Security Scan Report: neww-project-pdffiles.vercel.app

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Submitted: Completed: publiccompleted
This website contacted 11 IPs in 2 countries across 10 domains to perform 13 HTTP transactions. The main domain is vercel.app and was registered 6 years ago.

Submitted URL: https://neww-project-pdffiles.vercel.app/

AI Security Verdict

Confirmed Scam

Confidence: 97%

10
Risk Score

OneDrive/Microsoft look-alike login page on a free vercel.app subdomain that harvests email and password, flagged by Safe Browsing for social engineering and containing a Telegram bot token for exfiltration.

Risk Factors (6)
Brand impersonation of Microsoft OneDrive on a domain that is not Microsoft's
Credential collection (email + password) on a look-alike login page
Google Safe Browsing Social Engineering detection
Telegram bot token in inline script enabling exfiltration of submitted data
Right-click blocking and anti-analysis JavaScript
Document-sharing lure used to push victims into entering email and password
Domain age information unavailable

Details

Page Title

My Files - OneDrive

Scan Type

public

Domain Name Analysis

The domain 'neww-project-pdffiles.vercel.app' uses the application-focused generic top-level domain (.app) and includes subdomain 'neww-project-pdffiles'. The core label 'vercel' covers 6 characters split between two vowels and 4 consonants. Word splitting yields two words: ver, cel. Median word length is three characters. No strong language cues emerged from the frequency lists.

Screenshot

Samm Files Directory Ea My Files ‘D Recent Buploady sync #Aulomatew Q Search Allfiles v Shared Files 2, Shared ‘Shared Libaries Sites help you work on projets with your (eam and share informat...

Page Load Overview

11.09s
Total Load Time
349 KB
Total Size

Language Analysis

Primary Language

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

Detection Details

HTML Lang Attribute:en
Text Length:1,007 chars
Detector Agreement:100%

Website Classification

Primary Category

documentation technical66% confidence
Type: webapp
Method: ml+structural

All Detected Categories

documentation technical
66%
technology software
61%
download file sharing
48%
government public service
30%
social media network
28%

Detected Features

Login Form

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
3216.198.79.67Aws · CLOUDUnited States
AS16509Amazon.com, Inc.
123.207.210.77Akamai · CDNFrankfurt am Main, Hesse, Germany
AS20940Akamai International B.V.
1104.17.25.14Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
113.107.246.44Azure · CLOUDUnited States
AS8075Microsoft Corporation
1151.101.193.155Fastly · CDNUnited States
AS54113Fastly, Inc.
1104.18.11.207Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
1142.250.154.95Google · CDNUnited States
AS15169Google LLC
1104.16.179.112Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
1151.101.1.155Fastly · CDNUnited States
AS54113Fastly, Inc.
1104.17.24.14Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
1311--

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T165F38D7995B210A54123F3781B681588397180B3D72EDE0CBDFC9598DF5EFA85CA22EC

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

3072:TlDGSjM74yR/uCVqtzfESSh+nTSVul4R2KWzJHVWyXEPXT98zEa1OKMpEVOu/hJ/:TlDGSjM74yRG/R8SSMTxlY2pJHVWyXEm

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:165269:gwhAWGWU4AUABgiCAIF1AAdaEQZRAMqhAoSJRCifAkoQAgUCFAeAMQAhRCoCBWaS1IQ6TGg4IAUTgbCaKEEJAAEWQAEC6wBC

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:0018181818000000
Perceptual Hash:8c9c63674e4c3b1d
Difference Hash:61b3333373cdf1c1
Wavelet Hash:001d1d1d3d7d0d0d
Color Hash:N/A

Scan History

Scan history not available

Unable to load historical scan data