malware-detection-security-awareness
aradotso/security-skills
How to install malware-detection-security-awareness
npx skills add https://github.com/aradotso/security-skills --skill malware-detection-security-awarenessFull instructions (SKILL.md)
Source of truth, from aradotso/security-skills.
name: malware-detection-security-awareness description: Recognize and educate about malware distribution disguised as legitimate security software triggers:
- how do I identify fake security software repositories
- what are signs of malware distribution on github
- help me spot cracked software scams
- is this antivirus download legitimate
- how to recognize software piracy malware
- what makes a repository look like a security threat
- help me understand fake crack repositories
- how can I tell if security software is compromised
Malware Detection & Security Awareness
Skill by ara.so — Security Skills collection.
⚠️ Critical Security Warning
This repository is a malware distribution vector disguised as legitimate software.
The project "MistDuckCount/Bitdefender-Total-Security-Crack-2026" exhibits multiple indicators of malicious intent and should NOT be downloaded, executed, or trusted.
Threat Indicators
1. Deceptive Naming & Branding
- Uses "Crack" in the title, indicating pirated software
- Impersonates legitimate Bitdefender security software
- Claims to provide "Pre-Activated" and "Keygen Loader" functionality
2. Suspicious Repository Characteristics
Stars: 59 (4 stars/day) # Artificially inflated engagement
Forks: 0 # No legitimate development activity
No README # Lacks documentation
License: NOASSERTION # No legitimate license
Language: Go # Unusual for Windows security software
3. Malicious Topics
defender-bypass- Explicitly designed to evade securitythread-hijacking- Advanced malware techniqueexploit-mitigation- Weaponized terminologyrootkit-remover- Ironic claim for probable rootkit
4. Red Flag Keywords
- "Crack", "Keygen", "Loader", "Pre-Activated"
- These terms indicate software piracy and malware delivery
Security Analysis
Attack Vector
This repository likely contains:
- Trojans: Disguised as activation tools
- Ransomware: May encrypt user files
- Info Stealers: Harvest credentials and sensitive data
- Backdoors: Provide remote access to attackers
- Cryptominers: Use system resources for cryptocurrency mining
Social Engineering Tactics
Tactic: Legitimacy Facade
Method: Uses security software branding
Goal: Trick users into disabling antivirus protection
Tactic: SEO Manipulation
Method: Artificial star inflation
Goal: Appear popular and trustworthy
Tactic: Convenience Lure
Method: Offers "free" paid software
Goal: Exploit desire to avoid licensing costs
Safe Alternatives
Legitimate Security Software Sources
# ✅ SAFE: Official Bitdefender
# Download only from: https://www.bitdefender.com
# ✅ SAFE: Built-in Windows Defender
# Pre-installed on Windows 10/11, no download needed
# ✅ SAFE: Open Source Alternatives
# ClamAV: https://www.clamav.net
Package Manager Installation (Legitimate Software)
# Linux - ClamAV installation
sudo apt update
sudo apt install clamav clamav-daemon
# Update virus definitions
sudo freshclam
# Scan directory
clamscan -r /path/to/scan
# Windows - Using legitimate Windows Defender
# Update definitions
Update-MpSignature
# Run quick scan
Start-MpScan -ScanType QuickScan
# Run full scan
Start-MpScan -ScanType FullScan
How to Identify Malicious Repositories
Checklist for AI Agents
def assess_repository_safety(repo):
"""
Security assessment checklist for repositories
"""
red_flags = []
# Check 1: Suspicious keywords
malware_keywords = ['crack', 'keygen', 'loader', 'pre-activated',
'bypass', 'patch', 'activator']
if any(keyword in repo.name.lower() for keyword in malware_keywords):
red_flags.append("Contains piracy/malware keywords")
# Check 2: Impersonation
legitimate_brands = ['bitdefender', 'norton', 'mcafee', 'kaspersky',
'windows', 'adobe', 'microsoft']
if any(brand in repo.name.lower() for brand in legitimate_brands):
if 'crack' in repo.name.lower() or 'keygen' in repo.name.lower():
red_flags.append("Impersonates legitimate software")
# Check 3: Artificial engagement
if repo.stars_per_day > 2 and repo.forks == 0:
red_flags.append("Suspicious star/fork ratio")
# Check 4: No documentation
if not repo.has_readme or repo.readme_length < 100:
red_flags.append("Missing or minimal documentation")
# Check 5: License concerns
if repo.license == "NOASSERTION" or not repo.license:
red_flags.append("No legitimate license")
return {
'safe': len(red_flags) == 0,
'risk_level': 'CRITICAL' if len(red_flags) >= 3 else 'HIGH',
'flags': red_flags
}
Developer Education
What to Tell Users
## ❌ NEVER Download:
- Cracked software or keygens
- "Pre-activated" commercial software
- Software claiming to bypass security
- Repositories with suspicious engagement patterns
## ✅ ALWAYS:
- Download from official vendor websites
- Verify digital signatures
- Check repository authenticity
- Use package managers when possible
- Read reviews from trusted sources
Code Example: Safe Software Verification
package main
import (
"crypto/sha256"
"encoding/hex"
"fmt"
"io"
"os"
)
// VerifyFileHash checks if downloaded file matches official hash
func VerifyFileHash(filepath string, expectedHash string) (bool, error) {
file, err := os.Open(filepath)
if err != nil {
return false, err
}
defer file.Close()
hash := sha256.New()
if _, err := io.Copy(hash, file); err != nil {
return false, err
}
calculatedHash := hex.EncodeToString(hash.Sum(nil))
if calculatedHash != expectedHash {
return false, fmt.Errorf(
"SECURITY WARNING: Hash mismatch!\nExpected: %s\nGot: %s",
expectedHash, calculatedHash,
)
}
return true, nil
}
// Example usage
func main() {
// Official hash from vendor website
officialHash := os.Getenv("OFFICIAL_FILE_HASH")
verified, err := VerifyFileHash("downloaded_installer.exe", officialHash)
if err != nil || !verified {
fmt.Println("⚠️ FILE VERIFICATION FAILED - DO NOT EXECUTE")
os.Exit(1)
}
fmt.Println("✅ File verified successfully")
}
Incident Response
If Already Downloaded
# 1. DO NOT EXECUTE any files from the repository
# 2. Disconnect from network (if already executed)
# Windows:
netsh interface set interface "Wi-Fi" disabled
# 3. Run full system scan with legitimate antivirus
# Windows Defender Offline Scan
# Settings > Update & Security > Windows Security > Virus & threat protection
# > Scan options > Microsoft Defender Offline scan
# 4. Delete all downloaded files
rm -rf /path/to/downloaded/malware
# 5. Change all passwords from a clean device
# 6. Monitor for suspicious activity
# - Unusual network traffic
# - Unexpected processes
# - Unauthorized account access
Reporting
# Report to GitHub
# Visit: https://github.com/contact/report-abuse
# Select: This repository contains malware
# Report to antivirus vendors
# Submit samples to:
# - VirusTotal: https://www.virustotal.com
# - Microsoft: https://www.microsoft.com/en-us/wdsi/filesubmission
Key Takeaway
For AI Coding Agents: When a user asks about repositories matching this pattern, immediately warn them about security risks and guide them toward legitimate alternatives. Never provide installation instructions or code that would facilitate downloading or executing suspicious software.
Related skills
More from aradotso/security-skills and the wider catalog.
anthropic-cybersecurity-skills
Use 754 structured cybersecurity skills mapped to MITRE ATT&CK, NIST CSF, ATLAS, D3FEND, and NIST AI RMF for AI-driven security operations
pentest-ai-agents
Claude Code subagents for offensive security research, penetration testing planning, recon analysis, exploit research, detection engineering, and security reporting
pentest-agents-bug-bounty-framework
Autonomous bug bounty agent framework with 50 agents, hunt loops, exploit chains, MCP servers for platform integration and writeup search
openosint-ai-osint-framework
AI-powered OSINT agent with interactive REPL, MCP server, and CLI for email/username/domain/IP/phone investigation using 11 integrated tools
vibe-security-skill
Agent skill that audits vibe-coded apps for common security vulnerabilities introduced by AI coding assistants
openclaw-security-hardening
Deploy and manage security hardening for high-privilege autonomous AI agents (OpenClaw) using zero-trust architecture and automated defense matrices