Security Scan Report: goto1.72.chat

Redirected to:
https://2026-11-06.urldance.com/en?aaamu.top
Site favicon
Submitted: Completed: publiccompleted
This website contacted 10 IPs in 3 countries across 7 domains to perform 15 HTTP transactions. The main domain is 72.chat and was registered 3 years 11 months ago.

Submitted URL: https://goto1.72.chat/8d/?aaamu.top

Effective URL:
https://2026-11-06.urldance.com/en?aaamu.top
Redirected

The Cisco Umbrella rank of the primary domain is #724,238 of the top 1 million websites

AI Security Verdict

High Risk

Confidence: 76%

8
Risk Score

Off-domain redirect lands on a 'Loading……' page holding a disguised, orphan password field with Unicode evasion and DevTools blocking — a credential-harvesting shape with no legitimate content or brand.

Risk Factors (5)
Disguised password field (type='text' with password placeholder) plus hidden password input
Password field without a corresponding username/email field — credential-harvesting shape
Unicode/mixed-script evasion inside form fields
Anti-analysis DevTools and view-source blocking
Cross-domain redirect to a destination domain of unknown age with an unrelated '?aaamu.top' parameter and no visible site content
Domain age information unavailable

Details

Page Title

welcome

Scan Type

public

Domain Name Analysis

Domain 'goto1.72.chat' uses the .chat top-level domain, featuring subdomain 'goto1'. The core label '72' covers 2 characters holding 0 vowels versus 0 consonants, along with 2 digits. Breaking it apart gives one word: 72. The median word length lands at 2 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://goto1.72.chat/8d/?aaamu.top

Page Load Overview

1.17s
Total Load Time
126 KB
Total Size

Language Analysis

Primary Language

🏳️X
Code: x
Confidence:80%
Script:Unknown
Direction:ltr

Detection Details

HTML Lang Attribute:x-default
Text Length:17 chars
Detector Agreement:0%

Website Classification

Primary Category

social media network62% confidence
Type: static
Method: ml+structural+ocr_tiebreaker

All Detected Categories

social media network
62%
news media journalism
55%
government public service
47%
technology software
45%
documentation technical
36%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
6156.225.108.43Hong Kong
AS139057Edgenext Legend Dynasty Pte. Ltd.
143.174.246.36Singapore
1156.225.108.41Hong Kong
AS139057Edgenext Legend Dynasty Pte. Ltd.
1117.187.133.33China
AS138407The Internet Data Center of Guizhou Mobile Communication Company Limited
143.169.95.124Singapore
1183.240.98.228China
AS56040China Mobile communications corporation
1156.225.108.42Hong Kong
AS139057Edgenext Legend Dynasty Pte. Ltd.
1120.52.95.244China
AS133119China Unicom IP network
143.174.247.36Singapore
1111.45.3.198China
AS9808China Mobile Communications Group Co., Ltd.
1510--

Detected Technologies4

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T12F32C9D759CA20171DA3A17A07EF5514A373C413468AEC90BA4E93007F69A7C07BEAED

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

192:uhKAaE+CLqi9b9GilQi9rcz8ADhBFHlzU56t2qwHz+zJE1ia+HNJbJ2z8QiCR1pD:rE+wDwhFg5u+z6E1NiG1pL8c

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:11644:Qqc0Wm0JzjMCKQAxArQqMQl8o4QHGAWJgiUQEJAnBiALBoASGAEaUZdKwACwILAOLQsBEGkFGIRZeySSMhxlBgIkIwQsVUIS

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:0000001818000000
Perceptual Hash:cccc3333cccc3333
Difference Hash:0000083010000000
Wavelet Hash:f0f0f0f800000000
Color Hash:#522d86

Other Hashes

Crop Resistant:0000083010000000

Scan History

Scan history not available

Unable to load historical scan data