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 2951-3000 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2951 KingCZ — binance Honeypot — View →
2952 RUBYTOKEN — binance Honeypot — View →
2953 Wilder World — ethereum Honeypot — View →
2954 INTERGALACTIC — binance Honeypot — View →
2955 LevelUpX — binance Honeypot — View →
2956 Mozik.cc — ethereum Honeypot — View →
2957 WAKANDATOKEN — binance Honeypot — View →
2958 SafeGalaxy — binance Honeypot — View →
2959 Gelato.network — ethereum Honeypot — View →
2960 1 Million Token — binance Honeypot — View →
2961 Corgi Coin — ethereum Honeypot — View →
2962 CargoX — binance Honeypot — View →
2963 Anchor Protocol — binance Honeypot — View →
2964 Oiler Network — ethereum Honeypot — View →
2965 Ispolink Token — ethereum Honeypot — View →
2966 ELON MOONSHIP — binance Honeypot — View →
2967 TheCatToken.com — binance Honeypot — View →
2968 Dragon.Swap — binance Honeypot — View →
2969 dogemars — binance Honeypot — View →
2970 DEFIX — binance Honeypot — View →
2971 Arbitrade — binance Honeypot — View →
2972 EternityProtcol — binance Honeypot — View →
2973 Luminescence — binance Honeypot — View →
2974 d-fund.io — ethereum Honeypot — View →
2975 MASKSWAP — binance Honeypot — View →
2976 JaguarMOON — binance Honeypot — View →
2977 RockHard — binance Honeypot — —
2978 TheMountain — binance Honeypot — View →
2979 DeFiner — binance Honeypot — View →
2980 Mercurial Finance — ethereum Honeypot — View →
2981 Skem.finance — ethereum Honeypot — View →
2982 Falcon 9 SpaceX Token — ethereum Honeypot — View →
2983 TradeStars — ethereum Honeypot — View →
2984 TAXITOKEN — binance Honeypot — View →
2985 Moon Lambo — ethereum Honeypot — View →
2986 SpaceShuttle — binance Honeypot — View →
2987 ELON ORBIT — binance Honeypot — View →
2988 Starlink Mission Mars — ethereum Honeypot — View →
2989 BattlePets — binance Honeypot — View →
2990 SafeMarket Token — binance Honeypot — View →
2991 BondAppetit — binance Honeypot — View →
2992 LOCGame — ethereum Honeypot — View →
2993 PIethori — ethereum Honeypot — View →
2994 Dfyn.Network — ethereum Honeypot — View →
2995 India Pale Ale — binance Honeypot — View →
2996 Rocket Man — binance Honeypot — View →
2997 PHOENIXTOKEN — binance Honeypot — View →
2998 PAIRS — binance Honeypot — View →
2999 INFINITE SPACE — binance Honeypot — View →
3000 NyanCat — 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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