Security Scan Report: eoknglwe.an.r.appspot.com

Redirected to:
https://33d699cc.f00ae5aa62a9700f3d7a1922.workers.dev/
Site favicon
Submitted: Completed: publiccompleted
This website contacted 9 IPs in 1 country across 9 domains to perform 20 HTTP transactions. The main domain is appspot.com and was registered 21 years ago.

Submitted URL: https://eoknglwe.an.r.appspot.com/

Effective URL:
https://33d699cc.f00ae5aa62a9700f3d7a1922.workers.dev/
Redirected

AI Security Verdict

High Risk

Confidence: 72%

8
Risk Score

A random workers.dev subdomain presents itself as 'Microsoft SharePoint Online' with a misspelled 'Mutifactor Auth' title and a 'checking your browser' gate — brand impersonation on a domain unrelated to Microsoft, with cloaked, conditional serving. Avoid.

Risk Factors (5)
Impersonation of Microsoft SharePoint Online branding on an unrelated, random workers.dev subdomain
Misspelled page title 'Mutifactor Auth' characteristic of hastily-built phishing pages
Obfuscated random-hex workers.dev host with no brand relationship to the claimed product
Conditional content delivery (scanner sees error page, client sees real content)
Single-source threat-intel phishing report tied to a third-party domain on the page
Domain age information unavailable

Details

Page Title

Mutifactor Auth

Scan Type

public

Domain Name Analysis

The domain 'eoknglwe.an.r.appspot.com' uses the commercial generic top-level domain (.com), featuring subdomain 'eoknglwe.an.r'. Its registrable label 'appspot' stretches across 7 characters holding two vowels versus 5 consonants. It segments into two words: app, spot. Median word length is 3.5 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://eoknglwe.an.r.appspot.com/

Page Load Overview

1.75s
Total Load Time
238 KB
Total Size

Language Analysis

Primary Language

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

Detection Details

HTML Lang Attribute:en-US
Text Length:662 chars
Detector Agreement:100%

Website Classification

Primary Category

technology software53% confidence
Type: static
Method: ml+structural

All Detected Categories

technology software
53%
documentation technical
34%
cryptocurrency blockchain
30%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
4142.250.154.153Google · CDNUnited States
AS15169Google LLC
2188.114.97.9Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
2188.114.96.9Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
2172.66.172.247Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
252.216.33.122Aws · CLOUDAshburn, Virginia, United States
AS16509Amazon.com, Inc.
2192.178.183.95Google · CDNUnited States
AS15169Google LLC
2104.20.41.165Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
2142.250.154.94Google · CDNUnited States
AS15169Google LLC
252.216.54.98Aws · CLOUDAshburn, Virginia, United States
AS16509Amazon.com, Inc.
209--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1B85154B384F2441721019190FEB6B51DCDAEC10B9A9A5C10F5AE53BABFC1ED188AB55C

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

48:wr2/ECZDUikbbai4BqaieRait0orKbapZpBtUrJRtN/lc520ipO3fE:M25ubFK10orKIMD9lc8QE

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:3008:iBEgAEBOAAB4AISBhIAAEAAAUAAgDBCAQCABAAkAAAQiAAAQIAAAAABBDAABAAgRAAMIACgWCBAjQAgAtAAB4AAIAQgBASAA

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:e7e7e7e7ffffffff
Perceptual Hash:b3333399ccccc4cc
Difference Hash:080c0c0c00000000
Wavelet Hash:c3c3c3c30f0f0f0f
Color Hash:#9f53ac

Other Hashes

Crop Resistant:080c0c0c00000000

Scan History

Scan history not available

Unable to load historical scan data