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Security Scan Report: git.kemendag.go.id

Redirected to: https://git.kemendag.go.id/users/sign_in

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Submitted: Jan 16, 2026, 4:08:04 PMCompleted: Jan 16, 2026, 4:09:44 PMpubliccompleted
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Summary

This website contacted 1 IP in 1 country across 1 domain to perform 20 HTTP transactions. The main domain is git.kemendag.go.id and was registered NaN years ago.

Submitted URL: https://git.kemendag.go.id

Effective URL: https://git.kemendag.go.id/users/sign_inRedirected

The Cisco Umbrella rank of the primary domain is #339,859 of the top 1 million websites

AI Security Verdict

Moderate Risk

Confidence: 75%

4
Risk Score

Login page uses GitLab branding on a government domain; likely a legitimate self‑hosted instance but warrants caution.

Risk Factors
Brand impersonation detected (GitLab branding on unrelated domain)
Low Cisco Umbrella ranking for a site claiming a major brand
Safety Factors
Domain age 5581 days (well‑established)
No malicious Indicators of Compromise matches found
Standard hosting (no IPFS or cloud storage)
No external links or redirects to suspicious sites
HTTPS connection (assumed from standard GitLab deployment)
Domain age information unavailable

Details

Page Title

Sign in · GitLab

Scan Type

public

Language

🇺🇸

English

(80% confidence)

Category

forum community discussion

(95%)

Domain Information

Within the Indonesian country-code top-level domain (.go.id), 'git.kemendag.go.id' is registered; it also runs on subdomain 'git'. The core label 'kemendag' covers 8 characters holding 3 vowels versus five consonants. Breaking it apart gives 3 words: ke, men, dag. Median word length is three characters. No strong language cues emerged from the frequency lists.

Screenshot

Security scan screenshot of https://git.kemendag.go.id

Page Load Overview

19.63s
Total Load Time
31
HTTP Requests
1
Domains
325 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:385 chars
Detector Agreement:100%

Website Classification

Primary Category

forum community discussion95% confidence
Type: webapp
Method: ml+structural

All Detected Categories

forum community discussion
95%
technology software
66%
government public service
42%

Detected Features

Login Form
OG: object

Domain & IP Information

RequestsIP AddressLocationAS Autonomous System
31103.207.103.40Indonesia
AS133796
311--

Detected Technologies4

PasswordField
100%
OpenSearch
100%
HSTS
40%
Arkose Labs
30%

Content Similarity HashesFor malware variant detection

TLSH (Trend Micro Locality Sensitive Hash)

Security-focused

Specialized for malware detection and similarity analysis

T1F0E1C983BC049C6A41B37931FDD3F18C95A6EA41DEE0D4A89675E10A25D1FD29423E37

ssdeep (Context Triggered Piecewise Hashing)

Context-aware

Detects similar content even with modifications

192:9pSNnAbBEkcyGj1/djVmNK+n17BDze+N7k4Rr:SNAbBE7zZ/dRmNKy17B3e+N7k4Rr

sdhash (Similarity Digest Hashing)

High-precision

High-precision similarity detection for forensic analysis

sdhash:3:7313:iXEohETAwjQUKIIA2GBAFiADcwEFKiEBQgAlSgBAMEgEEZIniVhFGwCwAEQhQgASAxAAGXaKARJiVC2NwIFAIhAFAbBEiC5I

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:ffefefe7e7ffff00
Perceptual Hash:b3b308cc66e619b3
Difference Hash:000c180c0c200030
Wavelet Hash:fce4e0e00333ff00
Color Hash:#7d40bf

Other Hashes

Crop Resistant:000c180c0c000030

Scan History

Scan history not available

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