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

  • 3598

    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 3451-3500 of 3598 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
3451 Boson Token — ethereum Honeypot — View →
3452 SafeAmerica — binance Honeypot — View →
3453 ShineFinance — binance Honeypot — View →
3454 Virax Coin — binance Honeypot — View →
3455 Farrow Token — binance Honeypot — View →
3456 Humble — binance Honeypot — View →
3457 KRYPTCOIN — binance Honeypot — View →
3458 SafeStorm — binance Honeypot — View →
3459 AutoAction Token — binance Honeypot — View →
3460 SafeMoonz — binance Honeypot — View →
3461 ElonSpace BSC — binance Honeypot — View →
3462 MoonDeFi — binance Honeypot — View →
3463 DeversiFi Token — ethereum Honeypot — View →
3464 SafeAndromeda — binance Honeypot — View →
3465 Polkamon.com — ethereum Honeypot — View →
3466 SafeMcDc — binance Honeypot — View →
3467 unimex — binance Honeypot — View →
3468 Gundam Finance BSC — binance Honeypot — View →
3469 MirrorFinance — binance Honeypot — View →
3470 SafeGate — binance Honeypot — View →
3471 xrpmoon — binance Honeypot — View →
3472 SafePaid — binance Honeypot — View →
3473 EXEEDME.FINANCE — binance Honeypot — View →
3474 SafeHubble — binance Honeypot — View →
3475 DREAMSWAP — binance Honeypot — View →
3476 SpaceDEFI — binance Honeypot — View →
3477 OMNI — binance Honeypot — View →
3478 REFERRALPROJECT.NETWORK — binance Honeypot — View →
3479 Tripod Dao — binance Honeypot — View →
3480 LinkPool — ethereum Honeypot — View →
3481 SafeVice — binance Honeypot — View →
3482 HighQuality Swap — binance Honeypot — View →
3483 SafeShill — binance Honeypot — View →
3484 SafeSound — binance Honeypot — View →
3485 SafeDelore3n — binance Honeypot — View →
3486 Convergence Protocol — ethereum Honeypot — View →
3487 SafeCow — binance Honeypot — View →
3488 MetaChain — binance Honeypot — View →
3489 Stonedefi.io — ethereum Honeypot — View →
3490 ApplePie — binance Honeypot — View →
3491 Nintendo Protocol — binance Honeypot — View →
3492 Hoge Finance v2 — binance Honeypot — View →
3493 MOON FARM — binance Honeypot — View →
3494 SafeSushi — binance Honeypot — View →
3495 PROTOCOL PENA — binance Honeypot — View →
3496 Uniswap-V3 — ethereum Honeypot — View →
3497 Boson Protocol — ethereum Honeypot — View →
3498 Dextrarius Token — binance Honeypot — View →
3499 Yield Goku Token — binance Honeypot — View →
3500 Hoe Coin BSC — 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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