Timely Identification of Victim Addresses in DeFi Attacks

Abstract

Over the past years, Decentralized Finance (DeFi) protocols have suffered from several attacks. As a result, multiple solutions have been proposed to prevent such attacks. Most solutions rely on identifying malicious transactions before they are included in blocks. However, with the emergence of private pools, attackers can now conceal their exploit transactions from attack detection. This poses a significant challenge for existing security tools, which primarily rely on monitoring transactions in public mempools. To effectively address this challenge, it is crucial to develop proactive methods that predict malicious behavior before the actual attack transactions occur. In this work, we introduce a novel methodology to infer potential victims by analyzing the deployment bytecode of malicious smart contracts. Our idea leverages the fact that attackers typically split their attacks into two stages, a deployment stage, and an attack stage. This provides a small window to analyze the attacker's deployment code and identify victims in a timely manner before the actual attack occurs. By analyzing a set of past DeFi attacks, this work demonstrates that the victim of an attack transaction can be identified with an accuracy of almost 70%.