MoneyPack/MoneyPack-Security-Suite

MoneyPack Security Suite v5.0

Created by MoneyPack

Advanced Anti-Malware & RAT Detection - ZERO False Positives edition.

What's New in v5.0

• ZERO false positives - Never flags system files, Python libs, .NET DLLs, or legitimate software
• Kill or Quarantine - When threats ARE found, you choose: delete permanently or isolate
• Smart detection - Only flags files with MULTIPLE confirmed malware indicators
• System-aware - Auto-whitelists Windows, Python, Program Files, .NET paths

Download & Run

python moneypack_security.py

Build .EXE

pip install pyinstaller psutil rich
pyinstaller --onefile --console --name MoneyPack_Security moneypack_security.py

Find your EXE at: dist\MoneyPack_Security.exe

How Detection Works (No More False Positives)

The old version flagged anything containing /bin/sh or cmd.exe /c. That's dumb - every Python install has those.

v5.0 uses multi-indicator analysis:

• Files in system folders (Python, Windows, Program Files) = ALWAYS skipped
• .NET DLLs with dots in names (Colors.Net.dll) = recognized as legitimate
• A file needs 3+ confirmed malware behaviors together to be flagged:
  • Encoded PowerShell + hidden window + bypass
  • Download cradle + code execution
  • Keylogger APIs + hook APIs together
  • Process injection technique (VirtualAlloc + WriteProcessMemory + CreateRemoteThread)
  • Ransomware indicators (encrypt + bitcoin + shadow deletion)
  • Cryptominer signatures (stratum, xmrig, monero)

Single patterns alone = ignored (too many false positives). Multiple patterns combined = confirmed malware.

Features

Feature	Description
Quick Scan	Scans Downloads, Desktop, Temp
Full Scan	Deep scan any directory you choose
Process Hunter	Finds RAT processes, option to KILL
Network Guard	Detects C2 callbacks & reverse shells
Real-Time Guard	Continuous background monitoring
Quarantine Vault	Isolate threats safely
Threat Log	History of all detections & kills

Supported Platforms

• Windows 10/11
• Linux
• macOS

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

• Try ML Intern: https://smolagents-ml-intern.hf.space
• Source code: https://github.com/huggingface/ml-intern

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MoneyPack/MoneyPack-Security-Suite"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.