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 2351-2400 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2351 Coinbase — - Session Key Compromised — —
2352 Hallowenswap — - Rugpull — View →
2353 cArbFarm — - Rugpull — View →
2354 ChocSwap.finance — - Rugpull — View →
2355 PolyGladiatorToken — - Rugpull — View →
2356 DiamondBalls — binance Honeypot — View →
2357 Lizard finance — binance Honeypot — View →
2358 Compound V2 — ethereum Reward index initialization error Arithmetic Overflow & Underflow Attacks View →
2359 MagicToken — - Rugpull — View →
2360 PolyMountain — - Rugpull — View →
2361 PolyHomerToken — - Rugpull — View →
2362 BinanceInu.Bep20 — binance Honeypot — View →
2363 CoffeeSwap Arbitrum — - Rugpull — View →
2364 MEDUZA TOKEN — binance Honeypot — View →
2365 Playcent — binance Honeypot — View →
2366 sixpackfinance.org — binance Honeypot — View →
2367 Mask.io — ethereum Honeypot — View →
2368 ShibaSwap — binance Honeypot — View →
2369 InfinitySwap — binance Honeypot — View →
2370 Flamingo Boys — ethereum Honeypot — View →
2371 HappyMoon — binance Honeypot — View →
2372 MoonGold — binance Honeypot — View →
2373 VibeSwap — binance Honeypot — View →
2374 PlugToken — - Rugpull — View →
2375 PenginFinance Token — - Rugpull — View →
2376 INTERSWAP — - Rugpull — View →
2377 GullFinance Token — - Rugpull — View →
2378 MikuMilk Finance — - Rugpull — View →
2379 BloodySwap — - Rugpull — View →
2380 Earth DeFi Avalanche — - Rugpull — View →
2381 GulliFinance — - Rugpull — View →
2382 Bichon — binance Honeypot — View →
2383 darwin.finance — ethereum Honeypot — View →
2384 World of Winners — ethereum Honeypot — View →
2385 Treasure Chest — ethereum Honeypot — View →
2386 Koala Forest — ethereum Honeypot — View →
2387 ORAO Network — ethereum Honeypot — View →
2388 PLANET — ethereum Honeypot — View →
2389 Dragon Farm — ethereum Honeypot — View →
2390 TrollFace — - Rugpull — View →
2391 Fear NFTs — binance Honeypot — View →
2392 Arsenal FC Fan Token — binance Honeypot — View →
2393 MechaGodzilla — binance Honeypot — View →
2394 Bezop — ethereum Rugpull — View →
2395 EOS — ethereum Rugpull — View →
2396 Centra — ethereum Rugpull — View →
2397 Pitbull Classic — binance Honeypot — View →
2398 MentalHealth — binance Honeypot — View →
2399 Blue Lightning — binance Honeypot — —
2400 ElonSperm — 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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