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

Jump to full leaderboard
  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 2501-2550 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2501 PolyGarden — polygon Rugpull — View →
2502 PolyGorilla — polygon Rugpull — View →
2503 PolyCloud — polygon Rugpull — View →
2504 Rainicorn — binance Honeypot — View →
2505 Cobrafinance.org — binance Rugpull — View →
2506 Haikyu — binance Rugpull — View →
2507 Zombies Farm — binance Rugpull — View →
2508 Astro Yield — binance Rugpull — View →
2509 Polyvee Finance — polygon Rugpull — View →
2510 PolyGold — polygon Rugpull — View →
2511 Dino Finance — polygon Rugpull — View →
2512 PUGDOG INU — binance Rugpull — View →
2513 Shadowswap Finance — fantom Rugpull — View →
2514 Glockswap — polygon Rugpull — View →
2515 Evilswap — binance Rugpull — View →
2516 DinoSwap.farm — binance Rugpull — View →
2517 Alienswap — binance Rugpull — View →
2518 Oilrig Finance — binance Rugpull — View →
2519 Basset Farm — binance Rugpull — View →
2520 Chopper Finance — binance Rugpull — View →
2521 Pumpkin Swap — binance Rugpull — View →
2522 PolyWhat — polygon Rugpull — View →
2523 Geckoswap — binance Rugpull — View →
2524 Deerdefi — binance Rugpull — View →
2525 RUM COIN — ethereum Rugpull — View →
2526 Antisocial.finance — binance Rugpull — View →
2527 SunnyDefi finance — binance Rugpull — View →
2528 NoaSwap — binance Rugpull — View →
2529 Aladdin — binance Rugpull — View →
2530 Ozone Finance Token — binance Rugpull — View →
2531 Original.finance — binance Rugpull — View →
2532 Snakedefi — binance Rugpull — View →
2533 BACON — ethereum Rugpull — View →
2534 MYSTERYToken — binance Rugpull — View →
2535 Elara Finance — binance Rugpull — View →
2536 crocofi.org(CROCO) — binance Rugpull — View →
2537 Vine Protocol — binance Rugpull — View →
2538 Mortal Kombat — binance Rugpull — View →
2539 Dragonfly — binance Rugpull — View →
2540 Uranium U92 — binance Rugpull — View →
2541 Raccoon Swap — binance Rugpull — View →
2542 WindSwap — binance Rugpull — View →
2543 DogX Token — binance Rugpull — View →
2544 SeaShell.Financ — binance Rugpull — View →
2545 Polyfox.finance — polygon Rugpull — View →
2546 Zenko — polygon Rugpull — View →
2547 Pumaswap Token — binance Rugpull — View →
2548 Missile — binance Rugpull — View →
2549 Mummy Token — binance Rugpull — View →
2550 Fish Token — ethereum Rugpull — 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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