CRCEFLGTJun 2, 2021

Maximizing Extractable Value from Automated Market Makers

arXiv:2106.01870v41 citations
Originality Synthesis-oriented
AI Analysis

This addresses a security and fairness issue for users of decentralized finance platforms, but it appears incremental as it builds on known vulnerabilities without introducing a new mitigation.

The paper tackles the problem of adversaries exploiting transaction-ordering issues in Automated Market Makers (AMMs) to extract value from users, and it devises an effective procedure to construct a strategy for maximizing this extracted value.

Automated Market Makers (AMMs) are decentralized applications that allow users to exchange crypto-tokens without the need for a matching exchange order. AMMs are one of the most successful DeFi use cases: indeed, major AMM platforms process a daily volume of transactions worth USD billions. Despite their popularity, AMMs are well-known to suffer from transaction-ordering issues: adversaries can influence the ordering of user transactions, and possibly front-run them with their own, to extract value from AMMs, to the detriment of users. We devise an effective procedure to construct a strategy through which an adversary can maximize the value extracted from user transactions.

Foundations

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