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Terra Classic
$40.00B
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.
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$101B+
Total Value Lost
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3300
Incidents Tracked
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74
Chains Affected
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80
$100M+ Incidents
Hall of Infamy
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Top 10 Biggest Hacks
Breakdown
Where the Largest Losses Concentrate
Top Chains by Losses
Top Techniques by Losses
All Incidents
Full Leaderboard
Showing 2701–2750 of 3300 ranked incidents
| Rank | Project | Amount Lost | Date | Chain | Technique | Attack Guide | Source |
|---|---|---|---|---|---|---|---|
| 2701 | DeFiner | — | binance | Honeypot | — | View → | |
| 2702 | Mercurial Finance | — | ethereum | Honeypot | — | View → | |
| 2703 | Skem.finance | — | ethereum | Honeypot | — | View → | |
| 2704 | Falcon 9 SpaceX Token | — | ethereum | Honeypot | — | View → | |
| 2705 | TradeStars | — | ethereum | Honeypot | — | View → | |
| 2706 | TAXITOKEN | — | binance | Honeypot | — | View → | |
| 2707 | Moon Lambo | — | ethereum | Honeypot | — | View → | |
| 2708 | SpaceShuttle | — | binance | Honeypot | — | View → | |
| 2709 | ELON ORBIT | — | binance | Honeypot | — | View → | |
| 2710 | Starlink Mission Mars | — | ethereum | Honeypot | — | View → | |
| 2711 | BattlePets | — | binance | Honeypot | — | View → | |
| 2712 | SafeMarket Token | — | binance | Honeypot | — | View → | |
| 2713 | BondAppetit | — | binance | Honeypot | — | View → | |
| 2714 | LOCGame | — | ethereum | Honeypot | — | View → | |
| 2715 | PIethori | — | ethereum | Honeypot | — | View → | |
| 2716 | Dfyn.Network | — | ethereum | Honeypot | — | View → | |
| 2717 | India Pale Ale | — | binance | Honeypot | — | View → | |
| 2718 | Rocket Man | — | binance | Honeypot | — | View → | |
| 2719 | PHOENIXTOKEN | — | binance | Honeypot | — | View → | |
| 2720 | PAIRS | — | binance | Honeypot | — | View → | |
| 2721 | INFINITE SPACE | — | binance | Honeypot | — | View → | |
| 2722 | NyanCat | — | binance | Honeypot | — | View → | |
| 2723 | Tibetan Mastiff Protocol | — | ethereum | Honeypot | — | View → | |
| 2724 | SpaceX Network | — | ethereum | Honeypot | — | View → | |
| 2725 | Meebits | — | ethereum | Honeypot | — | View → | |
| 2726 | PORNHUB | — | binance | Honeypot | — | View → | |
| 2727 | XDAICHAIN | — | binance | Honeypot | — | View → | |
| 2728 | Crypto Puzzles | — | ethereum | Honeypot | — | View → | |
| 2729 | Polylastic | — | ethereum | Honeypot | — | View → | |
| 2730 | MoonSquirrel | — | binance | Honeypot | — | View → | |
| 2731 | TrustPad | — | ethereum | Honeypot | — | View → | |
| 2732 | LODE Finance | — | ethereum | Honeypot | — | View → | |
| 2733 | The Doge Space X | — | ethereum | Honeypot | — | View → | |
| 2734 | KimJongMoon | — | binance | Honeypot | — | View → | |
| 2735 | Chow Inu | — | ethereum | Honeypot | — | View → | |
| 2736 | SPACE GEM | — | binance | Honeypot | — | View → | |
| 2737 | ACID RAIN | — | binance | Honeypot | — | View → | |
| 2738 | Enlighten | — | binance | Honeypot | — | View → | |
| 2739 | WhiteNova | — | binance | Honeypot | — | View → | |
| 2740 | Doggy Token | — | ethereum | Honeypot | — | View → | |
| 2741 | PurpleGoat | — | binance | Honeypot | — | View → | |
| 2742 | Stability Protocol | — | binance | Honeypot | — | View → | |
| 2743 | ForTheWin | — | binance | Honeypot | — | View → | |
| 2744 | Dogefather | — | binance | Honeypot | — | View → | |
| 2745 | LamboToken | — | binance | Honeypot | — | View → | |
| 2746 | Dojecoin | — | binance | Honeypot | — | View → | |
| 2747 | NFTify.Network | — | ethereum | Honeypot | — | View → | |
| 2748 | CrossPad | — | ethereum | Honeypot | — | View → | |
| 2749 | makiswap.com | — | ethereum | Honeypot | — | View → | |
| 2750 | Sienna.Network | — | ethereum | Honeypot | — | View → |
Data sourced from DefiLlama & SunWeb3Sec/DeFiHackLabs . Thank you for keeping Web3 security data open.
Context
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.
Common Questions
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.
Learn to Prevent High-Impact Smart Contract Hacks
$101B+ lost to preventable vulnerabilities. Master real exploit patterns and defense techniques with hands-on Web3 security training.