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

Supported Platforms

• Windows 10/11
• Linux
• macOS

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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.