FlashSyn: Flash Loan Attack Synthesis via Counter Example Driven Approximation

In decentralized finance (DeFi), lenders can offer flash loans to bor-rowers, i.e., loans that are only valid within a blockchain transaction and must be repaid with fees by the end of that transaction. Unlike normal loans, flash loans allow borrowers to borrow large assets without upfront collaterals deposits. Malicious adversaries use flash loans to gather large assets to exploit vulnerable DeFi protocols. In this paper, we introduce a new framework for automated syn-thesis of adversarial transactions 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 approximation 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 novel counterexample driven approximation refinement technique. We implement our framework in a tool named FlashSyn. We evaluate FlashSyn on 16 DeFi protocols that were victims to flash loan attacks and 2 DeFi protocols from Damn Vulnerable DeFi challenges. FlashSyn automatically synthesizes an adversarial attack for 16 of the 18 benchmarks, demonstrating its effectiveness in finding possible flash loan attacks.