Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation

TL;DR

This survey addresses the multifaceted MEV problem in DeFi by introducing a comprehensive taxonomy of MEV transactions, backed by real Ethereum examples, and by critically evaluating detection and mitigation approaches. It analyzes how MEV types differ in their impact on security, efficiency, and decentralization, and discusses simulation and extraction frameworks to model MEV in practice. The study highlights gaps such as cross-domain MEV, multi-address tactics, and the limitations of current detection/mitigation methods, and it discusses potential solutions including PBS, privacy-preserving pools, and proactive detection. Overall, the paper provides a holistic roadmap for researchers, developers, and policymakers to understand, quantify, and curb harmful MEV while seeking to democratize value creation in DeFi.

Abstract

Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.

Figures (17)

• Figure 1: Taxonomy and comparison of MEV transactions.
• Figure 2: Displacement front-running transaction.
• Figure 3: Suppression front-running transaction.
• Figure 4: Back-running transaction.
• Figure 5: Single DEX sandwich transaction.