Taking Attacks to the Next Level: A Framework for Front-Running Attacks on Blockchain Systems
Zihan Wang, Yusen Wang, Chentian Yuan, Na Ruan, Jie Li, Jiong Lou · 2025
The rapid growth of permissionless blockchain systems have been accompanied by various threats. Due to the transparency of the blockchain, transactions published by traders can be discovered and exploited by malicious attackers in front-running attacks. These attacks harm profits of traders, compromise transactional security and threaten consensus process of blockchain systems. In this article, we present a comprehensive analytical framework of front-running attacks on permissionless blockchains for the first time. We analyze the entire process of front-running attacks, and then model the relationship between the success probability of front-running attacks and various factors during the process. Moreover, we analyze the behavior of victim and attacker in front-running attacks based on a dynamic game model, and derive the theoretical profit threshold of victim and attacker in closed form. Our extensive experimental results demonstrate that the framework is reasonable and accurate.