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Crypto hacks & exploits database
Crypto hacks are scattered across blog posts, Twitter threads and incident reports — hard to analyze, impossible to filter. For security research, audits, journalism, insurance and risk modelling, you need them as structured data: amount lost, chain, attack technique, and whether the funds came back.
What the data covers
A complete incident database lets you answer real questions instead of guessing: which attack techniques cause the biggest losses, whether bridges are still the weakest link, how often stolen funds are returned, and how exploit volume trends year over year. Each incident carries:
- Amount lost (USD) and date.
- Chain(s) involved and target type (DeFi, CEX, bridge, wallet, gaming…).
- Technique — private-key compromise, reentrancy, flash-loan, phishing, access control…
- Classification, a bridge-hack flag and whether funds were returned.
Get 540+ incidents as clean data
foXLabs publishes the Crypto Hacks Database on Apify — built on DefiLlama's free hacks dataset:
- 540+ incidents, filterable by date, minimum amount, chain, technique, classification and bridge-hack.
- Sort by most recent or biggest loss; export to JSON, CSV or Excel.
- No API key, no proxy — one request per run, schedulable for a live dataset.
Get the whole exploit dataset. 540+ crypto hacks — amount, chain, technique, bridge flag and returned funds — as clean, filterable CSV/Excel rows.
Run Crypto Hacks Database on Apify →Frequently asked questions
What was the biggest crypto hack?
By dollar value, record incidents include the LuBian Bitcoin private-key compromise (~$3.5B), the 2025 Bybit exploit (~$1.4B) and the Ronin Bridge hack (~$624M). Bridges and private-key compromises drive many of the largest losses.
Can I get this in a spreadsheet?
Yes — the actor exports every incident as flat CSV/Excel/JSON rows, so you can sort, filter and pivot the whole dataset without scraping blog posts.