RANDAO Manipulation in the Presence of MEV

2026-10-01

AI Summary

This study addresses the manipulation incentives inherent in Ethereum’s RANDAO mechanism under maximal extractable value (MEV) conditions, where traditional block-proportion metrics fail to provide precise quantification. By integrating game theory with optimization algorithms, this work constructs the first unified multi-source reward model encompassing consensus rewards, MEV, and arbitrage, thereby overcoming the limitations of single block-counting approaches and introducing a method for computing ε-optimal strategies. The analysis reveals that reward rollover and superlinear scaling significantly amplify manipulation incentives, while demonstrating that tail-slot slashing mechanisms can effectively restore honest equilibria. Ultimately, this research establishes a novel paradigm for the dynamic evaluation of protocol security under complex economic incentives.

Abstract

Ethereum's randomness beacon (RANDAO) is well-known to be manipulable, and prior work [AW24] computes the precise fraction of blocks a strategic proposer can propose. The fraction of blocks proposed, however, is only a proxy for participants' rewards. We propose a generalized reward model capturing many canonical forms of rewards: consensus reward rollover, multi-block MEV, CEX-DEX arbitrage, oracle manipulation, and others. We provide a methodology that computes an ε-optimal strategy for any reward scheme in our model (and in particular, any combination of the above rewards). Finally, we apply our methodology to several canonical examples, and establish the sensitivity of RANDAO manipulation to the underlying rewards. We find that if rewards partially roll over, or scale super-linearly with consecutive blocks, the incentive to manipulate RANDAO is amplified. Lastly, we investigate tail-slot slashing, which can be modeled as a reward function, and show that honest equilibria can be recovered.

Problem

• RANDAO manipulation
• Maximal Extractable Value (MEV)
• Ethereum
• reward model
• block proposer incentives

Innovation

• RANDAO manipulation
• generalized reward model
• epsilon-optimal strategy
• Maximal Extractable Value (MEV)
• tail-slot slashing