Most Expensive Crypto Hacks Leaderboard

The biggest Web3 and DeFi exploits ranked by amount lost. Track high-impact incidents, study attack patterns, and learn how security failures scale into multi-million dollar losses.

  • $91B+

    Total Value Lost

  • 3597

    Incidents Tracked

  • 141

    Chains Affected

  • 65

    $100M+ Incidents

Top 10 Biggest Hacks

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  2. 🥈

    Mantra

    $5.50B

  4. #4

    LuBian

    $3.50B

    bitcoin

  7. #7

    Thodex

    $2.00B

  9. #9

    Finiko

    $1.50B

  10. #10

    Bybit

    $1.40B

    ethereum

Where the Largest Losses Concentrate

Top Chains by Losses

  • ethereum $9,830M
  • bitcoin $5,404M
  • bsc $3,037M
  • solana $1,697M
  • polygon $1,539M

Top Techniques by Losses

  • Other $52,517M
  • Rugpull $15,631M
  • Private Key Compromised (Brute Force) / Weak Key Generation $3,500M
  • Safe Multisig wallet Phishing Exploit / Access Control / Signer Phishing $1,635M
  • Access Control / Hot Wallet Key Compromised $1,027M

Full Leaderboard

Showing 2651-2700 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2651 DeFi Wonderland — ethereum Honeypot — View →
2652 1Million — binance Honeypot — View →
2653 SHINYSHIBA — binance Honeypot — View →
2654 88mph — ethereum Missing Input Validation — —
2655 delta.theta — binance Honeypot — View →
2656 Cooktail Inu — ethereum Honeypot — View →
2657 Yearn-V3 — ethereum Honeypot — View →
2658 komainu.finance — ethereum Honeypot — View →
2659 JUST FAIR LAUNCH — ethereum Honeypot — View →
2660 MoonkeyKong — binance Honeypot — View →
2661 Shiba Diamond — binance Honeypot — View →
2662 Kyushu Inu — ethereum Honeypot — View →
2663 MONKEY — binance Honeypot — View →
2664 ArgonNetwork — binance Honeypot — View →
2665 Misa Inu — ethereum Honeypot — View →
2666 Nemo Inu — ethereum Honeypot — View →
2667 FTX Arena — ethereum Honeypot — View →
2668 Universe of Inu — ethereum Honeypot — View →
2669 Houbi Arena — ethereum Honeypot — View →
2670 NFT.Music — binance Honeypot — View →
2671 HODLEPUFF — binance Honeypot — View →
2672 Wrapped AMC — ethereum Honeypot — View →
2673 NFTDragonBall — binance Honeypot — View →
2674 BLUEHYPER — binance Honeypot — View →
2675 PolyWeed — polygon Rugpull — View →
2676 Excavator — binance Honeypot — View →
2677 NFTify — ethereum Honeypot — View →
2678 GEMSTONE — binance Honeypot — View →
2679 Yokohama Inu — ethereum Honeypot — View →
2680 ZIDDLE — binance Honeypot — View →
2681 VentureCoin — binance Honeypot — View →
2682 Bugs Bunny — ethereum Honeypot — View →
2683 WoodPecker — binance Honeypot — View →
2684 unirocket — binance Honeypot — View →
2685 Hunny Finance — bsc Incorrect Share Accounting — —
2686 Bafe.io — binance Honeypot — View →
2687 Koshka Miu — binance Honeypot — View →
2688 Artillery — binance Honeypot — View →
2689 Formation Finance — ethereum Honeypot — View →
2690 Nagasaki Shiba — ethereum Honeypot — View →
2691 phuture.finance — ethereum Honeypot — View →
2692 Lossless — binance Honeypot — View →
2693 NFTQR — binance Honeypot — View →
2694 KingCorgi — binance Honeypot — View →
2695 Post Malone Coin — ethereum Honeypot — View →
2696 HolderFinance — binance Honeypot — View →
2697 SunDoge Inu — ethereum Honeypot — View →
2698 CHRONOSFINANCE — binance Honeypot — View →
2699 TacoSwap — binance Honeypot — View →
2700 NFTD Protocol — binance Honeypot — View →

Data sourced from DefiLlama & SunWeb3Sec/DeFiHackLabs . Thank you for keeping Web3 security data open.

Understanding the Pattern Behind Billion-Dollar Losses

The most expensive crypto hacks are not random. They cluster around repeating vulnerability patterns: access-control failures, flash-loan manipulation, oracle abuse, and unsafe external calls. Studying large-loss incidents helps security researchers prioritize high-impact threat models first.

For deeper context, explore the full Web3 Hacks Dashboard and our Smart Contract Attack Library for hands-on exploit and mitigation breakdowns.

Frequently Asked Questions

How is this leaderboard ranked?

Incidents are ranked by parsed USD amount lost in descending order. Records without parseable loss values are excluded from this ranking.

Does this include every attack type?

It includes all incidents with known loss values in the database. Use the full dashboard for broader filtering and lower-severity incidents.

How can I learn to prevent these exploits?

Study attack-class pages first, then practice in guided exercises inside the Smart Contract Hacking course.

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