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 3051-3100 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
3051 Pumpamental — binance Honeypot — View →
3052 Moon Fox Community — ethereum Honeypot — View →
3053 Atomas — binance Honeypot — View →
3054 Sushi Finance — binance Honeypot — View →
3055 XenonFinance — binance Honeypot — View →
3056 CereNetwork — binance Honeypot — View →
3057 SaitoToken — binance Honeypot — View →
3058 XFai — binance Honeypot — View →
3059 MW — binance Honeypot — View →
3060 DerioxDefi — binance Honeypot — View →
3061 Infinity Cat — ethereum Honeypot — View →
3062 Atomic Bomb — binance Honeypot — View →
3063 RocketElon — binance Honeypot — View →
3064 Rao Token — ethereum Honeypot — View →
3065 Safecare — binance Honeypot — View →
3066 SPACEPENGUIN — binance Honeypot — View →
3067 MoonPay — binance Honeypot — View →
3068 TACHYON — binance Honeypot — View →
3069 Instaswap — binance Honeypot — View →
3070 Da Vinci Coin — ethereum Honeypot — View →
3071 Counter Strike Community — ethereum Honeypot — View →
3072 DogeMother — binance Honeypot — View →
3073 Larafinance — binance Honeypot — View →
3074 NeutrinoProtocol — binance Honeypot — View →
3075 LUNACAT — binance Honeypot — View →
3076 EpsilonFinance — binance Honeypot — View →
3077 ValueNFT — binance Honeypot — View →
3078 Pink Panther Protocol — ethereum Honeypot — View →
3079 SmartFinance — binance Honeypot — View →
3080 ElonDash — ethereum Honeypot — View →
3081 DEXTrust.Finance — ethereum Honeypot — View →
3082 World Of Warcraft — ethereum Honeypot — View →
3083 Yolo Token — binance Honeypot — View →
3084 Alphr Finance — ethereum Honeypot — View →
3085 Bull — binance Honeypot — View →
3086 SHIBA PROTOCOL — binance Honeypot — View →
3087 UniFarm — ethereum Honeypot — View →
3088 BucksCake — ethereum Honeypot — View →
3089 Smoothy Finance — ethereum Honeypot — View →
3090 HoneyMoon — binance Honeypot — View →
3091 Monkey Token — ethereum Honeypot — View →
3092 TheAPIS — binance Honeypot — View →
3093 Flare — binance Honeypot — View →
3094 LunarLaunchProject — binance Honeypot — View →
3095 Zevia — binance Honeypot — View →
3096 BitLink — binance Honeypot — View →
3097 NftFinal — binance Honeypot — View →
3098 The Crypto Prophecies — ethereum Honeypot — View →
3099 Mochi.market — ethereum Honeypot — View →
3100 SuperMoon Token — ethereum 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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