Security Scan Report: tokutibihugo-hhp6yp5g4e.edgeone.app

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Submitted: May 20, 2026, 12:50:35 PMCompleted: May 20, 2026, 12:51:55 PMpubliccompleted
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Summary

This website contacted 7 IPs in 2 countries across 7 domains to perform 13 HTTP transactions. The main domain is tokutibihugo-hhp6yp5g4e.edgeone.app and was registered NaN years ago.

Submitted URL: https://tokutibihugo-hhp6yp5g4e.edgeone.app/22143-investments-in-rolex-watches-beats-houses-gold-dow-jones-average.html

The Cisco Umbrella rank of the primary domain is #455,732 of the top 1 million websites

AI Security Verdict

Moderate Risk

Confidence: 72%

5
Risk Score

Moderate risk page mentioning Rolex; no phishing or malware signs but unknown subdomain age and low reputation suggest caution.

Risk Factors
Unknown subdomain age (potentially brand‑new)
Low domain reputation (rank > 100K)
High JavaScript obfuscation score
Brand mention on unrelated hosting subdomain
Safety Factors
Page type is article (og:type=article) – indicates news/reporting
No login or payment forms
No credential exfiltration detected
No Indicators of Compromise matched
No malicious YARA patterns or IDS alerts
Domain age information unavailable

Details

Page Title

Investments in Rolex Watches Beats Houses, Gold, Dow Jones Average

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

corporate

(35%)

Domain Information

Within the application-focused generic top-level domain (.app), 'tokutibihugo-hhp6yp5g4e.edgeone.app' is registered with subdomain 'tokutibihugo-hhp6yp5g4e'. The second-level label 'edgeone' is 7 characters long with four vowels and 3 consonants. Breaking it apart gives two words: edge, one. The median word length lands at 3.5 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://tokutibihugo-hhp6yp5g4e.edgeone.app/22143-investments-in-rolex-watches-beats-houses-gold-dow-jones-average.html

Page Load Overview

12.98s
Total Load Time
20
HTTP Requests
12
Domains
25 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-US
Text Length:4,700 chars
Detector Agreement:100%

Website Classification

Primary Category

corporate35% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

corporate
35%
corporate business
32%
download file sharing
27%
education learning
27%
blog personal website
26%

Detected Features

OG: article
Schema.org

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
8104.21.0.120United States
AS13335Cloudflare, Inc.
2142.250.154.95United States
AS15169Google LLC
2142.251.110.94United States
AS15169Google LLC
2172.240.127.244United States
AS7979Servers.com, Inc.
2104.20.7.223United States
AS13335Cloudflare, Inc.
2172.66.169.241United States
AS13335Cloudflare, Inc.
2101.33.10.57Frankfurt am Main, Hesse, Germany
207--

Detected Technologies3

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T137721C3BA985153C73310295B1C6B3FC7B69C47ED35549E06CA8732AE6C1AEB487628C

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:WS9hnYf8zX+zQJKJLJ/cj6kZKiExOxwgvdYzcQQYYrWag4MDw:n9hnz+zVJULKiE4qxYrWag4Aw

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:16086:0AwQn4AsoJshDYMmRVjWASBAGECUgQBGCgqhMABJBkAEgJGqhhAAHAmQAAQQmgkTZyVAAkNCUqAJoD4BWMQnRzqGTYC4pLkA

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:00000000002000ff
Perceptual Hash:826ded4a9c1abc3a
Difference Hash:f1c1c9d1d9c1c1c9
Wavelet Hash:1838383c3c3c3cff
Color Hash:#83e06c

Other Hashes

Scan History

Scan history not available

Unable to load historical scan data