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Unity is Strength: Enhancing Precision in Reentrancy Vulnerability Detection of Smart Contract Analysis Tools

  • Zexu Wang
  • , Jiachi Chen*
  • , Peilin Zheng
  • , Yu Zhang
  • , Weizhe Zhang
  • , Zibin Zheng
  • *Corresponding author for this work
  • Sun Yat-Sen University
  • Peng Cheng Laboratory
  • Zhejiang University
  • School of Computer Science and Technology, Harbin Institute of Technology
  • GuangDong Engineering Technology Research Center of Blockchain

Research output: Contribution to journalArticlepeer-review

Abstract

Reentrancy is one of the most notorious vulnerabilities in smart contracts, resulting in significant digital asset losses. However, many previous works indicate that current Reentrancy detection tools suffer from high false positive rates. Even worse, recent years have witnessed the emergence of new Reentrancy attack patterns fueled by intricate and diverse vulnerability exploit mechanisms. Unfortunately, current tools face a significant limitation in their capacity to adapt and detect these evolving Reentrancy patterns. Consequently, ensuring precise and highly extensible Reentrancy vulnerability detection remains critical challenges for existing tools. To address this issue, we propose a tool named ReEP, designed to reduce the false positives for Reentrancy vulnerability detection. Additionally, ReEP can integrate multiple tools, expanding its capacity for vulnerability detection. It evaluates results from existing tools to verify vulnerability likelihood and reduce false positives. ReEP also offers excellent extensibility, enabling the integration of different detection tools to enhance precision and cover different vulnerability attack patterns. We perform ReEP to eight existing state-of-the-art Reentrancy detection tools. The average precision of these eight tools increased from the original 0.5% to 73% without sacrificing recall. Furthermore, ReEP exhibits robust extensibility. By integrating multiple tools, the precision further improved to a maximum of 83.6%. These results demonstrate that ReEP effectively unites the strengths of existing works, enhances the precision of Reentrancy vulnerability detection tools.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Software Engineering
Volume51
Issue number1
DOIs
StatePublished - 2025
Externally publishedYes

Keywords

  • Reentrancy detection
  • path pruning
  • smart contracts
  • symbolic execution

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