# Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain

arXiv: https://arxiv.org/abs/2102.03347 · HTML: https://mlsanity.com/paper/2102.03347/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2021.

## Built on by (most cited first, 7 of 7)

- [Detecting Financial Bots on the Ethereum Blockchain](https://mlsanity.com/paper/2403.19530/) (2024)
- [GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum](https://mlsanity.com/paper/2405.19971/) (2024)
- [AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security](https://mlsanity.com/paper/2212.06951/) (2022)
- [FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model](https://mlsanity.com/paper/2311.14514/) (2023)
- [DMind-3: A Sovereign Edge--Local--Cloud AI System with Controlled Deliberation and Correction-Based Tuning for Safe, Low-Latency Transaction Execution](https://mlsanity.com/paper/2602.11651/) (2026)
- [If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalence](https://mlsanity.com/paper/2608.20377/) (2026)
- [EVAGE: Autonomous MEV Generation and Adaptation via Multi-Agent Harness](https://mlsanity.com/paper/2609.27424/) (2026)

## Abstract (arXiv)

Ethereum prospered the inception of a plethora of smart contract applications, ranging from gambling games to decentralized finance. However, Ethereum is also considered a highly adversarial environment, where vulnerable smart contracts will eventually be exploited. Recently, Ethereum's pool of pending transaction has become a far more aggressive environment. In the hope of making some profit, attackers continuously monitor the transaction pool and try to frontrun their victims' transactions by either displacing or suppressing them, or strategically inserting their transactions. This paper aims to shed some light into what is known as a dark forest and uncover these predators' actions. We present a methodology to efficiently measure the three types of frontrunning: displacement, insertion, and suppression. We perform a large-scale analysis on more than 11M blocks and identify almost 200K attacks with an accumulated profit of 18.41M USD for the attackers, providing evidence that frontrunning is both, lucrative and a prevalent issue.