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 3251-3300 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
3251 SuperTesla — binance Honeypot — View →
3252 Uga Uga — binance Honeypot — View →
3253 Lunar Eclipse — binance Honeypot Access Control Attacks View →
3254 ECOMI — binance Honeypot — View →
3255 EasyFinance — binance Honeypot — View →
3256 RAMBBO Finance — binance Honeypot — View →
3257 MOONSEASON — binance Honeypot — View →
3258 TITANMOON — binance Honeypot — View →
3259 CaptainJackFans — binance Honeypot — View →
3260 Bongo Cat — binance Honeypot — View →
3261 SAFE CHARIZARD — binance Honeypot — View →
3262 HYPERIONMOON — binance Honeypot — View →
3263 Glacier — binance Honeypot — View →
3264 MoonQueen — binance Honeypot — View →
3265 MickeyMars — binance Honeypot — View →
3266 Enceladus — binance Honeypot — View →
3267 Polkasol — ethereum Honeypot — View →
3268 Dopex Governance Token — ethereum Honeypot — View →
3269 SAFEMOUSE — binance Honeypot — View →
3270 FairSafeApe — binance Honeypot — View →
3271 SupremeBSCmoon — binance Honeypot — View →
3272 eGRID — binance Honeypot — View →
3273 Zion — binance Honeypot — View →
3274 YellowRoad — ethereum Honeypot — View →
3275 Genesis Shards — ethereum Honeypot — View →
3276 Polkadex — ethereum Honeypot — View →
3277 EPNS Token — ethereum Honeypot — View →
3278 Alphr.Finance — ethereum Honeypot — View →
3279 MOONSUN — binance Honeypot — View →
3280 MoonBanana — binance Honeypot — View →
3281 FractionalArt — ethereum Honeypot — View →
3282 SAFEORACLE — binance Honeypot — View →
3283 Matterhorn — binance Honeypot — View →
3284 Himalaya — binance Honeypot — View →
3285 Elon Rocket — binance Honeypot — View →
3286 Horny Farm — ethereum Honeypot — View →
3287 BSCx500 — binance Honeypot — View →
3288 Betelgeuse — binance Honeypot — View →
3289 Wazir Farm — ethereum Honeypot — View →
3290 GODZILLA — binance Honeypot — View →
3291 Planet Nine — binance Honeypot — View →
3292 Space Typhoon — binance Honeypot — View →
3293 Yoda Token — binance Honeypot — View →
3294 Clover.finance — ethereum Honeypot — View →
3295 Safe Gravity — binance Honeypot — View →
3296 MoonTerra — binance Honeypot — View →
3297 Crowny.io — ethereum Honeypot — View →
3298 Southern Cross — binance Honeypot — View →
3299 FAPE — binance Honeypot — View →
3300 Sunset — 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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