FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven Approximation
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In decentralized finance (DeFi) ecosystem, lenders can offer flash loans to borrowers, i.e., loans that are only valid within a blockchain transaction and must be repaid with some fees by the end of that transaction. Unlike normal loans, flash loans allow borrowers to borrow a large amount of assets without upfront collaterals deposits. Malicious adversaries can use flash loans to gather large amount of assets to launch costly exploitations targeting DeFi protocols. In this paper, we introduce a new framework for automated synthesis of adversarial contracts that exploit DeFi protocols using flash loans. To bypass the complexity of a DeFi protocol, we propose a new technique to approximate the DeFi protocol functional behaviors using numerical methods (polynomial linear regression and nearest-neighbor interpolation). We then construct an optimization query using the approximated functions of the DeFi protocol to find an adversarial attack constituted of a sequence of functions invocations with optimal parameters that gives the maximum profit. To improve the accuracy of the approximation, we propose a new counterexamples-driven approximation refinement technique. We implement our framework in a tool called FlashSyn. We evaluate FlashSyn on 12 DeFi protocols that were victims to flash loan attacks and DeFi protocols from Damn Vulnerable DeFi challenges. FlashSyn automatically synthesizes an adversarial attack for each one of them.
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