Strengthening DeFi Security: A Static Analysis Approach to Flash Loan Vulnerabilities

• URL: http://arxiv.org/abs/2411.01230v2
• Date: Sun, 23 Feb 2025 21:18:41 GMT
• Title: Strengthening DeFi Security: A Static Analysis Approach to Flash Loan Vulnerabilities
• Authors: Ka Wai Wu,
• Abstract summary: We introduce FlashDeFier, an advanced detection framework for price manipulation vulnerabilities arising from flash loans.<n>FlashDeFier expands the scope of taint sources and sinks, enabling comprehensive analysis of data flows across DeFi protocols.<n>Tested against a dataset of high-profile DeFi incidents, FlashDeFier identifies 76.4% of price manipulation vulnerabilities, marking a 30% improvement over DeFiTainter.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rise of Decentralized Finance (DeFi) has brought novel financial opportunities but also exposed serious security vulnerabilities, with flash loans frequently exploited for price manipulation attacks. These attacks, leveraging the atomic nature of flash loans, allow malicious actors to manipulate DeFi protocol oracles and pricing mechanisms within a single transaction, causing substantial financial losses. Traditional smart contract analysis tools address some security risks but often struggle to detect the complex, inter-contract dependencies that make flash loan attacks challenging to identify. In response, we introduce FlashDeFier, an advanced detection framework that enhances static taint analysis to target price manipulation vulnerabilities arising from flash loans. FlashDeFier expands the scope of taint sources and sinks, enabling comprehensive analysis of data flows across DeFi protocols. The framework constructs detailed inter-contract call graphs to capture sophisticated data flow patterns, significantly improving detection accuracy. Tested against a dataset of high-profile DeFi incidents, FlashDeFier identifies 76.4% of price manipulation vulnerabilities, marking a 30% improvement over DeFiTainter. These results highlight the importance of adaptive detection frameworks that evolve alongside DeFi threats, underscoring the need for hybrid approaches combining static, dynamic, and symbolic analysis methods for resilient DeFi security.

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