Security Scan Report: xiaolifandai.polished-wood-4078.workers.dev

Redirected to:
https://search.aipages.dev/en?baililai.xyz
Site favicon
Submitted: Completed: publiccompleted
This website contacted 10 IPs in 3 countries across 7 domains to perform 16 HTTP transactions. The main domain is workers.dev and was registered 7 years ago.

Submitted URL: https://xiaolifandai.polished-wood-4078.workers.dev/

Effective URL:
https://search.aipages.dev/en?baililai.xyz
Redirected

AI Security Verdict

High Risk

Confidence: 78%

8
Risk Score

Free-hosted link-gate page demanding a password through a disguised, unicode-obfuscated password field with DevTools blocking — a credential-collection pattern, not a legitimate login.

Risk Factors (6)
Disguised password field (text input with password placeholder) — critical credential-harvesting pattern
Password collection with no username context anywhere on the page
Unicode evasion in form fields
Anti-analysis: right-click/DevTools/keyboard shortcuts blocked, dynamic code generation in page scripts
Anonymous free-hosting origin redirecting cross-domain to a link-gate page of unknown age and no reputation
Page text pressures users to 'verify the password' to reach content ('Too many visitors')
Domain age information unavailable

Details

Page Title

Latest @ Sites

Scan Type

public

Domain Name Analysis

The domain 'xiaolifandai.polished-wood-4078.workers.dev' uses the developer-focused generic top-level domain (.dev) and includes subdomain 'xiaolifandai.polished-wood-4078'. The registrable portion 'workers' spans 7 characters split between 2 vowels and five consonants. Splitting it apart reveals one word: workers. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://xiaolifandai.polished-wood-4078.workers.dev/

Page Load Overview

4.91s
Total Load Time
322 KB
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:50%
Script:Latin
Direction:ltr

Detection Details

HTML Lang Attribute:x-default
Text Length:272 chars
Detector Agreement:100%
Language mismatch: Declared as x-default but detected as en

Website Classification

Primary Category

social media network82% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

social media network
82%
news media journalism
53%
documentation technical
50%
technology software
46%
healthcare medical
34%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
7172.67.196.251Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
1104.21.44.75Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
1104.21.36.163Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
1120.52.95.244China
AS133119China Unicom IP network
143.174.246.36Singapore
143.169.95.124SingaporeUnknown
1111.45.3.198China
AS9808China Mobile Communications Group Co., Ltd.
1112.46.240.47China
AS9808China Mobile Communications Group Co., Ltd.
114.215.182.140China
AS4134Chinanet
114.215.183.79China
AS4134Chinanet
1610--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1A0C1D66AAAE54D361367C1C92176FB0DBAB6D40BDA0BC904B1FCB9841FCBFC54813A54

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

96:CdEnw8/0oYIiCoDWNNBCLUJtZfnDEKs2gmwj:CdCYonVbR62hwj

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:5915:MREAoAEjMEIIAQIQDAIKAkRAJAIAAG1AoACAEEqMCqQY2kEECIgLAAB0QAQAQowFQABBkEjFTGIiE0ADDnAUABMEMFgEJISI

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:01091f1b1f1f1f0f
Perceptual Hash:8c1d337666cc9933
Difference Hash:fff3b3b3f3b3f3ff
Wavelet Hash:01191b1b1f1f1f1f
Color Hash:N/A

Other Hashes

Scan History

Scan history not available

Unable to load historical scan data