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 2851-2900 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2851 X1000 ONLY — binance Honeypot — View →
2852 GDRAGON — binance Honeypot — View →
2853 KumaInu — binance Honeypot — View →
2854 GDOGE — binance Honeypot — View →
2855 Alaskan — binance Honeypot — View →
2856 GOATMOON — binance Honeypot — View →
2857 Dasani — binance Honeypot — View →
2858 ASTIR — binance Honeypot — View →
2859 MOMAFINANCE — binance Honeypot — View →
2860 PangeaOceanCleaunUp — binance Honeypot — View →
2861 BIRDE — binance Honeypot — View →
2862 Chunky Inu — ethereum Honeypot — View →
2863 SMOKE TOKEN — binance Honeypot — View →
2864 KNIGHT RIDER — binance Honeypot — View →
2865 HungryWorldBSC — binance Honeypot — View →
2866 DOORToken — binance Honeypot — View →
2867 Lucky Token — binance Honeypot — View →
2868 SolarRocket — binance Honeypot — View →
2869 KingShiba — binance Honeypot — View →
2870 Glossify — binance Honeypot — View →
2871 Up DeFi — ethereum Honeypot — View →
2872 FUCK ENVIRONMENT — binance Honeypot — View →
2873 MoonKind — binance Honeypot — View →
2874 Windswap — binance Honeypot — View →
2875 CHINESE — binance Honeypot — View →
2876 SHIBAFARM — ethereum Honeypot — View →
2877 EAGLE — binance Honeypot — View →
2878 AstroPig — binance Honeypot — View →
2879 WINE TOKEN — binance Honeypot — View →
2880 Foxydoge — binance Honeypot — View →
2881 NoNameNetworks — binance Honeypot — View →
2882 CVL — binance Honeypot — View →
2883 DOGE 2.0 — binance Honeypot — View →
2884 BITCOIN CONSUMPTION — binance Honeypot — View →
2885 BEER COIN — binance Honeypot — View →
2886 t.me/astracoincommunity — binance Honeypot — View →
2887 Ether Shiba — ethereum Honeypot — View →
2888 Bingo Inu — ethereum Honeypot — View →
2889 SHINKY — binance Honeypot — View →
2890 SAD CAT — binance Honeypot — View →
2891 DogeMissile — binance Honeypot — View →
2892 Ultra Safe — binance Honeypot — View →
2893 DogelonMars — binance Honeypot — View →
2894 Cheetah Coin — ethereum Honeypot — View →
2895 LILDOG — binance Honeypot — View →
2896 Patrigio — binance Honeypot — View →
2897 ShinyDoge — binance Honeypot — View →
2898 Glitch-Finance — binance Honeypot — View →
2899 RUBY — binance Honeypot — View →
2900 WATER TOKEN — 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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