MrLiyiZhou

Qin K, Zhou L, Gamito P, Jovanovic P, Gervais Aet al., 2021, An empirical study of DeFi liquidations: Incentives, risks, and instabilities, Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC, Pages: 336-350

Financial speculators often seek to increase their potential gains with leverage. Debt is a popular form of leverage, and with over 39.88B USD of total value locked (TVL), the Decentralized Finance (DeFi) lending markets are thriving. Debts, however, entail the risks of liquidation, the process of selling the debt collateral at a discount to liquidators. Nevertheless, few quantitative insights are known about the existing liquidation mechanisms. In this paper, to the best of our knowledge, we are the first to study the breadth of the borrowing and lending markets of the Ethereum DeFi ecosystem. We focus on Aave, Compound, MakerDAO, and dYdX, which collectively represent over 85% of the lending market on Ethereum. Given extensive liquidation data measurements and insights, we systematize the prevalent liquidation mechanisms and are the first to provide a methodology to compare them objectively. We find that the existing liquidation designs well incentivize liquidators but sell excessive amounts of discounted collateral at the borrowers' expenses. We measure various risks that liquidation participants are exposed to and quantify the instabilities of existing lending protocols. Moreover, we propose an optimal strategy that allows liquidators to increase their liquidation profit, which may aggravate the loss of borrowers.

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• Citations: 13

Zhou L, Qin K, Cully A, Livshits B, Gervais Aet al., 2021, On the just-in-time discovery of profit-generating transactions in DeFi Protocols, Pages: 919-936, ISSN: 1081-6011

Decentralized Finance (DeFi) is a blockchain-asset-enabled finance ecosystem with millions of daily USD transaction volume, billions of locked up USD, as well as a plethora of newly emerging protocols (for lending, staking, and exchanges). Because all transactions, user balances, and total value locked in DeFi are publicly readable, a natural question that arises is: how can we automatically craft profitable transactions across the intertwined DeFi platforms?In this paper, we investigate two methods that allow us to automatically create profitable DeFi trades, one well-suited to arbitrage and the other applicable to more complicated settings. We first adopt the Bellman-Ford-Moore algorithm with DeFiPoser-ARB and then create logical DeFi protocol models for a theorem prover in DeFiPoser-SMT. While DeFiPoser-ARB focuses on DeFi transactions that form a cycle and performs very well for arbitrage, DeFiPoser-SMT can detect more complicated profitable transactions. We estimate that DeFiPoser-ARB and DeFiPoser-SMT can generate an average weekly revenue of 191.48 ETH (76, 592 USD) and 72.44 ETH (28, 976 USD) respectively, with the highest transaction revenue being 81.31 ETH (32, 524 USD) and 22.40 ETH (8, 960 USD) respectively. We further show that DeFiPoser-SMT finds the known economic bZx attack from February 2020, which yields 0.48M USD. Our forensic investigations show that this opportunity existed for 69 days and could have yielded more revenue if exploited one day earlier. Our evaluation spans 150 days, given 96 DeFi protocol actions, and 25 assets.Looking beyond the financial gains mentioned above, forks deteriorate the blockchain consensus security, as they increase the risks of double-spending and selfish mining. We explore the implications of DeFiPoser-ARB and DeFiPoser-SMT on blockchain consensus. Specifically, we show that the trades identified by our tools exceed the Ethereum block reward by up to 874×. Given optimal adversarial strategies provided by a M

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• Citations: 29

Qin K, Zhou L, Livshits B, Gervais Aet al., 2021, Attacking the DeFi Ecosystem with Flash Loans for Fun and Profit, FINANCIAL CRYPTOGRAPHY AND DATA SECURITY, FC 2021, PT I, Vol: 12674, Pages: 3-32, ISSN: 0302-9743

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• Citations: 15

Zhou L, Qin K, Torres CF, Le DV, Gervais Aet al., 2020, High-Frequency Trading on Decentralized On-Chain Exchanges, 42nd IEEE Symposium on Security and Privacy

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Wu C, Zhou L, Xie C, Zheng Y, Yu Jet al., 2019, Data Quality Transaction on Different Distributed Ledger Technologies, International Conference on Big Scientific Data Management

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