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Maximal Extractable Value Mitigation Approaches in Ethereum and Layer-2 Chains: A Comprehensive Survey

Zeinab Alipanahloo, Abdelhakim Senhaji Hafid, Kaiwen Zhang

TL;DR

This survey catalogs MEV mitigation approaches for Ethereum and L2s, proposing a two-axis taxonomy: prevention/reduction and side-effect mitigation. It highlights practical methods such as fair ordering (single, decentralized, and shared sequencing), mempool privacy (threshold and delay encryption, TEEs), private transactions, smart-contract-level protections, and Proposer-Builder Separation (notably MEV-Boost). Key findings emphasize mempool privacy and PBS as particularly promising for censorship resistance and democratized MEV, while noting centralization risks in relays and sequencer architectures. The work also identifies open challenges and future directions in shared sequencing, witness encryption, and scalable, trust-lean privacy techniques for a rapidly evolving blockchain landscape.

Abstract

Maximal Extractable Value (MEV) represents a pivotal challenge within the Ethereum ecosystem; it impacts the fairness, security, and efficiency of both Layer 1 (L1) and Layer 2 (L2) networks. MEV arises when miners or validators manipulate transaction ordering to extract additional value, often at the expense of other network participants. This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust. Given the growing complexity of blockchain applications, particularly with the increase of Decentralized Finance (DeFi) protocols, addressing MEV is crucial. This paper presents a comprehensive survey of MEV mitigation techniques as applied to both Ethereums L1 and various L2 solutions. We provide a novel categorization of mitigation strategies; we also describe the challenges, ranging from transaction sequencing and cryptographic methods to reconfiguring decentralized applications (DApps) to reduce front-running opportunities. We investigate their effectiveness, implementation challenges, and impact on network performance. By synthesizing current research, real-world applications, and emerging trends, this paper aims to provide a detailed roadmap for researchers, developers, and policymakers to understand and combat MEV in an evolving blockchain landscape.

Maximal Extractable Value Mitigation Approaches in Ethereum and Layer-2 Chains: A Comprehensive Survey

TL;DR

This survey catalogs MEV mitigation approaches for Ethereum and L2s, proposing a two-axis taxonomy: prevention/reduction and side-effect mitigation. It highlights practical methods such as fair ordering (single, decentralized, and shared sequencing), mempool privacy (threshold and delay encryption, TEEs), private transactions, smart-contract-level protections, and Proposer-Builder Separation (notably MEV-Boost). Key findings emphasize mempool privacy and PBS as particularly promising for censorship resistance and democratized MEV, while noting centralization risks in relays and sequencer architectures. The work also identifies open challenges and future directions in shared sequencing, witness encryption, and scalable, trust-lean privacy techniques for a rapidly evolving blockchain landscape.

Abstract

Maximal Extractable Value (MEV) represents a pivotal challenge within the Ethereum ecosystem; it impacts the fairness, security, and efficiency of both Layer 1 (L1) and Layer 2 (L2) networks. MEV arises when miners or validators manipulate transaction ordering to extract additional value, often at the expense of other network participants. This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust. Given the growing complexity of blockchain applications, particularly with the increase of Decentralized Finance (DeFi) protocols, addressing MEV is crucial. This paper presents a comprehensive survey of MEV mitigation techniques as applied to both Ethereums L1 and various L2 solutions. We provide a novel categorization of mitigation strategies; we also describe the challenges, ranging from transaction sequencing and cryptographic methods to reconfiguring decentralized applications (DApps) to reduce front-running opportunities. We investigate their effectiveness, implementation challenges, and impact on network performance. By synthesizing current research, real-world applications, and emerging trends, this paper aims to provide a detailed roadmap for researchers, developers, and policymakers to understand and combat MEV in an evolving blockchain landscape.
Paper Structure (24 sections, 2 equations, 11 figures, 4 tables)

This paper contains 24 sections, 2 equations, 11 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. MEV
  5. Front-Running
  6. PGA Example
  7. L2 Scaling Solutions
  8. Taxonomy of MEV Mitigation Strategies
  9. Fair Ordering Policy
  10. Single Sequencer
  11. Decentralized Sequencing
  12. Shared sequencers
  13. Discussion
  14. Privacy-preserving
  15. Encrypted Mempool
  16. ...and 9 more sections

Figures (11)

  • Figure 1: Opportunity for Front-running Attacks Arising from validators' Arbitrary Transaction Selection and Ordering
  • Figure 2: Taxonomy of MEV Mitigation Strategies
  • Figure 3: TimeBoost Algorithm
  • Figure 4: Themis Network Protocol
  • Figure 5: Example of a Condorcet Cycle
  • ...and 6 more figures