Security Scan Report: webmailxcueu89-klausefncoi.appwrite.network

Submitted: Mar 27, 2026, 4:21:48 PMCompleted: Mar 27, 2026, 4:23:07 PMpubliccompleted
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Summary

This website contacted 8 IPs in 3 countries across 6 domains to perform 9 HTTP transactions. The main domain is webmailxcueu89-klausefncoi.appwrite.network and was registered NaN years ago.

Submitted URL: https://webmailxcueu89-klausefncoi.appwrite.network/#[email protected]

AI Security Verdict

Moderate Risk

Confidence: 78%

5
Risk Score

Impersonates Medicinbloggen with a fake login prompt; likely phishing, avoid interaction.

Risk Factors
Brand impersonation on an unrelated domain
Email address in URL fragment used as lure
Misleading login page without a real form
External POST to unknown domain (potential data exfiltration)
502 Bad Gateway page displaying login fields
Safety Factors
Domain age 1368 days (well‑established)
No malicious Indicators of Compromise detected
No password or payment fields present
No JavaScript malware patterns detected
No credential exfiltration observed
Domain age information unavailable

Details

Page Title

Webmail Portal Login - medicinbloggen

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

healthcare medical

(70%)

Domain Information

Within the .network top-level domain, 'webmailxcueu89-klausefncoi.appwrite.network' is registered with subdomain 'webmailxcueu89-klausefncoi'. The second-level label 'appwrite' is 8 characters long split between three vowels and 5 consonants. It segments into 2 words: app, write. Median word length comes out to four characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://webmailxcueu89-klausefncoi.appwrite.network/#contact@medicinbloggen.se

Page Load Overview

1.14s
Total Load Time
9
HTTP Requests
6
Domains
126 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:123 chars
Detector Agreement:33%

Website Classification

Primary Category

healthcare medical70% confidence
Type: static
Method: ml+structural

All Detected Categories

healthcare medical
70%
news media journalism
45%
blog personal website
44%
government public service
38%
documentation technical
29%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
2151.101.67.52United States
AS54113Fastly, Inc.
1142.251.152.119United States
AS15169Google LLC
1208.91.114.103Surrey, British Columbia, Canada
AS40934Fortinet Inc.
1142.251.157.119United States
AS15169Google LLC
1178.63.16.224Falkenstein, Saxony, Germany
AS24940Hetzner Online GmbH
113.35.58.104United States
AS16509Amazon.com, Inc.
1151.101.66.137United States
AS54113Fastly, Inc.
113.35.58.10United States
AS16509Amazon.com, Inc.
98--

Detected Technologies5

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T18692626629F308215557A4BDBBD763053A31E003990ACD487FAC874C9FA6ED29D3378D

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

192:nYXMiFoY1ALs4tF2ckOIFHCCDByJHRifTgzreyO73Q+VYOvM1PvCG/W7yBPbkrQT:YpFYLjFIFiCdTTOCTFB6bkrQBASaxm

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:20307:yUUKWWNAEIhCYsIDKDsAYVoqIL4H4KCMBVbCsEiEDJIgYu0DSrjao1EwEiQJWFBkA5CEEBWkMQqLlLLGAiAQh0SEBxR2BJqk

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:08b43c4c38000000
Perceptual Hash:d50f262719991f66
Difference Hash:d22a2a5222120202
Wavelet Hash:20383828faf2f2f2
Color Hash:#1f9361

Other Hashes

Crop Resistant:d22a2a5222120202

Scan History

Scan history not available

Unable to load historical scan data