Security Scan Report: github.sunshixu1994.workers.dev

Redirected to:
https://github.sunshixu1994.workers.dev/features/ai
Submitted: Completed: publiccompleted
This website contacted 13 IPs in 3 countries across 5 domains to perform 173 HTTP transactions. The main domain is workers.dev and was registered 7 years ago.

Submitted URL: http://github.sunshixu1994.workers.dev/features/ai

Effective URL:
https://github.sunshixu1994.workers.dev/features/ai
Redirected

AI Security Verdict

High Risk

Confidence: 80%

7
Risk Score

Unaffiliated workers.dev subdomain that clones GitHub's AI features page and claims to be GitHub, including brand-matching title and site name. No forms or malware detected, but it is clear brand impersonation and should be avoided.

Risk Factors (4)
Impersonation of a different entity's brand (GitHub) on a non-GitHub domain
Deceptive hostname embedding the impersonated brand
Unaffiliated shared-hosting subdomain with unknown age and no reputation ranking
Complete reproduction of a legitimate brand's site including sign-in links, a common phishing lure structure
Domain age information unavailable

Details

Page Title

GitHub AI · AI built into every step of your workflow · GitHub

Scan Type

public

Domain Name Analysis

The domain 'github.sunshixu1994.workers.dev' uses the developer-focused generic top-level domain (.dev) with subdomain 'github.sunshixu1994'. Its registrable label 'workers' stretches across 7 characters holding two vowels versus 5 consonants. It segments into 1 word: workers. Median word length is 7 characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of http://github.sunshixu1994.workers.dev/features/ai

Page Load Overview

3.88s
Total Load Time
3.3 MB
Total Size

Language Analysis

Primary Language

🇺🇸English
Code: en
Confidence:80%
Script:Latin
Direction:ltr

Detection Details

HTML Lang Attribute:en
Text Length:6,000 chars
Detector Agreement:100%

Website Classification

Primary Category

technology software66% confidence
Type: dynamic
Method: ml+structural

All Detected Categories

technology software
66%
documentation technical
39%
forum community discussion
33%

Detected Features

OG: object

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
17188.114.96.9Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
13188.114.97.9Cloudflare · WAFUnited States
AS13335Cloudflare, Inc.
13185.199.110.215Fastly · CDNUnited States
AS54113Fastly, Inc.
133.174.46.114Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
13140.82.113.21United States
AS36459GitHub, Inc.
1320.250.119.72Azure · CLOUDZurich, Zurich, Switzerland
AS8075Microsoft Corporation
13185.199.111.215Fastly · CDNUnited States
AS54113Fastly, Inc.
13185.199.108.215Fastly · CDNUnited States
AS54113Fastly, Inc.
13185.199.109.215Fastly · CDNUnited States
AS54113Fastly, Inc.
133.174.46.65Cloudfront · CDNUnited States
AS16509Amazon.com, Inc.
17313--

Detected Technologies5

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T12094E8FAB0B0D73A069E57ABF0E15A40D2E3169BC396ADD5C01DC758C1C688CF5A3267

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

12288:Lm7xNAT+8DMN3NpXoghdTQGtr+NN2HEoN/myH565/LqTYR+rdL0QZkwjzHVWlUJq:Lm7bmH

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:427789:kFQmZPxJkhBtaSAOsCoaeAgNmTDASWRAhEoIzigIbmCEIy2waEFgxBBwgBuBRkRUjEAAQVRCJAm5RMrYkgQx2lJgWMQHHHGw

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:f17c3c1800240018
Perceptual Hash:caccea636931c333
Difference Hash:23f0e1b151e90171
Wavelet Hash:e17f7f39003c003d
Color Hash:#409fbf

Other Hashes

Crop Resistant:23f0e1b151e90171

Scan History

Scan history not available

Unable to load historical scan data