Key takeaways:
- An arbitrage bot exploited MakerDAO’s ‘DssFlash’ contract, generating a $200 million flash loan for a profit of $3.
- The bot borrowed 200 million DAI tokens and sold them on the Aave DAI market, using $2,300 in wrapped ether (WETH) as collateral.
In recent news, a crypto bot made headlines by executing a flash loan and generating a $3.24 profit from a staggering $200 million loan. This peculiar occurrence has brought attention to the concept of flash loans in the cryptocurrency world and raised questions about their potential risks and benefits.
On Wednesday, an arbitrage bot borrowed $200 million in dai stablecoin (DAI) from MakerDAO, profiting $3.24 after transaction costs.
According to crypto data source Arkham Intelligence, the bot took use of MakerDAO’s ‘DssFlash’ contract, which allows users to borrow any amount of DAI without incurring costs.
In this instance, the bot employed a flash loan, a type of loan obtained and repaid within a single block of transactions, without the need for any initial collateral. It borrowed 200 million DAI tokens and utilized them as collateral in the Aave DAI market to borrow $2,300 worth of wrapped ether (WETH).
Once the bot acquired the WETH, it proceeded to purchase Threshold Network (T) on the Curve platform. Subsequently, the bot engaged in a sequence of transactions within a single block, selling the acquired T tokens on the Balancer platform.
That WETH was used to purchase Threshold Network (T) on Curve before selling it on Balancer in a slew of single-block transactions.
The overall profit before fees was $33, but it paid nearly $30 in transaction and protocol costs, resulting in a net profit of $3.24.
It is important to note that flash loans can be a powerful tool for efficient and innovative financial operations, it is essential to use them responsibly and with a thorough understanding of the potential risks involved.
By maintaining a vigilant approach to security and risk management, the DeFi ecosystem can continue to evolve and provide valuable opportunities while minimising the likelihood of exploitations like the one witnessed in the Euler Finance attack.
