The 12-Line Anti-Bot Trick That Saved Our Airdrop Snapshot From Sybil Farms
A new anti-bot technique successfully identified 94% of Sybil wallets during a testnet airdrop. The method utilized behavioral entropy analysis on RPC call patterns, proving more effective than traditional filters. This approach flagged 23,400 Sybil clusters while maintaining a low false positive rate of 6.3%.
- ▪The anti-bot trick involved a 12-line Python heuristic that analyzed wallet interactions.
- ▪The method processed 847,000 wallet interactions in just 4.2 hours.
- ▪It ran within an Intel TDX enclave, ensuring RPC logs remained unencrypted.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3559398) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } VoltageGPU Posted on May 25 • Originally published at voltagegpu.com The 12-Line Anti-Bot Trick That Saved Our Airdrop Snapshot From Sybil Farms #blockchain #airdropsecurity #sybilattack #confidentialcomputing Quick Answer: A 12-line Python heuristic caught 94% of Sybil wallets in our testnet airdrop before we spent $0.01 on tokens. The trick? Behavioral entropy analysis on RPC call patterns — not wallet age, not balance thresholds. Cost to run: $0.68/hr on an RTX 4090.
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