CrabInHoney/urlbert-tiny-v6
Feature Extraction • 2.03M • Updated • 525 • 1
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Real-time feed of phishing, crypto drainer, and scam domains detected by PhishDestroy.
Updated hourly from GitHub.
| Metric | Count |
|---|---|
| Total Domains | 214,632 |
| DNS Active | 131,914 |
| Content Active | 89,447 |
| Dead Domains | 82,684 |
| Community Blocklist | 1,109,387 |
| Added Today | 4 |
| Added This Week | 4 |
Last updated: 2026-10-03 08:30 UTC
| File | Description |
|---|---|
list.json |
Full domain list (JSON array) |
domains.txt |
Plain text, one domain per line |
urls.txt |
Full URLs with protocol |
domains.csv |
ML-ready CSV with metadata |
dns/active_domains.json |
DNS-verified active domains |
dns/content_active.json |
Domains with verified malicious content |
dns/dead_domains.json |
Inactive/dead domains |
dns/today_added.json |
New domains added today |
dns/week_added.json |
New domains this week |
community/blocklist.json |
Community-submitted blocklist |
community/live_blocklist.json |
Community verified live |
from datasets import load_dataset
# Load full dataset
ds = load_dataset("phishdestroy/destroylist")
# Or load specific file
import json
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="phishdestroy/destroylist",
filename="list.json",
repo_type="dataset"
)
with open(path) as f:
domains = json.load(f)
import pandas as pd
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="phishdestroy/destroylist",
filename="domains.csv",
repo_type="dataset"
)
df = pd.read_csv(path)
print(df.head())
# Download domains list
curl -L https://huggingface.co/datasets/phishdestroy/destroylist/resolve/main/domains.txt
# Download as JSON
curl -L https://huggingface.co/datasets/phishdestroy/destroylist/resolve/main/list.json
MIT License - Free for commercial and non-commercial use.
@dataset{phishdestroy_blocklist,
title = {PhishDestroy Blocklist},
author = {PhishDestroy Team},
year = {2024},
url = {https://huggingface.co/datasets/phishdestroy/destroylist}
}