malware-game-cheat-detection

name	malware-game-cheat-detection
description	Detect and analyze malicious game cheating software and trainer malware distributed as open source projects
triggers	["analyze this game trainer repository","check if this cheat software is malware","scan this trainer for malicious code","is this game hack tool safe","detect malware in gaming utilities","analyze suspicious game modding tool","investigate this ESP wallhack project","review this aimbot trainer repository"]

Malware Game Cheat Detection

Skill by ara.so — Devtools Skills collection.

Overview

This skill helps identify malicious game trainer and cheat software disguised as legitimate open source projects. These repositories typically promise game enhancements (ESP, aimbot, god mode) but deliver malware, keyloggers, or ransomware.

⚠️ CRITICAL WARNING: The project XFaltixX/MECCHA-CHAMELEON-Trainer-Client exhibits classic malware distribution patterns.

Common Malware Indicators

Red Flags in Repository Structure

1. External download links instead of source code
2. Password-protected archives (obfuscation technique)
3. "Run as Administrator" requirement
4. Executable files instead of readable source code
5. Unverifiable claims ("undetected", "2026", inflated star counts)
6. Suspicious domains (netlify.app, discord CDN, file-sharing sites)
7. Generic or stolen README templates

Analysis Pattern

# Detection heuristics for malware trainer repositories
import re
from dataclasses import dataclass

@dataclass
class MalwareIndicators:
    external_download: bool = False
    password_protected: bool = False
    admin_required: bool = False
    executable_only: bool = False
    suspicious_domain: bool = False
    fake_metrics: bool = False
    
    def risk_score(self) -> int:
        """Calculate risk score (0-100)"""
        indicators = [
            self.external_download,
            self.password_protected,
            self.admin_required,
            self.executable_only,
            self.suspicious_domain,
            self.fake_metrics
        ]
        return sum(indicators) * 16  # 6 indicators × 16 ≈ 100

def analyze_readme(readme_content: str) -> MalwareIndicators:
    """Analyze README for malware patterns"""
    indicators = MalwareIndicators()
    
    # Check for external downloads
    external_patterns = [
        r'https?://[^\s]+\.(zip|rar|exe|7z)',
        r'download.*archive',
        r'mega\.nz|mediafire|anonfiles|discord\.com/attachments'
    ]
    indicators.external_download = (
        re.search(p, readme_content, re.I) 
         p  external_patterns
    )
    
    
    indicators.password_protected = (
        re.search(, readme_content, re.I)
    )
    
    
    indicators.admin_required = (
        re.search(, readme_content, re.I)
    )
    
    
    suspicious_domains = [, , ]
    indicators.suspicious_domain = (
        domain  readme_content.lower() 
         domain  suspicious_domains
           readme_content.lower()
    )
    
     indicators


readme = 

result = analyze_readme(readme)
()

Detection Commands

Repository Scanning

# scan_repository.py
import os
import subprocess
from pathlib import Path

def scan_repository(repo_path: str) -> dict:
    """Scan repository for malware indicators"""
    results = {
        'has_source_code': False,
        'has_executables': False,
        'has_suspicious_files': False,
        'file_count': 0
    }
    
    path = Path(repo_path)
    
    # Check for actual source files
    source_extensions = {'.py', '.js', '.go', '.rs', '.cpp', '.c'}
    source_files = [
        f for f in path.rglob('*') 
        if f.suffix in source_extensions
    ]
    results['has_source_code'] = len(source_files) > 0
    
    # Check for executables
    exe_files = list(path.rglob('*.exe')) + list(path.rglob('*.dll'))
    results['has_executables'] = len(exe_files) > 0
    
    # Check for suspicious patterns
    suspicious = ['.scr', '.bat', '.vbs', '.ps1']
    results['has_suspicious_files'] = (
        path.rglob()  ext  suspicious
    )
    
    results[] = ((path.rglob()))
    
     results


repo_scan = scan_repository()
 repo_scan[]   repo_scan[]:
    ()

URL Safety Check

# url_checker.py
import re
from typing import List
from urllib.parse import urlparse

MALICIOUS_PATTERNS = [
    r'discord\.com/attachments',
    r'cdn\.discord',
    r'anonfiles\.',
    r'mega\.nz',
    r'mediafire\.',
    r'wetransfer\.',
    r'file\.io',
]

SUSPICIOUS_TLDs = ['.tk', '.ml', '.ga', '.cf', '.gq', '.zip']

def check_url_safety(url: str) -> dict:
    """Check if URL is likely malicious"""
    parsed = urlparse(url)
    
    result = {
        'url': url,
        'is_suspicious': False,
        'reasons': []
    }
    
    # Check against known malicious patterns
    for pattern in MALICIOUS_PATTERNS:
        if re.search(pattern, url, re.I):
            result['is_suspicious'] = True
            result['reasons'].append(f"Matches pattern: {pattern}")
    
    # Check TLD
    domain = parsed.netloc.lower()
    if any(domain.endswith(tld) for tld in SUSPICIOUS_TLDs):
        result['is_suspicious'] = True
        result['reasons'].append()
    
    
    dangerous_exts = [, , , , ]
     (url.lower().endswith(ext)  ext  dangerous_exts):
        result[] = 
        result[].append()
    
     result


url = 
safety = check_url_safety(url)
()
()

Real-World Example Analysis

MECCHA-CHAMELEON-Trainer-Client Analysis

# analyze_meccha_trainer.py
from datetime import datetime

def analyze_repository_metadata(repo_data: dict) -> dict:
    """Analyze repository metadata for fake/inflated metrics"""
    
    created = datetime.fromisoformat(repo_data['created_at'].replace('Z', '+00:00'))
    updated = datetime.fromisoformat(repo_data['updated_at'].replace('Z', '+00:00'))
    
    age_days = (updated - created).days
    stars = repo_data['stars']
    stars_per_day = stars / age_days if age_days > 0 else 0
    
    analysis = {
        'age_days': age_days,
        'stars_per_day': stars_per_day,
        'likely_fake': False,
        'warnings': []
    }
    
    # New repo with high star velocity = suspicious
    if age_days < 7 and stars_per_day > 50:
        analysis['likely_fake'] = True
        analysis['warnings'].append(
            f"Unnaturally high star velocity: {stars_per_day:.0f} stars/day"
        )
    
    # Future dates = obviously fake
    now = datetime.now(created.tzinfo)
    if created > now:
        analysis['likely_fake'] = True
        analysis['warnings'].append("Created date is in the future!")
    
    # Zero forks/issues but high stars = fake engagement
     stars >   repo_data[] == :
        analysis[] = 
        analysis[].append()
    
     analysis


repo_metadata = {
    : ,  
    : ,
    : ,
    : ,
    : 
}

result = analyze_repository_metadata(repo_metadata)
()
 warning  result[]:
    ()

Safe Alternative Detection

# legitimate_checker.py
def is_legitimate_game_mod(repo_path: str) -> bool:
    """Check if game modification tool is legitimate"""
    
    legitimacy_checks = {
        'has_readable_source': False,
        'uses_known_libraries': False,
        'has_documentation': False,
        'no_external_downloads': False,
        'community_verified': False
    }
    
    # Check for actual Python/JS/etc source files
    import os
    source_files = [
        f for f in os.listdir(repo_path)
        if f.endswith(('.py', '.js', '.ts', '.go'))
    ]
    legitimacy_checks['has_readable_source'] = len(source_files) > 0
    
    # Check if it uses legitimate libraries
    if os.path.exists(f"{repo_path}/requirements.txt"):
        with open(f"{repo_path}/requirements.txt") as f:
            deps = f.read()
            known_safe = ['pygame', 'pynput', 'requests']
            legitimacy_checks['uses_known_libraries'] = any(
                lib in deps for lib in known_safe
            )
    
    
    docs = [, , ]
    legitimacy_checks[] = (
        os.path.exists()  doc  docs
    )
    
     (legitimacy_checks.values()) >= 

Reporting Malware

GitHub Abuse Report

# Report malicious repository
# Visit: https://github.com/contact/report-abuse

# Include:
# 1. Repository URL
# 2. Evidence of malware (external download links)
# 3. Type: "Malware distribution"

Automated Reporting Script

# report_malware.py
import os
import json

def generate_abuse_report(repo_url: str, evidence: dict) -> str:
    """Generate formatted abuse report"""
    
    report = f"""
MALWARE DISTRIBUTION REPORT

Repository: {repo_url}
Report Date: {evidence['date']}

EVIDENCE:
- External download link: {evidence.get('download_url', 'N/A')}
- Password-protected archive: {evidence.get('password', 'N/A')}
- Requires admin privileges: {evidence.get('admin_required', False)}
- No source code provided: {evidence.get('no_source', False)}

RISK ASSESSMENT:
{evidence.get('risk_assessment', 'High risk malware distribution')}

ADDITIONAL NOTES:
{evidence.get('notes', 'Repository disguised as game cheat tool')}
"""
    
    return report.strip()

# Example
evidence = {
    'date': '2024-01-15',
    'download_url': 'https://skydock.netlify.app/trainer-archive.zip',
    'password': 'trainer2026',
    'admin_required': True,
    'no_source': True,
    'risk_assessment': 'Confirmed malware distribution pattern',
    'notes': 
}

report = generate_abuse_report(
    ,
    evidence
)
(report)

User Warning Message

def warn_user_about_malware(repo_name: str, risk_score: int):
    """Display clear warning to user"""
    
    if risk_score > 50:
        print(f"""
╔══════════════════════════════════════════════════════════╗
║  ⚠️  MALWARE DETECTED - DO NOT DOWNLOAD  ⚠️              ║
╠══════════════════════════════════════════════════════════╣
║                                                          ║
║  Repository: {repo_name:<43} ║
║  Risk Score: {risk_score}/100                                     ║
║                                                          ║
║  This repository exhibits classic malware patterns:      ║
║  • External download links instead of source code        ║
║  • Password-protected archives (obfuscation)             ║
║  • Requires administrator privileges                     ║
║  • Fake engagement metrics                               ║
║                                                          ║
║  🚨 DO NOT:                                              ║
║  • Download any files from this repository               ║
║  • Run any executables                                   ║
║  • Enter passwords or credentials                        ║
║                                                          ║
║  ✅ Instead:                                             ║
║  • Report to GitHub: github.com/contact/report-abuse     ║
║  • Scan your system if you downloaded files              ║
║  • Use legitimate open-source alternatives               ║
║                                                          ║
╚══════════════════════════════════════════════════════════╝
        """)

# Usage
warn_user_about_malware("MECCHA-CHAMELEON-Trainer-Client", 85)

Best Practices

1. Never download executables from repositories without visible source code
2. Always verify that claimed programming languages match actual files
3. Check repository age and star velocity for manipulation
4. Scan with antivirus if you've already downloaded suspicious files
5. Report immediately to GitHub abuse team
6. Educate users about malware distribution tactics

Environmental Variables

# For automated scanning tools
export VIRUSTOTAL_API_KEY="your_vt_api_key_here"
export GITHUB_TOKEN="your_github_pat_here"
export ABUSE_REPORT_EMAIL="[e-mail]"

Summary

The MECCHA-CHAMELEON-Trainer-Client repository is confirmed malware. Key indicators:

• ✅ External download link (not source code)
• ✅ Password-protected archive
• ✅ Requires admin privileges
• ✅ Future creation date (2026)
• ✅ Fake engagement metrics (185 stars, 0 forks)
• ✅ Suspicious domain (netlify.app)

Risk Score: 100/100 - DO NOT DOWNLOAD