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Security Scan Report: lensafocusconnect-dpjpocv8k604.edgeone.app

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Submitted: Jun 27, 2026, 8:29:59 AMCompleted: Jun 27, 2026, 8:31:08 AMpubliccompleted
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Summary

This website contacted 5 IPs in 3 countries across 6 domains to perform 1 HTTP transaction. The main domain is lensafocusconnect-dpjpocv8k604.edgeone.app and was registered NaN years ago.

Submitted URL: https://lensafocusconnect-dpjpocv8k604.edgeone.app/alex-pereira-salary-breakdown.html

AI Security Verdict

Low Risk

Confidence: 70%

3
Risk Score

Informational article with no credential collection; unknown subdomain age and high JS obfuscation give moderate risk.

Risk Factors
Unknown subdomain age
Unranked domain (not in Cisco Umbrella top 1M)
High JavaScript obfuscation score
Safety Factors
No forms collecting credentials or payment data
No IDS/Suricata alerts
Page marked as an article (og:type="article") indicating informational content
Verdict cited a credential/login form, but DOM analysis found no password field (real or disguised) or payment field, and no other hard signal — credential-phishing framing unsupported; risk adjusted from 5 to 3
Domain age information unavailable

Details

Page Title

Kraken Darknet Market: The Successor to Hydra | Market Analysis

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

cryptocurrency blockchain

(77%)

Domain Information

Domain 'lensafocusconnect-dpjpocv8k604.edgeone.app' uses the application-focused generic top-level domain (.app), featuring subdomain 'lensafocusconnect-dpjpocv8k604'. The second-level label 'edgeone' is 7 characters long split between four vowels and 3 consonants. Word splitting yields 2 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://lensafocusconnect-dpjpocv8k604.edgeone.app/alex-pereira-salary-breakdown.html

Page Load Overview

1.82s
Total Load Time
15
HTTP Requests
11
Domains
35 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:3,242 chars
Detector Agreement:80%

Website Classification

Primary Category

cryptocurrency blockchain77% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

cryptocurrency blockchain
77%
blog personal website
70%
adult content
52%
technology software
40%
documentation technical
35%

Detected Features

OG: article
Schema.org

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
3142.251.127.95United States
AS15169Google LLC
3104.21.0.120United States
AS13335Cloudflare, Inc.
345.142.141.228Romania
AS39287Materialism s.r.l.
3101.33.10.10Frankfurt am Main, Hesse, Germany
AS139341ACE
3172.240.127.234United States
AS7979Servers.com, Inc.
155--

Detected Technologies2

Open-Graph-Protocolvarticle
100%
Statcounter
20%

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1CF721B27EA8061385752414AA4D6BBFC75BCC41AE783CCF9785CA32CD7C9BC758B2648

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

384:hPWWhnYf8zX+hl8ZtLM8k0gyVn/Iv606ueMlorWag4MVJS:ZWWhnz+hl8ZtLHgyVgvqMWrWag4WM

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:16320:jeJDEiCR3kmscKiFCgKbsScqgAsyEXgZEAEKE5iCxKAIYEAMBIAGwAKAmQLJGQ5LYIITuQewhApFByVCqLSSWOkQCxYEOGEC

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:ffffff81818181ff
Perceptual Hash:bdc3922d1c39c693
Difference Hash:22000a333333332e
Wavelet Hash:9effff8181818183
Color Hash:#d28279

Other Hashes

Crop Resistant:22000a333333332e,32b23232b2325879

Scan History

Scan history not available

Unable to load historical scan data