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 3001-3050 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
3001 Tibetan Mastiff Protocol — ethereum Honeypot — View →
3002 SpaceX Network — ethereum Honeypot — View →
3003 Meebits — ethereum Honeypot — View →
3004 PORNHUB — binance Honeypot — View →
3005 XDAICHAIN — binance Honeypot — View →
3006 Crypto Puzzles — ethereum Honeypot — View →
3007 Polylastic — ethereum Honeypot — View →
3008 MoonSquirrel — binance Honeypot — View →
3009 TrustPad — ethereum Honeypot — View →
3010 LODE Finance — ethereum Honeypot — View →
3011 The Doge Space X — ethereum Honeypot — View →
3012 KimJongMoon — binance Honeypot — View →
3013 Chow Inu — ethereum Honeypot — View →
3014 SPACE GEM — binance Honeypot — View →
3015 ACID RAIN — binance Honeypot — View →
3016 Enlighten — binance Honeypot — View →
3017 WhiteNova — binance Honeypot — View →
3018 Doggy Token — ethereum Honeypot — View →
3019 PurpleGoat — binance Honeypot — View →
3020 Stability Protocol — binance Honeypot — View →
3021 ForTheWin — binance Honeypot — View →
3022 Dogefather — binance Honeypot — View →
3023 LamboToken — binance Honeypot — View →
3024 Dojecoin — binance Honeypot — View →
3025 NFTify.Network — ethereum Honeypot — View →
3026 CrossPad — ethereum Honeypot — View →
3027 makiswap.com — ethereum Honeypot — View →
3028 Sienna.Network — ethereum Honeypot — View →
3029 Bring.finance — ethereum Honeypot — View →
3030 Umbrella — binance Honeypot — View →
3031 The Dragon Father — ethereum Honeypot — View →
3032 London X Protocol — ethereum Honeypot — View →
3033 Oportunity Token — ethereum Honeypot — View →
3034 SUPERCYCLE — binance Honeypot — View →
3035 SolstarterToken — ethereum Honeypot — View →
3036 DOGE ROCKET — binance Honeypot — View →
3037 SafeCash — binance Honeypot — View →
3038 Mother Of Dragon — ethereum Honeypot — View →
3039 GrumpyDoge — ethereum Honeypot — View →
3040 Tentrix — binance Honeypot — View →
3041 QixiSwap — binance Honeypot — View →
3042 Covalent — ethereum Honeypot — View →
3043 FujiProtocol — binance Honeypot — View →
3044 NAOS Finance — ethereum Honeypot — View →
3045 Media Network — ethereum Honeypot — View →
3046 Horse Protocol — ethereum Honeypot — View →
3047 DOGE PARTY — binance Honeypot — View →
3048 Librex — binance Honeypot — View →
3049 IPX — binance Honeypot — View →
3050 Diamond Hands Elon — 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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