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In decentralized finance (DeFi), lenders can offer flash loans to borrowers,i.e., loans that are only valid within a blockchain transaction and must berepaid with fees by the end of that transaction. Unlike normal loans, flashloans allow borrowers to borrow large assets without upfront collateralsdeposits. Malicious adversaries use flash loans to gather large assets toexploit vulnerable DeFi protocols. In this paper, we introduce a new frameworkfor automated synthesis of adversarial transactions that exploit DeFi protocolsusing flash loans. To bypass the complexity of a DeFi protocol, we propose anew technique to approximate the DeFi protocol functional behaviors usingnumerical methods (polynomial linear regression and nearest-neighborinterpolation). We then construct an optimization query using the approximatedfunctions of the DeFi protocol to find an adversarial attack constituted of asequence of functions invocations with optimal parameters that gives themaximum profit. To improve the accuracy of the approximation, we propose anovel counterexample driven approximation refinement technique. We implementour framework in a tool named FlashSyn. We evaluate FlashSyn on 16 DeFiprotocols that were victims to flash loan attacks and 2 DeFi protocols fromDamn Vulnerable DeFi challenges. FlashSyn automatically synthesizes anadversarial attack for 16 of the 18 benchmarks. Among the 16 successful cases,FlashSyn identifies attack vectors yielding higher profits than those employedby historical hackers in 3 cases, and also discovers multiple distinct attackvectors in 10 cases, demonstrating its effectiveness in finding possible flashloan attacks.

FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven Approximation