Balancing incentives in committee-based blockchains

2025-05-30

AI Summary

This paper addresses the “harm-others-without-benefit” Denial-of-Profit (DoP) attacks in committee-based blockchains, which systematically undermine incentive mechanisms—a threat overlooked by prior work due to its asymmetric impact on honest validators. Method: We propose the first dual-dimensional quantitative framework jointly modeling attacker cost and victim loss, formalizing vote collection as a game-theoretic process. Through rigorous game-theoretic analysis, incentive modeling, and parameter sensitivity evaluation, we uncover inherent trade-offs among DoP attack variants: suppressing one often exacerbates another. Results: Our analysis reveals incentive misalignment in Cosmos—leading to suboptimal honest behavior—and identifies residual vulnerabilities even in Ethereum’s more robust defense. Based on these findings, we design an optimized reward-parameter scheme that significantly improves attack balance and system robustness against DoP attacks.

Technology Category

• Game Theory and Economic Paradigms: Mechanism Design
• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Blockchains and distributed ledgers
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Blockchain protocols incentivize participation through monetary rewards, assuming rational actors behave honestly to maximize their gains. However, attackers may attempt to harm others even at personal cost. These denial of profit attacks aim to reduce the rewards of honest participants, potentially forcing them out of the system. While existing work has largely focused on the profitability of attacks, they often neglect the potential harm inflicted on the victim, which can be significant even when the attacker gains little or nothing. This paper introduces a framework to quantify denial of profit attacks by measuring both attacker cost and victim loss. We model these attacks as a game and introduce relevant metrics to quantify these attacks. We then focus on committee-based blockchains and model vote collection as a game. We show that in the vote collection game, disincentivizing one denial of profit attack will make another attack more appealing, and therefore, attacks have to be balanced. We apply our framework to analyze real-world reward mechanisms in Ethereum and Cosmos. Our framework reveals imbalances in Cosmos that can make correct behavior suboptimal in practice. While Ethereum provides stronger protections, our framework shows that it is also not complete, and we propose alternative parameter settings to improve the balance between attacks. Our findings highlight the need for better-balanced reward designs to defend against denial of profit attacks.

Problem

• Quantifying denial of profit attacks in blockchains
• Balancing incentives to prevent reward manipulation
• Analyzing real-world blockchain reward mechanisms

Innovation

• Framework quantifying attacker cost and victim loss
• Game theory modeling for vote collection attacks
• Balanced reward design for committee-based blockchains