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 3151-3200 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
3151 CUMROCKET CRYPTO — binance Honeypot — View →
3152 Malamute Token — ethereum Honeypot — View →
3153 MOAR Finance — ethereum Honeypot — View →
3154 Golden Inu Token — ethereum Honeypot — View →
3155 Spitz Inu Token — ethereum Honeypot — View →
3156 Event Horizon — binance Honeypot — View →
3157 Rocket Inu — ethereum Honeypot — View →
3158 TESLAFARM.FINANCE — binance Honeypot — View →
3159 MarsStop — binance Honeypot — View →
3160 Ursa Major — binance Honeypot — View →
3161 ChowChow Token — ethereum Honeypot — View →
3162 Kleeka Inu — binance Honeypot — View →
3163 Payship.Finance — ethereum Honeypot — View →
3164 Blowfish Token — ethereum Honeypot — View →
3165 Singularity — binance Honeypot — View →
3166 unifarm.co — ethereum Honeypot — View →
3167 Plethori — ethereum Honeypot — View →
3168 Dehive.finance — ethereum Honeypot — View →
3169 Starshot — binance Honeypot — View →
3170 Pug Token — ethereum Honeypot — View →
3171 DirtyPig — binance Honeypot — View →
3172 Nova — binance Honeypot — View →
3173 Magnet Attraction — binance Honeypot — View →
3174 Comet — binance Honeypot — View →
3175 Coordinape — ethereum Honeypot — View →
3176 Gamma Ray Burst — binance Honeypot — View →
3177 Dogzilla — ethereum Honeypot — View →
3178 Hound Token — ethereum Honeypot — View →
3179 FireBunny — binance Honeypot — View →
3180 Collapsar — binance Honeypot — View →
3181 Moon Send — binance Honeypot — View →
3182 ELONSAFE — binance Honeypot — View →
3183 Einstein Rosen Bridge — binance Honeypot — View →
3184 Lucky Charm — binance Honeypot — View →
3185 Franklin — ethereum Honeypot — View →
3186 MOONBERRY — binance Honeypot — View →
3187 MoonWait — binance Honeypot — View →
3188 Mechadog — ethereum Honeypot — View →
3189 Volcano — binance Honeypot — View →
3190 Adastarter — ethereum Honeypot — View →
3191 CYCLOPS FINANCE TREASURE — ethereum Honeypot — View →
3192 PigMOON — binance Honeypot — View →
3193 Disbalancer — ethereum Honeypot — View →
3194 MOONCALENDA — binance Honeypot — View →
3195 SAFESHIBA — binance Honeypot — View →
3196 www.SafePussy.tech — binance Honeypot — View →
3197 Majestic Deer — binance Honeypot — View →
3198 Snow — binance Honeypot — View →
3199 Kianite.co — ethereum Honeypot — View →
3200 LURNA — 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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