Lanturn: Measuring Economic Security of Smart Contracts Through Adaptive Learning

被引:0
作者
Babel, Kushal [1 ]
Javaheripi, Mojan [2 ]
Ji, Yan [1 ]
Kelkar, Mahimna [1 ]
Koushanfar, Farinaz [2 ]
Juels, Ari [1 ]
机构
[1] Cornell Tech, IC3, New York, NY 10044 USA
[2] Univ Calif San Diego, La Jolla, CA 92093 USA
来源
PROCEEDINGS OF THE 2023 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, CCS 2023 | 2023年
关键词
MEV; Machine Learning; Optimization; Decentralized Finance; Cryptoeconomics; Smart Contract Security Tool;
D O I
10.1145/3576915.3623204
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We introduce Lanturn: a general purpose adaptive learning-based framework for measuring the cryptoeconomic security of composed decentralized-finance (DeFi) smart contracts. Lanturn discovers strategies comprising of concrete transactions for extracting economic value from smart contracts interacting with a particular transaction environment. We formulate the strategy discovery as a black-box optimization problem and leverage a novel adaptive learning-based algorithm to address it. Lanturn features three key properties. First, it needs no contract-specific heuristics or reasoning, due to our black-box formulation of cryptoeconomic security. Second, it utilizes a simulation framework that operates natively on blockchain state and smart contract machine code, such that transactions returned by Lanturn's learning-based optimization engine can be executed on-chain without modification. Finally, Lanturn is scalable in that it can explore strategies comprising a large number of transactions that can be reordered or subject to insertion of new transactions. We evaluate Lanturn on the historical data of the biggest and most active DeFi Applications: Sushiswap, UniswapV2, UniswapV3, and AaveV2. Our results show that Lanturn not only rediscovers existing, well-known strategies for extracting value from smart contracts, but also discovers new strategies that are previously undocumented. Lanturn also consistently discovers higher value than evidenced in the wild, surpassing a natural baseline computed using value extracted by bots and other strategic agents.
引用
收藏
页码:1212 / 1226
页数:15
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