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 3201-3250 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
3201 Apron Network — ethereum Honeypot — View →
3202 icecreamland.org — binance Honeypot — View →
3203 Icy Comet — binance Honeypot — View →
3204 OccamFi — ethereum Honeypot — View →
3205 GoldMoon — binance Honeypot — View →
3206 MOONRIKVIP — binance Honeypot — View →
3207 MOONTOKEN — binance Honeypot — View →
3208 MoonElevator — binance Honeypot — View →
3209 SafePancake — binance Honeypot — View →
3210 Elon Protocol — binance Honeypot — View →
3211 FairCat — binance Honeypot — View →
3212 fairy.finance — binance Honeypot — View →
3213 Safe Nintendo — binance Honeypot — View →
3214 Star Shot — binance Honeypot — View →
3215 SUNMOON — binance Honeypot — View →
3216 BSCcommunity — binance Honeypot — View →
3217 ThenaMoon — binance Honeypot — View →
3218 Eclipse — binance Honeypot — View →
3219 BirdSwap — ethereum Honeypot — View →
3220 YES Token — binance Honeypot — View →
3221 ProtonFinance — binance Honeypot — View →
3222 SafePlatinum — binance Honeypot — View →
3223 SafeLuxury — binance Honeypot — View →
3224 DATAMOON — binance Honeypot — View →
3225 MarsHistory — binance Honeypot — View →
3226 Deflagration — binance Honeypot — View →
3227 ROCKETSHOT — binance Honeypot — View →
3228 SAFEBORN — binance Honeypot — View →
3229 GangBang — binance Honeypot — View →
3230 SAFEFOOD — binance Honeypot — View →
3231 SafeTechnology — binance Honeypot — View →
3232 MoonFox — binance Honeypot — View →
3233 Garbage Disposal — binance Honeypot — View →
3234 XTRA Protocol — ethereum Honeypot — View →
3235 EUROTRIP — binance Honeypot — View →
3236 Hord.App — ethereum Honeypot — View →
3237 PicaMoon — binance Honeypot — View →
3238 Rocket Vault — ethereum Honeypot — View →
3239 Photosynthesis — binance Honeypot — View →
3240 Dark Comics NFT — ethereum Honeypot — View →
3241 MoonFintech — binance Honeypot — View →
3242 SafeCoinToken — binance Honeypot — View →
3243 Maelstrom — binance Honeypot — View →
3244 Karma — binance Honeypot — View →
3245 JetCombat — binance Honeypot — View →
3246 Meteor — binance Honeypot — View →
3247 BRONZE MOON — binance Honeypot — View →
3248 DRACULA PROJECT — binance Honeypot — View →
3249 Beyond Finance — ethereum Honeypot — View →
3250 FairMoonElon — 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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