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

Jump to full leaderboard
  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 2451-2500 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2451 Cupcake Defi — binance Rugpull — View →
2452 Farm Ninja — polygon Rugpull — View →
2453 Mew Two — binance Rugpull — View →
2454 Mew — binance Rugpull — View →
2455 RoboBuddy — binance Rugpull — View →
2456 rabbitfarm.finance — binance Rugpull — View →
2457 Cat.Moji Swap — binance Rugpull — View →
2458 MewMegaX Token — binance Rugpull — View →
2459 WeedSwap — polygon Rugpull — View →
2460 NutPie — binance Rugpull — View →
2461 Flamingo Lagoon — polygon Rugpull — View →
2462 DonaldSwap — binance Rugpull — View →
2463 koroswap finance — binance Rugpull — View →
2464 Hamster Farm — polygon Rugpull — View →
2465 PolyHorse Finance — polygon Rugpull — View →
2466 Hawk Finance — polygon Rugpull — View →
2467 PolySphere — polygon Rugpull — View →
2468 Starfall Finance — binance Rugpull — View →
2469 XAN Swap — polygon Rugpull — View →
2470 PitbullSwap — binance Rugpull — View →
2471 LanternSwap — polygon Rugpull — View →
2472 RooSwap — binance Rugpull — View →
2473 Sashimi swap — polygon Rugpull — View →
2474 E-Coin — binance Honeypot — View →
2475 Blazar Finance — polygon Rugpull — View →
2476 Area51 DeFi — binance Rugpull — View →
2477 Duck Finance — polygon Rugpull — View →
2478 CookieSwap Layer 2 — binance Rugpull — View →
2479 PoliEx Swap — binance Rugpull — View →
2480 KoalaDefi — polygon Rugpull — View →
2481 TamagochiSwap — polygon Rugpull — View →
2482 HermitSwap — binance Rugpull — View →
2483 Carousel Finance — polygon Rugpull — View →
2484 CatgeSwap — binance Rugpull — View →
2485 CarolinaReaper Finance — binance Rugpull — View →
2486 Royalflush Farm — polygon Rugpull — View →
2487 The Apollo Project — binance Rugpull — View →
2488 NekoCat — binance Rugpull — View →
2489 Frog Farm — binance Rugpull — View →
2490 Hotdog Cart Finance — binance Rugpull — View →
2491 Hippolygon — polygon Rugpull — View →
2492 OVNI Finance — binance Rugpull — View →
2493 LavaSwap — binance Rugpull — View →
2494 Jester DeFi — binance Rugpull — View →
2495 Panda Matic — polygon Rugpull — View →
2496 Helios.Cash — polygon Rugpull — View →
2497 Astro Bunny Finance — polygon Rugpull — View →
2498 Polywater — polygon Rugpull — View →
2499 BEESwap — binance Rugpull — View →
2500 Night Wolf — binance Rugpull — 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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