Security Scan Report: rapsurviveinsixblog-dp4psqwwurg0.edgeone.app

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Submitted: Jun 20, 2026, 9:59:14 AMCompleted: Jun 20, 2026, 10:00:23 AMpubliccompleted
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Summary

This website contacted 5 IPs in 3 countries across 5 domains to perform 2 HTTP transactions. The main domain is rapsurviveinsixblog-dp4psqwwurg0.edgeone.app and was registered NaN years ago.

Submitted URL: https://rapsurviveinsixblog-dp4psqwwurg0.edgeone.app/aboitiz-power-corporation-linkedin.html

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

AI Security Verdict

Moderate Risk

Confidence: 75%

5
Risk Score

Informational article with moderate risk due to unknown subdomain age and obfuscated JavaScript, but no direct phishing or malware indicators.

Risk Factors
Unknown subdomain age (could be brand‑new)
Low domain ranking for a site mentioning a corporate brand
High JavaScript obfuscation score (potentially suspicious code)
Hosted on a generic hosting platform subdomain
Safety Factors
No forms collecting credentials or payment information
No malicious IoC, YARA, or IDS alerts
Content appears to be informational/reporting about a brand
No cross‑origin credential exfiltration observed
Domain age information unavailable

Details

Page Title

Aboitiz Power Corporation LinkedIn: Strategies for Smarter Engagement

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

social media network

(77%)

Domain Information

Within the application-focused generic top-level domain (.app), 'rapsurviveinsixblog-dp4psqwwurg0.edgeone.app' is registered; it also runs on subdomain 'rapsurviveinsixblog-dp4psqwwurg0'. Count 7 characters in 'edgeone' containing four vowels alongside 3 consonants. Word splitting yields two words: edge, one. Average segment length settles at 3.5 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://rapsurviveinsixblog-dp4psqwwurg0.edgeone.app/aboitiz-power-corporation-linkedin.html

Page Load Overview

1.70s
Total Load Time
16
HTTP Requests
10
Domains
328 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:3,825 chars
Detector Agreement:100%

Website Classification

Primary Category

social media network77% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

social media network
77%
government public service
71%
corporate business
65%
education learning
47%
entertainment media
39%

Detected Features

OG: article
Schema.org

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
443.152.26.58Singapore
3104.101.243.100Hamburg, Free and Hanseatic City of Hamburg, Germany
AS16625Akamai Technologies, Inc.
3104.21.0.120United States
AS13335Cloudflare, Inc.
3142.251.14.95United States
AS15169Google LLC
3172.240.108.68United States
AS7979Servers.com, Inc.
165--

Detected Technologies2

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1507209376A84112C53654059A4D2B3FCA9788826F392CDE170AD7B6C8F8ABC3537378D

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:hPW3hnYf8zX+9kh8KMY+vm3Ahdl/orWag4MVeE:ZW3hnz+9kNMY+u3A3lQrWag4WD

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:17563:pUuzQIgYM5AIQM0hSCClJSAqQMAAsbUIUagqCseAchC4I4yNyIqAkgTDpAIaAGCoCiRBMQgIiyuXZAUxM2YBEZAqA5hCSkkV

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:ffffffc18181c1ff
Perceptual Hash:e9c6963846639c9e
Difference Hash:3210220f0f3b0f27
Wavelet Hash:9effff8181818183
Color Hash:#ac5372

Other Hashes

Scan History

Scan history not available

Unable to load historical scan data