Efficiently Detecting Reentrancy Vulnerabilities in Complex Smart Contracts

TL;DR

This paper introduces SliSE, a two-stage tool combining program slicing and symbolic execution to efficiently detect reentrancy vulnerabilities in complex smart contracts, outperforming existing tools in accuracy and recall.

Contribution

The paper presents SliSE, a novel detection framework that improves accuracy and efficiency in identifying reentrancy vulnerabilities in complex smart contracts.

Findings

SliSE achieved an F1 score of 78.65%, surpassing previous tools.

SliSE attained over 90% recall on Ethereum contracts.

It outperformed eight state-of-the-art detection tools.

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 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.