Quantifying Blockchain Extractable Value: How dark is the forest?
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Abstract (arXiv)
Permissionless blockchains such as Bitcoin have excelled at financial services. Yet, opportunistic traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain extractable value (BEV). The recent emergence of centralized BEV relayer portrays BEV as a positive additional revenue source. Because BEV was quantitatively shown to deteriorate the blockchain's consensus security, BEV relayers endanger the ledger security by incentivizing rational miners to fork the chain. For example, a rational miner with a 10% hashrate will fork Ethereum if a BEV opportunity exceeds 4x the block reward. However, related work is currently missing quantitative insights on past BEV extraction to assess the practical risks of BEV objectively. In this work, we allow to quantify the BEV danger by deriving the USD extracted from sandwich attacks, liquidations, and decentralized exchange arbitrage. We estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses when capturing 49,691 cryptocurrencies and 60,830 on-chain markets. The highest BEV instance we find amounts to 4.1M USD, 616.6x the Ethereum block reward. Moreover, while the practitioner's community has discussed the existence of generalized trading bots, we are, to our knowledge, the first to provide a concrete algorithm. Our algorithm can replace unconfirmed transactions without the need to understand the victim transactions' underlying logic, which we estimate to have yielded a profit of 57,037.32 ETH (35.37M USD) over 32 months of past blockchain data. Finally, we formalize and analyze emerging BEV relay systems, where miners accept BEV transactions from a centralized relay server instead of the peer-to-peer (P2P) network. We find that such relay systems aggravate the consensus layer attacks and therefore further endanger blockchain security.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2021.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 5; 2026: 6.
15 papers.
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- 2023Blockchain Large Language Modelscited by 16
- 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learningcited by 8
- 2025AI Agent Smart Contract Exploit Generationcited by 8
- 2024Detecting Financial Bots on the Ethereum Blockchaincited by 6
- 2025Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchainscited by 5
- 2025When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Marketscited by 5
- 2025Detecting Various DeFi Price Manipulations with LLM Reasoningcited by 4
- 2025LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embeddingcited by 3
- 2023SourceP: Detecting Ponzi Schemes on Ethereum with Source Codecited by 1
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Financecited by 1
- 2026If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalencecited by 0
- 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chainscited by 0
- 2026Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents30 Sep 2026
- 2026EVAGE: Autonomous MEV Generation and Adaptation via Multi-Agent Harness23 Sep 2026
- 2026When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economy15 Sep 2026
- 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains2 Sep 2026
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance18 Aug 2026
- 2026If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalence26 Jun 2026
- 2025When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets30 Sep 2025
- 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
- 2025Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains24 May 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
3 more
- 2024Detecting Financial Bots on the Ethereum Blockchain28 Mar 2024
- 2023SourceP: Detecting Ponzi Schemes on Ethereum with Source Code2 Jun 2023
- 2023Blockchain Large Language Models25 Apr 2023
- 2025AI Agent Smart Contract Exploit Generationcited by 8
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Financecited by 1
- 2026When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economycited 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
- 2026When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economy15 Sep 2026
- 2026PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance18 Aug 2026
- 2025AI Agent Smart Contract Exploit Generation8 Jul 2025
- 2023Blockchain Large Language Modelscited by 16
- 2024Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learningcited by 8
- 2024Detecting Financial Bots on the Ethereum Blockchaincited by 6
- 2025Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchainscited by 5
- 2025When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Marketscited by 5
- 2025Detecting Various DeFi Price Manipulations with LLM Reasoningcited by 4
- 2025LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embeddingcited by 3
- 2023SourceP: Detecting Ponzi Schemes on Ethereum with Source Codecited by 1
- 2026If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalencecited by 0
- 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chainscited by 0
- 2026Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agentscited by 0
- 2026Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents30 Sep 2026
- 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains2 Sep 2026
- 2026If It Walks Like an Arbitrage: Protocol-Agnostic Detection with Decidable Structural Equivalence26 Jun 2026
- 2025When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets30 Sep 2025
- 2025LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding4 Sep 2025
- 2025Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains24 May 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
- 2024Detecting Financial Bots on the Ethereum Blockchain28 Mar 2024
- 2023SourceP: Detecting Ponzi Schemes on Ethereum with Source Code2 Jun 2023
- 2023Blockchain Large Language Models25 Apr 2023