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Register a new userOn the Just-In-Time Discovery of Profit-Generating Transactions in DeFi Protocols
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Added by
Liyi Zhou
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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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 191.48ETH (76,592USD) and 72.44ETH (28,976USD) respectively, with the highest transaction revenue being 81.31ETH(32,524USD) and22.40ETH (8,960USD) 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 874x. Given optimal adversarial strategies provided by a Markov Decision Process (MDP), we quantify the value threshold at which a profitable transaction qualifies as Miner ExtractableValue (MEV) and would incentivize MEV-aware miners to fork the blockchain. For instance, we find that on Ethereum, a miner with a hash rate of 10% would fork the blockchain if an MEV opportunity exceeds 4x the block reward.
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Credit allows a lender to loan out surplus capital to a borrower. In the traditional economy, credit bears the risk that the borrower may default on its debt, the lender hence requires upfront collateral from the borrower, plus interest fee payments. Due to the atomicity of blockchain transactions, lenders can offer flash loans, i.e., loans that are only valid within one transaction and must be repaid by the end of that transaction. This concept has lead to a number of interesting attack possibilities, some of which were exploited in February 2020. This paper is the first to explore the implication of transaction atomicity and flash loans for the nascent decentralized finance (DeFi) ecosystem. We show quantitatively how transaction atomicity increases the arbitrage revenue. We moreover analyze two existing attacks with ROIs beyond 500k%. We formulate finding the attack parameters as an optimization problem over the state of the underlying Ethereum blockchain and the state of the DeFi ecosystem. We show how malicious adversaries can efficiently maximize an attack profit and hence damage the DeFi ecosystem further. Specifically, we present how two previously executed attacks can be boosted to result in a profit of 829.5k USD and 1.1M USD, respectively, which is a boost of 2.37x and 1.73x, respectively.
The decentralized and trustless nature of cryptocurrencies and blockchain technology leads to a shift in the digital world. The possibility to execute small programs, called smart contracts, on cryptocurrencies like Ethereum opened doors to countless new applications. One particular exciting use case is decentralized finance (DeFi), which aims to revolutionize traditional financial services by founding them on a decentralized infrastructure. We show the potential of DeFi by analyzing its advantages compared to traditional finance. Additionally, we survey the state-of-the-art of DeFi products and categorize existing services. Since DeFi is still in its infancy, there are countless hurdles for mass adoption. We discuss the most prominent challenges and point out possible solutions. Finally, we analyze the economics behind DeFi products. By carefully analyzing the state-of-the-art and discussing current challenges, we give a perspective on how the DeFi space might develop in the near future.
Decentralized finance, i.e., DeFi, has become the most popular type of application on many public blockchains (e.g., Ethereum) in recent years. Compared to the traditional finance, DeFi allows customers to flexibly participate in diverse blockchain financial services (e.g., lending, borrowing, collateralizing, exchanging etc.) via smart contracts at a relatively low cost of trust. However, the open nature of DeFi inevitably introduces a large attack surface, which is a severe threat to the security of participants funds. In this paper, we proposed BLOCKEYE, a real-time attack detection system for DeFi projects on the Ethereum blockchain. Key capabilities provided by BLOCKEYE are twofold: (1) Potentially vulnerable DeFi projects are identified based on an automatic security analysis process, which performs symbolic reasoning on the data flow of important service states, e.g., asset price, and checks whether they can be externally manipulated. (2) Then, a transaction monitor is installed offchain for a vulnerable DeFi project. Transactions sent not only to that project but other associated projects as well are collected for further security analysis. A potential attack is flagged if a violation is detected on a critical invariant configured in BLOCKEYE, e.g., Benefit is achieved within a very short time and way much bigger than the cost. We applied BLOCKEYE in several popular DeFi projects and managed to discover potential security attacks that are unreported before. A video of BLOCKEYE is available at https://youtu.be/7DjsWBLdlQU.
Recently, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. In this work, we study the privacy guarantees of gossip protocols in general networks in terms of differential privacy and prediction uncertainty. First, lower bounds of the differential privacy guarantees are derived for gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. For the private gossip algorithm, the differential privacy and prediction uncertainty guarantees are derived in closed form. Moreover, considering that these two metrics may be restrictive in some scenarios, the relaxed variants are proposed. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantify the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present at the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.
The rapid growth of Decentralized Finance (DeFi) boosts the Ethereum ecosystem. At the same time, attacks towards DeFi applications (apps) are increasing. However, to the best of our knowledge, existing smart contract vulnerability detection tools cannot be directly used to detect DeFi attacks. Thats because they lack the capability to recover and understand high-level DeFi semantics, e.g., a user trades a token pair X and Y in a Decentralized EXchange (DEX). In this work, we focus on the detection of two types of new attacks on DeFi apps, including direct and indirect price manipulation attacks. The former one means that an attacker directly manipulates the token price in DEX by performing an unwanted trade in the same DEX by attacking the vulnerable DeFi app. The latter one means that an attacker indirectly manipulates the token price of the vulnerable DeFi app (e.g., a lending app). To this end, we propose a platform-independent way to recover high-level DeFi semantics by first constructing the cash flow tree from raw Ethereum transactions and then lifting the low-level semantics to high-level ones, including token trade, liquidity mining, and liquidity cancel. Finally, we detect price manipulation attacks using the patterns expressed with the recovered DeFi semantics. We have implemented a prototype named tool{} and applied it to more than 350 million transactions. It successfully detected 432 real-world attacks in the wild. We confirm that they belong to four known security incidents and five zero-day ones. We reported our findings. Two CVEs have been assigned. We further performed an attack analysis to reveal the root cause of the vulnerability, the attack footprint, and the impact of the attack. Our work urges the need to secure the DeFi ecosystem.
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