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

Junliang Luo, Hong Kang, Shuhao Zheng, Xue Liu

TL;DR

This work addresses the vulnerability of token airdrops to Sybil‑driven exploits by combining empirical analysis of Hop Protocol and LayerZero attacker data with a four‑stage game‑theoretic model that couples organizer incentives, attacker reporting, and bounty hunter contracts. The empirical analysis identifies consistent hunter patterns (funding networks, cross‑chain transfers, and uniformity) and demonstrates economically meaningful profits in many groups, while the theoretical model yields incentive‑compatible contract structures that deter undetected attacks and minimize costs. Key contributions include closed‑form expressions for optimal self‑report rewards, a supermodular attacker‑report subgame with guaranteed equilibria, and a contract feasibility framework that delivers optimal bounty rewards and task complexities under IC and IR constraints. The proposed framework provides practical guidance for building resilient airdrop mechanisms with incentivized reporting and self‑reporting options, improving trust and sustainability in decentralized distributions.

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.

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

TL;DR

This work addresses the vulnerability of token airdrops to Sybil‑driven exploits by combining empirical analysis of Hop Protocol and LayerZero attacker data with a four‑stage game‑theoretic model that couples organizer incentives, attacker reporting, and bounty hunter contracts. The empirical analysis identifies consistent hunter patterns (funding networks, cross‑chain transfers, and uniformity) and demonstrates economically meaningful profits in many groups, while the theoretical model yields incentive‑compatible contract structures that deter undetected attacks and minimize costs. Key contributions include closed‑form expressions for optimal self‑report rewards, a supermodular attacker‑report subgame with guaranteed equilibria, and a contract feasibility framework that delivers optimal bounty rewards and task complexities under IC and IR constraints. The proposed framework provides practical guidance for building resilient airdrop mechanisms with incentivized reporting and self‑reporting options, improving trust and sustainability in decentralized distributions.

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.

Paper Structure

This paper contains 30 sections, 6 theorems, 79 equations, 9 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Collection
  4. Airdrop Hunter Behavior Patterns
  5. Measuring on Hop Protocol
  6. Measuring on LayerZero
  7. Profit Modeling on Hop Protocol
  8. Airdrop Game theory Modeling
  9. Overview
  10. Stage 1: Self-Report Reward Policy
  11. Stage 2: Attackers' Self-Report Strategy
  12. Optimal Decision for Each Attacker:
  13. Stage 3: Bounty Reward Mechanism
  14. Stage 4: Bounty Hunters' Strategy
  15. Conclusion
  16. ...and 15 more sections

Key Result

Proposition 1

The optimal self-report reward ratio $\pi_s^*$ in Stage 1 is given by:

Figures (9)

  • Figure 1: Percent of addresses funded by an initial funder, and sent to a common receiver in each group
  • Figure 2: Percent of addresses involved in sequential transfers and the avg number of sequential transfers per address in each group
  • Figure 3: Number of sequential transfers and average token value per transfer over time (grouped by 30 days).
  • Figure 4: Uniformity of transaction count and volume in the Hop dataset
  • Figure 5: Uniformity of transaction count and volume in the LayerZero dataset
  • ...and 4 more figures

Theorems & Definitions (14)

  • Proposition 1
  • Lemma 1: Optimal Rewards for Given Task Complexities
  • Proposition 2: Optimal Reward Ratio $\pi_{b,j}^*$
  • Theorem 1: Contract Feasibility
  • proof
  • Proposition 3
  • proof
  • Definition 1: Incentive Compatibility (IC)
  • Definition 2: Individual Rationality (IR)
  • Lemma 2: Feasibility of the Contract
  • ...and 4 more