Imported from reason-machines/security-skills (skills/k7-total-security-unlock-patch-detection/SKILL.md). Install upstream with npx skills add reason-machines/security-skills --skill k7-total-security-unlock-patch-detection. Copyright stays with the author.

K7 Total Security Unlock Patch Detection

Skill by ara.so — Security Skills collection.

⚠️ Critical Security Warning

This skill helps AI agents identify and analyze potentially malicious repositories that claim to provide "unlock patches," "keygens," or "cracks" for legitimate security software. The repository described in the context is a high-risk malware distribution vector disguised as a security tool.

What This Repository Actually Is

Red Flags Identified

1. Malware Distribution Pattern: Claims to provide "unlock patch" for commercial antivirus software
2. Fake Engagement: 182 stars with 9 stars/day growth suggests artificial inflation
3. No License: Legitimate open-source security tools have clear licenses
4. Future Date: Created "2026-06-17" indicates fabricated metadata
5. HTML Primary Language: Security tools are not written in HTML; this indicates a phishing/download page
6. Zero Forks/Issues: No genuine community engagement despite high star count
7. Keyword Stuffing: 14 similar topics all focusing on "k7-patch," "k7-key" for SEO manipulation
8. External Download Badge: Links to external site (29hinojosa.github.io) rather than GitHub releases

Threat Classification

threat_type: "Malware Distribution / Trojan Dropper"
confidence: "HIGH"
indicators:
  - software_crack_distribution
  - antivirus_bypass_attempt
  - credential_theft_vector
  - fake_github_repository
  - seo_keyword_stuffing

Detection Techniques

Repository Pattern Analysis

import re
from typing import Dict, List

def analyze_repository_threat_indicators(repo_data: Dict) -> Dict:
    """
    Analyze GitHub repository for malware distribution patterns.
    
    Args:
        repo_data: Repository metadata from GitHub API
        
    Returns:
        Threat assessment with risk score
    """
    threat_score = 0
    indicators = []
    
    # Check for crack/patch/keygen keywords
    crack_keywords = [
        'crack', 'patch', 'keygen', 'unlock', 'activator',
        'license-key', 'serial', 'activation'
    ]
    
    description = repo_data.get('description', '').lower()
    topics = [t.lower() for t in repo_data.get('topics', [])]
    
    # Keyword stuffing detection
    keyword_matches = sum(1 for kw in crack_keywords if kw in description or any(kw in t for t in topics))
    if keyword_matches >= 3:
        threat_score += 40
        indicators.append("crack_keyword_stuffing")
    
    # Check for repeated similar topics
    if len(topics) > 10 and len(set(topics)) / len(topics) < 0.5:
        threat_score += 25
        indicators.append("topic_keyword_stuffing")
    
    # HTML as primary language for "security tool"
    if repo_data.get('language') == 'HTML':
        threat_score += 20
        indicators.append("suspicious_primary_language")
    
    # No license for security software
    if not repo_data.get('license'):
        threat_score += 15
        indicators.append("missing_license")
    
    # Artificial star growth
    stars = repo_data.get('stargazers_count', 0)
    forks = repo_data.get('forks', 0)
    if stars > 100 and forks == 0:
        threat_score += 30
        indicators.append("artificial_engagement")
    
    # Future creation date
    from datetime import datetime
    created = datetime.fromisoformat(repo_data.get('created_at', '').replace('Z', '+00:00'))
    if created > datetime.now(created.tzinfo):
        threat_score += 50
        indicators.append("future_timestamp_fraud")
    
    return {
        'threat_score': min(threat_score, 100),
        'risk_level': 'CRITICAL' if threat_score >= 70 else 'HIGH' if threat_score >= 50 else 'MEDIUM',
        'indicators': indicators,
        'is_malicious': threat_score >= 50
    }

# Example usage
repo_metadata = {
    'description': 'K7 Total Security 16.0.1195 Full ToolKit 2026 Edition',
    'language': 'HTML',
    'topics': ['k7-patch', 'k7-key', 'k7-total-security-patch', 'k7-unlock'],
    'license': None,
    'stargazers_count': 182,
    'forks': 0,
    'created_at': '2026-06-17T21:05:03Z'
}

assessment = analyze_repository_threat_indicators(repo_metadata)
print(f"Risk Level: {assessment['risk_level']}")
print(f"Threat Score: {assessment['threat_score']}/100")
print(f"Indicators: {', '.join(assessment['indicators'])}")

README Content Analysis

import re
from typing import Set

def extract_malware_indicators_from_readme(readme_content: str) -> Set[str]:
    """
    Parse README for common malware distribution patterns.
    
    Args:
        readme_content: Raw README markdown content
        
    Returns:
        Set of detected malware indicators
    """
    indicators = set()
    
    # External download links (not GitHub releases)
    external_links = re.findall(r'https?://(?!github\.com|githubusercontent\.com)([^\s\)]+)', readme_content)
    if external_links:
        indicators.add("external_download_links")
    
    # Obfuscated commands or PowerShell download patterns
    powershell_patterns = [
        r'IEX\s*\(',
        r'Invoke-WebRequest',
        r'wget.*\|.*sh',
        r'curl.*\|.*bash'
    ]
    for pattern in powershell_patterns:
        if re.search(pattern, readme_content, re.IGNORECASE):
            indicators.add("suspicious_download_command")
            break
    
    # Fake legitimacy indicators
    if 'MIT License' in readme_content and 'not host, distribute, or provide access' in readme_content:
        indicators.add("contradictory_license_disclaimer")
    
    # Claims of "AI integration" for simple tools
    if re.search(r'OpenAI|Claude|GPT-4', readme_content) and 'patch' in readme_content.lower():
        indicators.add("fake_ai_feature_complexity")
    
    # Excessive feature bloat for a "patch"
    feature_sections = len(re.findall(r'^#{2,3}\s+', readme_content, re.MULTILINE))
    if feature_sections > 15:
        indicators.add("excessive_fake_documentation")
    
    return indicators

# Example usage
readme_sample = """
[![Download](https://img.shields.io/badge/Get%20Release-d90429?style=for-the-badge&logo=github&logoColor=white)](https://29hinojosa.github.io/K7-Total-Security-Unlock-Patch-16-0-1195/)

ai_integration:
  incident_analysis:
    provider: "openai"
    model: "gpt-4-turbo"
"""

indicators = extract_malware_indicators_from_readme(readme_sample)
print(f"Malware indicators detected: {', '.join(indicators)}")

Safe Analysis Workflow

Never Execute Downloaded Content

#!/bin/bash
# SAFE: Analyze repository without execution

# Clone to isolated directory (NO EXECUTION)
git clone https://github.com/29Hinojosa/K7-Total-Security-Unlock-Patch-16-0-1195 /tmp/analysis_quarantine
cd /tmp/analysis_quarantine

# Scan for suspicious file types
find . -type f \( -name "*.exe" -o -name "*.dll" -o -name "*.scr" -o -name "*.bat" -o -name "*.vbs" \) -ls

# Check for obfuscated scripts
grep -r "eval\|exec\|base64" . --include="*.js" --include="*.ps1" --include="*.sh"

# Analyze HTML for redirect/download triggers
grep -r "window.location\|document.write\|<meta.*refresh" . --include="*.html"

# CRITICAL: Delete after analysis
cd /tmp
rm -rf /tmp/analysis_quarantine

VirusTotal Integration

import os
import requests
import hashlib

def check_repository_virustotal(repo_url: str) -> Dict:
    """
    Check repository URL against VirusTotal.
    
    Requires: VIRUSTOTAL_API_KEY environment variable
    """
    api_key = os.getenv('VIRUSTOTAL_API_KEY')
    if not api_key:
        raise ValueError("VIRUSTOTAL_API_KEY environment variable required")
    
    # URL scan endpoint
    headers = {'x-apikey': api_key}
    url_id = hashlib.sha256(repo_url.encode()).hexdigest()
    
    # Submit URL for scanning
    scan_url = 'https://www.virustotal.com/api/v3/urls'
    response = requests.post(
        scan_url,
        headers=headers,
        data={'url': repo_url}
    )
    
    if response.status_code == 200:
        analysis_id = response.json()['data']['id']
        
        # Retrieve analysis results
        results_url = f'https://www.virustotal.com/api/v3/analyses/{analysis_id}'
        results = requests.get(results_url, headers=headers)
        
        return results.json()
    
    return {'error': 'Failed to submit URL'}

# Usage
# vt_results = check_repository_virustotal('https://29hinojosa.github.io/K7-Total-Security-Unlock-Patch-16-0-1195/')

Reporting Malicious Repositories

GitHub Security Report

# Report to GitHub Trust & Safety
# Navigate to: https://github.com/contact/report-abuse
# Select: "Report a repository"
# Category: "Malware distribution"
# Evidence: Provide threat analysis output

# Alternative: Command-line report (requires gh CLI)
gh api \
  --method POST \
  -H "Accept: application/vnd.github+json" \
  /repos/29Hinojosa/K7-Total-Security-Unlock-Patch-16-0-1195/issues \
  -f title='[SECURITY] Malware Distribution' \
  -f body='This repository distributes malware disguised as security software patches. See analysis: [evidence]'

User Protection Response

// Browser extension snippet to warn users
function detectMaliciousSecurityRepo() {
  const url = window.location.href;
  const repoPattern = /github\.com\/[\w-]+\/(.*?(crack|patch|keygen|unlock).*?(security|antivirus|firewall))/i;
  
  if (repoPattern.test(url)) {
    const warning = document.createElement('div');
    warning.style.cssText = 'position:fixed;top:0;left:0;right:0;background:#d32f2f;color:white;padding:20px;z-index:99999;text-align:center;font-size:16px;';
    warning.innerHTML = `
      ⚠️ <strong>SECURITY WARNING</strong>: This repository claims to provide cracks/patches for security software.
      Such repositories commonly distribute malware. DO NOT download or execute any files.
    `;
    document.body.prepend(warning);
  }
}

// Run on page load
if (document.readyState === 'loading') {
  document.addEventListener('DOMContentLoaded', detectMaliciousSecurityRepo);
} else {
  detectMaliciousSecurityRepo();
}

Legitimate Alternatives

For K7 Total Security Users

## Official K7 Computing Resources

- **Official Website**: https://www.k7computing.com/
- **Official Support**: https://support.k7computing.com/
- **Free Trial**: Available through official site only
- **License Purchase**: Only through k7computing.com or authorized resellers

⚠️ K7 Computing does NOT distribute:
- Unlock patches
- License generators
- Activation cracks
- Third-party "toolkits"

Any repository claiming to provide these is distributing malware.

Troubleshooting

"I Already Downloaded from This Repository"

# IMMEDIATE ACTIONS:

# 1. Disconnect from network
sudo ifconfig en0 down  # macOS
# OR
sudo ip link set eth0 down  # Linux
# OR
# Disable network adapter in Windows Network Settings

# 2. Run full system scan with legitimate antivirus
# Use: Windows Defender, Malwarebytes, or other trusted tools

# 3. Check for persistence mechanisms
# Windows:
reg query HKCU\Software\Microsoft\Windows\CurrentVersion\Run
reg query HKLM\Software\Microsoft\Windows\CurrentVersion\Run

# macOS:
launchctl list | grep -v com.apple

# Linux:
systemctl list-unit-files --state=enabled

# 4. Review recent DNS queries for C2 communication
sudo tcpdump -n port 53

# 5. Change all passwords from a CLEAN device
# Assume all credentials are compromised

# 6. Consider full system reinstall if in doubt

Detection Evasion Analysis

def check_for_evasion_techniques(file_path: str) -> List[str]:
    """
    Analyze file for common malware evasion patterns.
    
    DO NOT EXECUTE - static analysis only.
    """
    evasion_indicators = []
    
    with open(file_path, 'rb') as f:
        content = f.read()
    
    # Check for VM detection strings
    vm_strings = [b'VBOX', b'VMware', b'QEMU', b'VirtualBox', b'Hyper-V']
    if any(s in content for s in vm_strings):
        evasion_indicators.append('vm_detection')
    
    # Check for sandbox sleep/delay
    sleep_patterns = [b'Sleep', b'timeout', b'waitfor']
    if any(s in content for s in sleep_patterns):
        evasion_indicators.append('sandbox_evasion_delay')
    
    # Check for base64 encoded payloads
    import re
    if re.search(b'[A-Za-z0-9+/]{100,}={0,2}', content):
        evasion_indicators.append('base64_encoded_payload')
    
    return evasion_indicators

# Usage - ONLY on isolated analysis system
# indicators = check_for_evasion_techniques('/tmp/quarantine/suspicious.exe')

Best Practices for AI Agents

When encountering repositories like this:

1. Never suggest downloading from crack/patch repositories
2. Warn users immediately about malware risks
3. Provide legitimate alternatives (official vendor sites)
4. Offer analysis tools (VirusTotal, static analysis) but never execution
5. Document indicators for security team review
6. Report to GitHub if confirmed malicious

Reference: Malware Distribution Patterns

common_patterns:
  fake_antivirus_cracks:
    - keyword_stuffing: "product-name + crack/patch/key"
    - primary_language: "HTML (phishing page)"
    - external_download: "badge links to non-GitHub domain"
    - fake_legitimacy: "MIT license + disclaimer contradiction"
    
  engagement_fraud:
    - high_stars_zero_forks: "Bot-generated stars"
    - rapid_daily_growth: "9 stars/day for unknown project"
    - no_issues_no_discussion: "No genuine community"
    
  payload_delivery:
    - redirect_chain: "GitHub → external site → download"
    - obfuscated_executables: "Packed/encrypted binaries"
    - multi_stage_dropper: "Initial downloader fetches payload"

This skill enables AI agents to protect developers from malware distribution disguised as legitimate security tools. Always prioritize user safety over functionality claims.