High-Frequency Trading on Decentralized On-Chain Exchanges
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Abstract (arXiv)
Decentralized exchanges (DEXs) allow parties to participate in financial markets while retaining full custody of their funds. However, the transparency of blockchain-based DEX in combination with the latency for transactions to be processed, makes market-manipulation feasible. For instance, adversaries could perform front-running -- the practice of exploiting (typically non-public) information that may change the price of an asset for financial gain. In this work we formalize, analytically exposit and empirically evaluate an augmented variant of front-running: sandwich attacks, which involve front- and back-running victim transactions on a blockchain-based DEX. We quantify the probability of an adversarial trader being able to undertake the attack, based on the relative positioning of a transaction within a blockchain block. We find that a single adversarial trader can earn a daily revenue of over several thousand USD when performing sandwich attacks on one particular DEX -- Uniswap, an exchange with over 5M USD daily trading volume by June 2020. In addition to a single-adversary game, we simulate the outcome of sandwich attacks under multiple competing adversaries, to account for the real-world trading environment.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Sep 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 3; 2026: 3.
10 papers.
- 2023Blockchain Large Language Modelscited by 17
- 2025AI Agent Smart Contract Exploit Generationcited by 9
- 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learningcited by 8
- 2024GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereumcited by 5
- 2025Detecting Various DeFi Price Manipulations with LLM Reasoningcited by 4
- 2022Do not rug on me: Zero-dimensional Scam Detectioncited by 3
- 2025LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embeddingcited by 3
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Financecited by 1
- 2026DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attackscited 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
- 2026DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks10 Sep 2026
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance18 Aug 2026
- 2025LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding4 Sep 2025
- 2025AI Agent Smart Contract Exploit Generation8 Jul 2025
- 2025Detecting Various DeFi Price Manipulations with LLM Reasoning17 Feb 2025
- 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning9 Sep 2024
- 2024GasTrace: Detecting Sandwich Attack Malicious Accounts in Ethereum30 May 2024
- 2023Blockchain Large Language Models25 Apr 2023
- 2022Do not rug on me: Zero-dimensional Scam Detection16 Jan 2022