Zheng and Zibin Zheng. 2023. To Healthier Ethereum: A Comprehensive and Iterative Smart Contract Weakness Enumeration. arxiv:cs.SE/2308.10227.

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