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k7-total-security-unlock-patch-detection

Detect and analyze potentially malicious security software crack/patch repositories

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Quellinformationen

Repository
reason-machines/security-skills
Letzte Quellaktivität
6. Juli 2026 um 03:11
Erkannte Sprache von SKILL.md
Englisch
Sterne
12
Forks
1

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
k7-total-security-unlock-patch-detection
description
Detect and analyze potentially malicious security software crack/patch repositories
triggers
["analyze this k7 security repository","is this k7 patch repository safe","check if this security software repo is legitimate","detect malware distribution in antivirus cracks","identify fake security software repositories","analyze suspicious patch download sites","verify k7 total security repository authenticity","scan for trojan distribution in security tools"]
# K7 Total Security Unlock Patch Detection > Skill by [ara.so](https://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 ```yaml 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 ```python 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 ```python 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 ```bash #!/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 ```python 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 ```bash # 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 ```javascript // 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 ```markdown ## 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" ```bash # 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 ```python 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')
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