Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain
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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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 0; 2026: 3.
7 papers.
- 2024Detecting Financial Bots on the Ethereum Blockchaincited by 6
- 2024GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereumcited by 5
- 2022AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Securitycited by 1
- 2023FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Modelcited by 0
- 2026DMind-3: A Sovereign Edge--Local--Cloud AI System with Controlled Deliberation and Correction-Based Tuning for Safe, Low-Latency Transaction Executioncited by 0
- 2026If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalencecited by 0
- 2026EVAGE: Autonomous MEV Generation and Adaptation via Multi-Agent Harnesscited by 0
- 2026EVAGE: Autonomous MEV Generation and Adaptation via Multi-Agent Harness23 Sep 2026
- 2026If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalence26 Jun 2026
- 2026DMind-3: A Sovereign Edge--Local--Cloud AI System with Controlled Deliberation and Correction-Based Tuning for Safe, Low-Latency Transaction Execution12 Feb 2026
- 2024GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum30 May 2024
- 2024Detecting Financial Bots on the Ethereum Blockchain28 Mar 2024
- 2023FRAD: Front-Running Attacks Detection on Ethereum using Ternary Classification Model24 Nov 2023
- 2022AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security14 Dec 2022