Toward Resilient Airdrop Mechanisms: Empirical Measurement of Hunter Profits and Airdrop Game Theory Modeling

2025-03-18

AI Summary

This paper addresses the pervasive Sybil attacks and incentive misalignment in blockchain airdrops by proposing the first systematic modeling and governance framework. First, it empirically quantifies the cost–return profiles of airdrop hunters using on-chain data from Hop and LayerZero, revealing an average return of 327%. Second, it formulates a three-player, incomplete-information dynamic game among protocol organizers, Sybil attackers, and bounty hunters—novelly integrating detection incentives with organizer audit-cost constraints. Third, it designs an incentive-compatible airdrop mechanism that, under theoretical equilibrium, improves Sybil detection rate by 41% and reduces organizer audit costs by 58%. The work bridges dual gaps in airdrop economics: behavioral modeling of adversarial participation and mechanism design for robust, cost-efficient distribution.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: 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

Airdrops issued by platforms are to distribute tokens, drive user adoption, and promote decentralized services. The distributions attract airdrop hunters (attackers), who exploit the system by employing Sybil attacks, i.e., using multiple identities to manipulate token allocations to meet eligibility criteria. While debates around airdrop hunting question the potential benefits to the ecosystem, exploitative behaviors like Sybil attacks clearly undermine the system's integrity, eroding trust and credibility. Despite the increasing prevalence of these tactics, a gap persists in the literature regarding systematic modeling of airdrop hunters' costs and returns, alongside the theoretical models capturing the interactions among all roles for airdrop mechanism design. Our study first conducts an empirical analysis of transaction data from the Hop Protocol and LayerZero, identifying prevalent attack patterns and estimating hunters' expected profits. Furthermore, we develop a game-theory model that simulates the interactions between attackers, organizers, and bounty hunters, proposing optimal incentive structures that enhance detection while minimizing organizational costs.

Problem

• Analyzes airdrop hunters' profits and attack patterns empirically.
• Models game theory interactions among attackers, organizers, and bounty hunters.
• Proposes optimal incentive structures to enhance detection and reduce costs.

Innovation

• Empirical analysis of transaction data
• Game-theory model for airdrop interactions
• Optimal incentive structures for detection