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Avoiding Bans in Web3: A Practical Anti-Sybil Airdrop Guide

Airdrop farming is a key strategy for Web3 users to gain wealth effects at low cost. However, project teams’ "Anti-Sybil mechanisms" act like invisible scythes—one misstep could lead to address bans or even blacklisting across networks. As a seasoned airdrop hunter, have you encountered these scenarios?

  • Addresses with hard-earned interactions labeled as "Sybil," resulting in zero airdrop rewards;

  • Multiple accounts under the same IP detected and wiped out by risk control systems;

  • Automated scripts with low efficiency, while manual operations struggle to scale...

This guide reveals the core logic behind airdrop anti-Sybil systems and provides a low-cost, high-success-rate anti-detection strategy.

1. How Do Projects Screen Sybil Addresses? 3 Pitfalls You Must Avoid

Airdrop projects typically target Sybil clusters through three dimensions:

1. IP & Device Fingerprinting

  • Batch address creation or transactions under the same IP (especially during popular airdrop campaigns);

  • Repeated browser fingerprints (Canvas fingerprint, WebGL fingerprint, timezone/language settings);

  • Hardware device IDs (e.g., phone IMEI, PC MAC address) linked to multiple wallets.

2. On-Chain Behavior Patterns

  • Concentrated fund flows (e.g., all Gas fees sourced from a single intermediary address);

  • Excessively regular transaction intervals (e.g., interactions at fixed daily times);

  • Monotonous behavior (only interacting with target protocols, no other on-chain activity).

3. Social & Identity Data

  • No domain/DID bindings (e.g., ENS, .bit) or linked social accounts (Twitter/Discord);

  • Using bots for community tasks (e.g., auto-retweeting tweets, bulk joining groups).

Case Study: A user deployed 10 addresses for a Layer2 airdrop, but all were flagged as Sybil due to interactions via the same AWS server IP, resulting in a $50k+ potential loss.

 

2. Anti-Sybil Quad Methodology: Low-Cost Disguise as "Real Users"

Step 1: IP Isolation — Sever Address Correlations

Core Principle: One address = One unique IP.
Solutions:

  • Residential Proxies: Use IPFoxy to obtain global static residential IPs (avoid datacenter IPs).

  • IP Rotation: Rotate IPs before each on-chain interaction (leverage IPFoxy’s API for on-demand switching).

  • Hybrid IP Usage: Combine proxy IPs with home broadband IPs to avoid 100% proxy reliance.

 

Step 2: Device Fingerprint Obfuscation — Ensure Address "Independence"

Basic Setup:

  • Isolate accounts across browsers (Chrome/Firefox/Brave);

  • Clear Cookies & LocalStorage after each session.

Advanced Tactics:

  • Use fingerprint browsers (Multilogin, AdsPower) for isolated environments per address;

  • Modify browser fingerprints (screen resolution, timezone, language);

  • Disable WebRTC to prevent IP leaks (use plugins like WebRTC Leak Prevent).

 

Step 3: Mimic Human Behavior — Ditch "Bot Patterns"

Randomize Timing: Set interaction intervals between 15 mins–6 hrs (use Python’s random library for delays).

Diversify Funding Paths:

  • Source initial Gas from different exchanges (e.g., Binance → Address A, OKX → Address B);

  • Conduct periodic transfers between addresses to simulate organic activity.

  • Engage with On-Chain Ecosystems: Randomly interact with other DApps (e.g., Uniswap swaps, OpenSea NFT browsing).

 

Step 4: Social Identity Binding — Build "Human Proof Chains"

Bind Decentralized IDs: Register ENS/SPACE ID domains per address, link to unique emails (ProtonMail/Tutanota).

Complete Community Tasks:

  • Manually verify Discord roles, retweet tasks on Twitter;

  • Create unique social profiles per account (avoid duplicate bios/avatars).

 

4. Pitfall Alerts: What NOT to Do

❌ Use a single MetaMask wallet to derive multiple addresses (HD Wallet paths expose links);
❌ Fund all addresses from the same exchange account;
❌ Run multiple instances on VMs/VPS (hardware fingerprints overlap);
❌ Skip tests: Always test paths with 0.001 ETH before interactions.

 

5. Conclusion

The essence of anti-Sybil defense is "making machines believe you’re human." By layering IP isolation, fingerprint obfuscation, and behavioral randomization, you significantly reduce ban risks. Remember: Project teams continuously upgrade their risk algorithms. Stay technically vigilant to thrive in the airdrop arena.

Last modified: 2025-03-07Powered by