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SoK: MEV Countermeasures: Theory and Practice

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arxiv 2212.05111 v2 pith:T3SVNFD7 submitted 2022-12-09 cs.CR

classification cs.CR
keywords countermeasuresblockchainmempoolauctiondataplatformspracticeprofits
verification ladder T0 review T1 audit T2 compute T3 formal
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Blockchains offer strong security guarantees, but they cannot protect the ordering of transactions. Powerful players, such as miners, sequencers, and sophisticated bots, can reap significant profits by selectively including, excluding, or re-ordering user transactions. Such profits are called Miner/Maximal Extractable Value or MEV. MEV bears profound implications for blockchain security and decentralization. While numerous countermeasures have been proposed, there is no agreement on the best solution. Moreover, solutions developed in academic literature differ quite drastically from what is widely adopted by practitioners. For these reasons, this paper systematizes the knowledge of the theory and practice of MEV countermeasures. The contribution is twofold. First, we present a comprehensive taxonomy of 30 proposed MEV countermeasures, covering four different technical directions. Secondly, we empirically studied the most popular MEV-auction-based solution with rich blockchain and mempool data. We also present the Mempool Guru system, a public service system that collects, persists, and analyzes the Ethereum mempool data for research. In addition to gaining insights into MEV auction platforms' real-world operations, our study shed light on the prevalent censorship by MEV auction platforms as a result of the recent OFAC sanction, and its implication on blockchain properties.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reveal, Correct, Then Pay: Encrypted Mempools and Perpetual Funding Security

    cs.CR 2026-07 accept novelty 7.0 of 10

    In commit-then-reveal mempools, a self-authored trade is hidden from the arbitrageurs who would correct it, so perpetual-futures funding distortion is amplified; privacy can therefore increase manipulation value despi...

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