Remeasuring the Arbitrage and Sandwich Attacks of Maximal Extractable Value in Ethereum

TL;DR

This paper introduces new algorithms to accurately identify MEV activities in Ethereum, overcoming previous heuristic limitations, and provides a comprehensive analysis of the MEV ecosystem post-merge.

Contribution

It proposes a profitability-based identification algorithm and two robust methods to detect MEV activities on a large Ethereum dataset, improving accuracy over heuristic approaches.

Findings

Characterized the Ethereum MEV landscape after The Merge

Analyzed the impact of private transaction architectures

Examined the adoption of back-running mechanisms

Abstract

Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem. By strategically including, excluding, or reordering transactions within blocks, block producers can extract additional value, which in turn incentivizes them to keep the decentralization of the whole blockchain platform. Before September 2022, around $675M was extracted in terms of MEV in Ethereum. Despite its importance, current work on identifying MEV activities suffers from two limitations. On the one hand, current methods heavily rely on clumsy heuristic rule-based patterns, leading to numerous false negatives or positives. On the other hand, the observations and conclusions are drawn from the early stage of Ethereum, which cannot be used as effective guiding principles after The Merge. To address these challenges, in this work, we innovatively proposed a profitability identification algorithm. Based on this, we designed two robust algorithms to identify MEV activities on our collected largest-ever dataset. Based on the identified results, we have characterized the overall landscape of the Ethereum MEV ecosystem, the impact the private transaction architectures bring in, and the adoption of back-running mechanisms. Our research sheds light on future MEV-related work.