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 2701-2750 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2701 RocketBanana — binance Honeypot — View →
2702 BlueOrigin — binance Honeypot — View →
2703 Ethereum Apex — ethereum Honeypot — View →
2704 Iron Mars — binance Honeypot — View →
2705 One — binance Honeypot — View →
2706 NFTbs — binance Honeypot — View →
2707 WenMoon — binance Honeypot — View →
2708 Governor Network — ethereum Honeypot — View →
2709 Charged Particles Protocol — ethereum Honeypot — View →
2710 PandoraBSC — binance Honeypot — View →
2711 NYAN INU — ethereum Honeypot — View →
2712 EA Sports Token — ethereum Honeypot — View →
2713 MarsRocket — binance Honeypot — View →
2714 GBull — binance Honeypot — View →
2715 Ethereum SHIBA — ethereum Honeypot — View →
2716 STARTFI — binance Honeypot — View →
2717 Forest token — binance Honeypot — View →
2718 dogejavu.com — binance Honeypot — View →
2719 Ethereum 2.0 — ethereum Honeypot — View →
2720 Moonpad — binance Honeypot — View →
2721 NINJA CAT — binance Honeypot — View →
2722 FudderElon — binance Honeypot — View →
2723 SpheriumFinance — binance Honeypot — View →
2724 DuckCoin — binance Honeypot — View →
2725 PhutureProtocol — binance Honeypot — View →
2726 dmoon.finance — ethereum Honeypot — View →
2727 NeuroChain — binance Honeypot — View →
2728 BongWeedCoin — binance Honeypot — View →
2729 1000xCoin — binance Honeypot — View →
2730 Centrifuge — binance Honeypot — View →
2731 Shiba Inu Shih Tzu — binance Honeypot — View →
2732 DogeStar — binance Honeypot — View →
2733 SPACEDOGE — binance Honeypot — View →
2734 BathroomSwap — binance Honeypot — View →
2735 Pigg Token — binance Honeypot — View →
2736 EMACITY TOKEN — binance Honeypot — View →
2737 Freela — ethereum Honeypot — View →
2738 Coffee Shop DAO — ethereum Honeypot — View →
2739 SafeTed — binance Honeypot — View →
2740 Glitch — binance Honeypot — View →
2741 SkyRocket — binance Honeypot — View →
2742 GAMESTOP — binance Honeypot — View →
2743 Melon Musk — binance Honeypot — View →
2744 Squirtle — binance Honeypot — View →
2745 FUTURE — binance Honeypot — View →
2746 Stratos — binance Honeypot — View →
2747 PI NETWORK TOKEN — binance Honeypot — View →
2748 KING TRUMP — binance Honeypot — View →
2749 New Deep Thots — binance Honeypot — View →
2750 Telcoin — 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.

Learn to Prevent High-Impact Smart Contract Hacks

$91B+ lost to preventable vulnerabilities. Master real exploit patterns and defense techniques with hands-on Web3 security training.

Start Free Trial → View Full Course