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 2901-2950 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2901 JizzUs — binance Honeypot — View →
2902 Mia Khalifa — binance Honeypot — View →
2903 SHIBAMOON — binance Honeypot — View →
2904 FORESTTOKEN — binance Honeypot — View →
2905 TESLA — binance Honeypot — View →
2906 THANKS ELON — binance Honeypot — View →
2907 DoucheCoin — binance Honeypot — View →
2908 Laugh Out Loud — binance Honeypot — View →
2909 Chava — binance Honeypot — View →
2910 Capellix — binance Honeypot — View →
2911 FUN TOKEN — binance Honeypot — View →
2912 WeSpace — binance Honeypot — View →
2913 NoCapCoin — binance Honeypot — View →
2914 DoubtFire — binance Honeypot — View →
2915 ElonGate — binance Honeypot — View →
2916 PIDGEY — binance Honeypot — View →
2917 French Shiba — ethereum Honeypot — View →
2918 Inu Doge — ethereum Honeypot — View →
2919 HONEYROCKET — binance Honeypot — View →
2920 SpaceXDoge1 — binance Honeypot — View →
2921 LABRADOR — binance Honeypot — View →
2922 Winnie-the-Pot — binance Honeypot — View →
2923 AlaskanDoge — binance Honeypot — View →
2924 Vitalik Shiba Buterin — ethereum Honeypot — View →
2925 Binance Fail Chain — binance Honeypot — View →
2926 victoriavr — binance Honeypot — View →
2927 InjectiveProtocol — binance Honeypot — View →
2928 Kishu Doge Inu — ethereum Honeypot — View →
2929 SaveFlipper — binance Honeypot — View →
2930 Torio — binance Honeypot — —
2931 CROT Finance — binance Honeypot — View →
2932 ProStarter — ethereum Honeypot — View →
2933 NOZOMI COIN — binance Honeypot — View →
2934 The Internet Computer — ethereum Honeypot — View →
2935 Dogelon — binance Honeypot — View →
2936 HellGate — binance Honeypot — View →
2937 DonaldPump — binance Honeypot — —
2938 XXXRated-NFT — binance Honeypot — View →
2939 HYDRA — binance Honeypot — View →
2940 Meow Moon Cat — ethereum Honeypot — View →
2941 SafeShib — ethereum Honeypot — —
2942 ChickenCoin — binance Honeypot — View →
2943 EchoX — binance Honeypot — View →
2944 Strike Protocol — binance Honeypot — View →
2945 DOGE SNL — binance Honeypot — View →
2946 Kitty Token — ethereum Honeypot — View →
2947 SafeCookie — binance Honeypot — View →
2948 MOON WEED — binance Honeypot — View →
2949 DogeWars — binance Honeypot — View →
2950 Bulldoge — 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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