Security Scan Report: coincommunity-gz.netlify.app

Redirected to:
https://coincommunity-gz.netlify.app/
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Submitted: Jul 7, 2026, 7:50:10 PMCompleted: Jul 7, 2026, 7:50:33 PMpubliccompleted
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Summary

This website contacted 10 IPs in 2 countries across 9 domains to perform 73 HTTP transactions. The main domain is coincommunity-gz.netlify.app and was registered NaN years ago.

Submitted URL: http://coincommunity-gz.netlify.app

Effective URL: https://coincommunity-gz.netlify.app/Redirected

AI Security Verdict

Low Risk

Confidence: 78%

2
Risk Score

The site shows no malicious forms, IoCs, or malware; only an unknown subdomain age, resulting in a low risk rating.

Risk Factors
Unknown subdomain creation date on a hosting platform
Safety Factors
Absence of malicious forms or credential exfiltration
No malicious JavaScript or external IoC links
No IDS or Safe Browsing alerts
Domain age information unavailable

Details

Page Title

$CUM · cummunity

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

social media network

(57%)

Domain Information

The domain 'coincommunity-gz.netlify.app' uses the application-focused generic top-level domain (.app) with subdomain 'coincommunity-gz'. The registrable portion 'netlify' spans 7 characters with two vowels and five consonants. Splitting it apart reveals three words: net, li, fy. Average segment length settles at two characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of http://coincommunity-gz.netlify.app

Page Load Overview

3.39s
Total Load Time
73
HTTP Requests
9
Domains
235 KB
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:80%
Script:Latin
Direction:ltr

Detection Details

Language Code:en
Detection Confidence:80%
Script Type:Latin
HTML Lang Attribute:en
Text Length:5,237 chars
Detector Agreement:100%

Website Classification

Primary Category

social media network57% confidence
Type: static
Method: ml+structural+ocr_tiebreaker

All Detected Categories

social media network
57%
documentation technical
53%
forum community discussion
49%
adult content
49%
cryptocurrency blockchain
45%

Detected Features

Articles
OG: website

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
1035.157.26.135Aws · CLOUDFrankfurt am Main, Hesse, Germany
AS16509Amazon.com, Inc.
73.174.46.71Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
763.176.8.218Aws · CLOUDFrankfurt am Main, Hesse, Germany
AS16509Amazon.com, Inc.
7104.18.37.127Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
7142.251.20.95Google · CDNUnited States
AS15169Google LLC
7172.64.150.129Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
7104.26.9.44Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
7104.26.1.43Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
7142.251.20.94Google · CDNUnited States
AS15169Google LLC
718.245.60.114Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
7310--

Detected Technologies1

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T18224C8F263B0217F1E1BC7E5FE672B5930975674EEC204C8C69886E45AE9CF2C9098C5

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

6144:X4cbgmRboz9uZIPeH+wfwOIiVvXMk4mHjikvnx8hp:ocbgmRUz9uZIPeewfwOIiVvXMk4mHjib

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:223168:uAG0wEBIBiCBIYhNoYEllCghIAWAgGAci3yjAcYCqcpiibAoMBYABEkugJrJIJggiqgCPjJMAhAA0BGDkABIAlMAuEaDC6hR

These hashes enable detection of similar websites and malware variants by comparing content similarity even when exact matches aren't found.

Image Hashes

Perceptual Hashes

Average Hash:7c8f8f8fefe7e7cf
Perceptual Hash:bc1cc7c35858c3c7
Difference Hash:d42b393a0a4e0e0a
Wavelet Hash:3c878d878de1a38d
Color Hash:#79d292

Other Hashes

Crop Resistant:d42b393a0a4e0e0a

Scan History

Scan history not available

Unable to load historical scan data