  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.
