Thomas Thiery

Aikaterini-Panagiota Stouka, Julian Ma, Thomas Thiery

Transaction Fee Mechanism (TFM) design in blockchain protocols has gained significant attention following the pioneering work of Roughgarden [EC' 21], which established a formal framework for analyzing user and block proposer incentives under various Transaction Fee Mechanisms, including Ethereum's current fee mechanism EIP-1559. However, the original TFM framework and follow-up TFM works overlook the critical challenge of censorship resistance-specifically in the presence of an external malicious actor who is willing to bribe the proposer to censor a transaction. In this paper, we extend the Roughgarden's framework to capture censorship resistance under bribery attacks via a Bayesian game, where a strategic block proposer's "type" is determined by a bribe function from an external malicious actor. Under this framework, the definition of a standard TFM is extended to a bribery-aware TFM. This technique is broadly applicable to analyze censorship resistance under bribery attacks of both single and multiple proposer protocols within the original TFM scope. We choose to utilize it to evaluate the incentive compatibility and censorship resistance of several TFMs within the context of a multiple proposer protocol called Fork-Choice Enforced Inclusion Lists (FOCIL). FOCIL represents a critical evolution in the Ethereum roadmap, serving as the consensus and censorship resistance flagship for the upcoming Hegota hard fork. It aims to bolster Ethereum's censorship resistance by enabling multiple proposers to contribute to block construction. While recent works such as Garimidi et al.[FC' 25] have extended the TFM framework to multiple proposer settings, they do not aim to capture censorship under bribery attacks and they are not compatible with the unique hierarchical structure of FOCIL.

Xinyuan Sun, Davide Crapis, Matt Stephenson et al.

Credible commitment devices have been a popular approach for robust multi-agent coordination. However, existing commitment mechanisms face limitations like privacy, integrity, and susceptibility to mediator or user strategic behavior. It is unclear if the cooperative AI techniques we study are robust to real-world incentives and attack vectors. However, decentralized commitment devices that utilize cryptography have been deployed in the wild, and numerous studies have shown their ability to coordinate algorithmic agents facing adversarial opponents with significant economic incentives, currently in the order of several million to billions of dollars. In this paper, we use examples in the decentralization and, in particular, Maximal Extractable Value (MEV) (arXiv:1904.05234) literature to illustrate the potential security issues in cooperative AI. We call for expanded research into decentralized commitments to advance cooperative AI capabilities for secure coordination in open environments and empirical testing frameworks to evaluate multi-agent coordination ability given real-world commitment constraints.