Flash Boys 2.0: Frontrunning, Transaction Reordering, and Consensus Instability in Decentralized Exchanges
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Abstract (arXiv)
Blockchains, and specifically smart contracts, have promised to create fair and transparent trading ecosystems. Unfortunately, we show that this promise has not been met. We document and quantify the widespread and rising deployment of arbitrage bots in blockchain systems, specifically in decentralized exchanges (or "DEXes"). Like high-frequency traders on Wall Street, these bots exploit inefficiencies in DEXes, paying high transaction fees and optimizing network latency to frontrun, i.e., anticipate and exploit, ordinary users' DEX trades. We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots. We also study bots' profit-making strategies, with a focus on blockchain-specific elements. We observe bots engage in what we call priority gas auctions (PGAs), competitively bidding up transaction fees in order to obtain priority ordering, i.e., early block position and execution, for their transactions. PGAs present an interesting and complex new continuous-time, partial-information, game-theoretic model that we formalize and study. We release an interactive web portal, http://frontrun.me/, to provide the community with real-time data on PGAs. We additionally show that high fees paid for priority transaction ordering poses a systemic risk to consensus-layer security. We explain that such fees are just one form of a general phenomenon in DEXes and beyond---what we call miner extractable value (MEV)---that poses concrete, measurable, consensus-layer security risks. We show empirically that MEV poses a realistic threat to Ethereum today. Our work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Apr 2019.
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Citing papers per year: 2022: 4; 2023: 2; 2024: 1; 2025: 1; 2026: 3.
11 papers.
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- 2025When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets30 Sep 2025
- 2024SoK: Blockchain-Based Decentralized AI (DeAI)26 Nov 2024
- 2023ZeroSwap: Data-driven Optimal Market Making in DeFi13 Oct 2023
- 2023BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection29 Mar 2023
- 2022AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security14 Dec 2022
- 2022FLock: Defending Malicious Behaviors in Federated Learning with Blockchain5 Nov 2022
- 2022Explainable AI via Learning to Optimize29 Apr 2022
- 2022Do not rug on me: Zero-dimensional Scam Detection16 Jan 2022