Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts
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Abstract (arXiv)
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 I, 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 II, 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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Mar 2024.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
- 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilitiescited by 2
- 2025SIExVulTS: Sensitive Information Exposure Vulnerability Detection System using Transformer Models and Static Analysiscited by 1
- 2025BugSweeper: Function-Level Detection of Smart Contract Vulnerabilities Using Graph Neural Networkscited by 1
- 2025Facial Recognition Leveraging Generative Adversarial Networkscited by 0
- 2025BugSweeper: Function-Level Detection of Smart Contract Vulnerabilities Using Graph Neural Networks10 Dec 2025
- 2025SIExVulTS: Sensitive Information Exposure Vulnerability Detection System using Transformer Models and Static Analysis26 Aug 2025
- 2025Facial Recognition Leveraging Generative Adversarial Networks17 May 2025
- 2024Detection Made Easy: Potentials of Large Language Models for Solidity Vulnerabilities15 Sep 2024