Eliminating Sandwich Attacks with the Help of Game Theory
This work addresses a critical issue for traders on AMMs by providing a practical solution to reduce financial losses from sandwich attacks, though it is incremental as it builds on existing game theory and algorithmic approaches.
The paper tackles the problem of sandwich attacks on automated market makers (AMMs) in Ethereum, where predatory bots exploit slippage tolerances to profit from traders, and introduces an algorithm that reduces costs by over a factor of 100 compared to existing methods while minimizing transaction failure risk.
Predatory trading bots lurking in Ethereum's mempool present invisible taxation of traders on automated market makers (AMMs). AMM traders specify a slippage tolerance to indicate the maximum price movement they are willing to accept. This way, traders avoid automatic transaction failure in case of small price movements before their trade request executes. However, while a too-small slippage tolerance may lead to trade failures, a too-large slippage tolerance allows predatory trading bots to profit from sandwich attacks. These bots can extract the difference between the slippage tolerance and the actual price movement as profit. In this work, we introduce the sandwich game to analyze sandwich attacks analytically from both the attacker and victim perspectives. Moreover, we provide a simple and highly effective algorithm that traders can use to set the slippage tolerance. We unveil that most broadcasted transactions can avoid sandwich attacks while simultaneously only experiencing a low risk of transaction failure. Thereby, we demonstrate that a constant auto-slippage cannot adjust to varying trade sizes and pool characteristics. Our algorithm outperforms the constant auto-slippage suggested by the biggest AMM, Uniswap, in all performed tests. Specifically, our algorithm repeatedly demonstrates a cost reduction exceeding a factor of 100.