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 2601-2650 of 3597 ranked incidents

Largest Web3 incidents by USD amount lost
Rank Project Amount Lost Date Chain Technique Attack Guide Source
2601 ChildDoge — binance Honeypot — View →
2602 Haven Protocol — haven Asset validation and unauthorized minting — View →
2603 Papito — binance Honeypot — View →
2604 Shiba King — binance Honeypot — View →
2605 2Crazy — ethereum Honeypot — View →
2606 RAICHU TOKEN | raichutoken.com — ethereum Honeypot — View →
2607 Shiba Inu Bone — ethereum Honeypot — View →
2608 MoneyWulf — binance Honeypot — View →
2609 PepeRise — binance Honeypot — View →
2610 Knight — binance Honeypot — View →
2611 MoonRise — binance Honeypot — View →
2612 MoonSquad — binance Honeypot — View →
2613 AmpChain — ethereum Honeypot — View →
2614 Player 2 — binance Honeypot — View →
2615 EverRise Token — binance Honeypot — View →
2616 Hex-Mex — ethereum Honeypot — View →
2617 plex — ethereum Honeypot — View →
2618 ZUMBA MARS — binance Honeypot — View →
2619 HEXICO — ethereum Honeypot — View →
2620 LOVELY SHIBA — binance Honeypot — View →
2621 CleanEnergyToken — binance Honeypot — View →
2622 CosmoKitty — binance Honeypot — View →
2623 BitSpawn Token — ethereum Honeypot — View →
2624 Defi Land — ethereum Honeypot — View →
2625 Human Protocol — binance Honeypot — View →
2626 Saiba Neko — binance Honeypot — View →
2627 Beets Farm — polygon Rugpull — View →
2628 AstroDoge — binance Honeypot — View →
2629 DRAGON INU — binance Honeypot — View →
2630 Iron Finance — - Other — View →
2631 Gigachad Kishu — ethereum Honeypot — View →
2632 LordShiba — binance Honeypot — View →
2633 Instadapp — ethereum Honeypot — View →
2634 DOGEMASK — binance Honeypot — View →
2635 Foxography — binance Honeypot — View →
2636 RCums — binance Honeypot — View →
2637 BSCSportsBet — binance Honeypot — View →
2638 SpaceX Token — binance Honeypot — View →
2639 EASYFEEDBACK — binance Honeypot — View →
2640 CocoBee — binance Honeypot — View →
2641 AstroCum — binance Honeypot — View →
2642 MIAU — binance Honeypot — View →
2643 AurataGoldCat — binance Honeypot — View →
2644 OCTOPIX — binance Honeypot — View →
2645 RASIM OZAN — binance Honeypot — View →
2646 VOLCANOS — binance Honeypot — View →
2647 Croco Inu — ethereum Honeypot — View →
2648 ghostshiba.xyz — ethereum Honeypot — View →
2649 Kenshi Inu — ethereum Honeypot — View →
2650 FLOUR FINANCIAL — 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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