Security Scan Report: mitt-selskap.test.gronnjobb.no

Redirected to:
https://mitt-selskap.test.gronnjobb.no/api/auth/signin?error=SessionRe...
Submitted: Apr 21, 2026, 1:05:34 AMCompleted: Apr 21, 2026, 1:07:21 AMpubliccompleted
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Summary

This website contacted 7 IPs in 1 country across 5 domains to perform 24 HTTP transactions. The main domain is mitt-selskap.test.gronnjobb.no and was registered NaN years ago.

Submitted URL: https://mitt-selskap.test.gronnjobb.no/auth?authredirect=%2Ftasks%2F4512220416228375481

Effective URL: https://mitt-selskap.test.gronnjobb.no/api/auth/signin?error=SessionRequired&callbackUrl=https%3A%2F%2Fmitt-selskap.test.gronnjobb.no%2Fauth%3Fauthredirect%3D%252Ftasks%252F4512220416228375481Redirected

AI Security Verdict

Low Risk

Confidence: 85%

3
Risk Score

Site appears legitimate with no credential collection, but external malicious IP and unranked reputation warrant caution.

Risk Factors
Association with known malicious IP address
Unranked domain reputation
Safety Factors
Long‑standing domain registration (>12 years)
No forms collecting credentials or payments
No JavaScript malware YARA matches
No network IDS alerts
Domain age information unavailable

Details

Page Title

Sign In

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

news media journalism

(43%)

Domain Information

Domain 'mitt-selskap.test.gronnjobb.no' uses the Norwegian country-code top-level domain (.no); it also runs on subdomain 'mitt-selskap.test'. Its registrable label 'gronnjobb' stretches across 9 characters split between two vowels and 7 consonants. Splitting it apart reveals 5 words: gr, on, n, job, b. Expect two characters per word on average. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://mitt-selskap.test.gronnjobb.no/auth?authredirect=%2Ftasks%2F4512220416228375481

Page Load Overview

1.78s
Total Load Time
24
HTTP Requests
5
Domains
158 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:65 chars
Detector Agreement:100%

Website Classification

Primary Category

news media journalism43% confidence
Type: static
Method: ml+structural

All Detected Categories

news media journalism
43%
healthcare medical
41%
government public service
37%
real estate property
34%
finance banking
33%

Detected Features

No structural features detected

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
664.239.109.1United States
AS16509Amazon.com, Inc.
364.239.123.129United States
AS16509Amazon.com, Inc.
3142.251.20.97United States
AS15169Google LLC
3216.239.32.36United States
AS15169Google LLC
3216.150.1.65United States
AS16509Amazon.com, Inc.
366.33.60.67United States
AS16509Amazon.com, Inc.
376.76.21.98Walnut, California, United States
AS16509Amazon.com, Inc.
247--

Detected Technologies3

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T17BC171E5F010623B2213857837E2DF5C7259A600CA026FADFF67B1F098CA9D709B2949

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

96:nmO4nT/AOxpyW0K35keev7hxiDbIXNHAAGfBVhWIZIf0grIs1sw38z:mb5pyxKJe2nm1YHWkQ0gA

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:6099:EQKUM4AhQkjcCArFYIhABIIAIAVCKCAIgKqMAKATBSIQiCIgIJCwNNFmWsEsBAARoBKlyBAoQDKECEIAWIgQuDMcCkEAgAAE

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:ffe7ffa5ffffffff
Perceptual Hash:e626999966669999
Difference Hash:0008304c28100000
Wavelet Hash:f0f0c0c0d8c0f0f0
Color Hash:#a6c587

Other Hashes

Crop Resistant:0008304c28100000

Scan History

Scan history not available

Unable to load historical scan data