Maximal Extractable Value (MEV) drives the prosperity of the blockchain ecosystem. By strategically including, excluding, or reordering transactions within blocks, block producers can extract additional value, which in turn incentivizes them to keep the decentralization of the whole blockchain platform. Before September 2022, around $675M was extracted in terms of MEV in Ethereum. Despite its importance, current work on identifying MEV activities suffers from two limitations. On the one hand, current methods heavily rely on clumsy heuristic rule-based patterns, leading to numerous false negatives or positives. On the other hand, the observations and conclusions are drawn from the early stage of Ethereum, which cannot be used as effective guiding principles after The Merge. To address these challenges, in this work, we innovatively proposed a profitability identification algorithm. Based on this, we designed two robust algorithms to identify MEV activities on our collected largest-ever dataset. Based on the identified results, we have characterized the overall landscape of the Ethereum MEV ecosystem, the impact the private transaction architectures bring in, and the adoption of back-running mechanisms. Our research sheds light on future MEV-related work.
Michael Neuder, Daniel J. Moroz, Rithvik Rao, David C. Parkes
Proof-of-Stake consensus protocols give rise to complex modeling challenges. We analyze the Babylon update (October 2019) to the Proof-of-Stake protocol on the Tezos blockchain, and demonstrate that, under certain conditions, rational participants are incentivized to behave dishonestly. In doing so, we provide a theoretical analysis of the feasibility and profitability of a block stealing attack that we call selfish endorsing, a concrete instance of an attack previously only theoretically considered. We propose and analyze a simple change to the Tezos protocol which significantly reduces the (already small) profitability of this dishonest behavior, and introduce a new delay and reward scheme that is provably secure against length-1 and length-2 selfish endorsing attacks. Our framework provides a template for analyzing other Proof-of-Stake protocols for the possibility of selfish behavior.