Turn the Rudder: A Beacon of Reentrancy Detection for Smart Contracts on Ethereum
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Abstract (arXiv)
Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value().
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Mar 2023.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Modelscited by 2
- 2025An Initial Exploration of Fine-tuning Small Language Models for Smart Contract Reentrancy Vulnerability Detectioncited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contractscited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts17 Jul 2026
- 2025An Initial Exploration of Fine-tuning Small Language Models for Smart Contract Reentrancy Vulnerability Detection25 May 2025
- 2025SymGPT: Auditing Smart Contracts via Combining Symbolic Execution with Large Language Models11 Feb 2025