Phishing Analysis: Meta

Visual Capture

Detection Info

http://lateefshow.github.io/About-Meta

Detected Brand

Content Hashes (HTML Similarity)

Used to detect similar phishing pages based on HTML content

Algorithm	Hash Value
CONTENT TLSH	T1E0E1CF227085752B07936399BF26AB69A3924192976A1E0131F5491F9FE6E02CC231EF
CONTENT ssdeep	96:TlOKiKrU0D0LM/OpTuTB4/5zOqCRo83NJm1kCAUy+6ubW:QynYXzOqQ0AUJ6UW

Visual Hashes (Screenshot Similarity)

Used to detect visually similar phishing pages based on screenshots

Algorithm	Hash Value
VISUAL pHash	a9c768f61fb28229
VISUAL aHash	fbf31b3f3fffc9c9
VISUAL dHash	96d6f3696bac9393
VISUAL wHash	f3f1011f1fa74149
VISUAL colorHash	06600008000
VISUAL cropResistant	96d6f3696bac9393,7f6d4f353733988d,25cc8f1c5e397a71,27d31333278793b3,032646e66664556d,674757577f3f5f4f

Code Analysis

Threat Level ALTO

⚠️ Phishing Confirmed

📊 Risk Score Breakdown

Total Risk Score

40/100

🔬 Comprehensive Threat Analysis

Threat Type

Meta Phishing Landing Page

Target

Meta users

Attack Method

Phishing webpage

Exfiltration Channel

Form submission (backend endpoint not detected - likely JavaScript-based)

Risk Assessment

LOW - Automated credential harvesting with Form submission (backend endpoint not detected - likely JavaScript-based)

🏢 Brand Impersonation Analysis

Impersonated Brand

⚔️ Attack Methodology

Primary Method: Search Engine Manipulation

Fake Meta site positioned to capture victims through SEO tactics, typosquatting, or paid advertising. Serves as entry point for multi-stage attacks including credential theft and malware distribution.

Secondary Method: Standard Phishing Techniques

Uses typical phishing tactics including brand impersonation, urgency tactics, and social engineering to trick victims into providing sensitive information.