How to Serve Your Sandwich? MEV Attacks in Private L2 Mempools

2026-01-27

AI Summary

This study systematically investigates the feasibility, profitability, and prevalence of sandwich attacks within private mempools of Ethereum Layer 2 networks. By formalizing the optimal front-running transaction size, integrating on-chain transaction-level data analysis, simulating priority fee strategies, and modeling sequencing mechanisms in builder-free environments, the work quantifies— for the first time—the impact of co-inclusion constraints and probabilistic execution on attack success rates in private mempools. The findings reveal that existing heuristic detection methods generate substantial false positives: a majority of transactions flagged as sandwich attacks actually yield negative net profits. This indicates that such attacks are both rare and economically unviable in L2 private mempools.

Technology Category

• Search and Optimization: Evaluation and Analysis
• Machine Learning: Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We study the feasibility, profitability, and prevalence of sandwich attacks on Ethereum rollups with private mempools. First, we extend a formal model of optimal front- and back-run sizing, relating attack profitability to victim trade volume, liquidity depth, and slippage bounds. We complement it with an execution-feasibility model that quantifies co-inclusion constraints under private mempools. Second, we examine execution constraints in the absence of builder markets: without guaranteed atomic inclusion, attackers must rely on sequencer ordering, redundant submissions, and priority fee placement, which renders sandwiching probabilistic rather than deterministic. Third, using transaction-level data from major rollups, we show that naive heuristics overstate sandwich activity. We find that the majority of flagged patterns are false positives and that the median net return for these attacks is negative. Our results suggest that sandwiching, while endemic and profitable on Ethereum L1, is rare, unprofitable, and largely absent in rollups with private mempools. These findings challenge prevailing assumptions, refine measurement of MEV in L2s, and inform the design of sequencing policies.

Problem

• sandwich attacks
• MEV
• private mempools
• Ethereum rollups
• transaction ordering

Innovation

• sandwich attacks
• private mempools
• MEV
• rollups
• co-inclusion constraints