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

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.