A high-reliability heuristic-based Python code detector and sanitizer for text and JSON data extracted from the NeuroGenesys Framework.

This module is currently integrated as an operational node within NeuroGenesys' node-based graph editor. It is specifically designed to reliably detect, isolate, and wrap Python code segments embedded within arbitrary textual or JSON data.

In practice, this node processes user prompts, allowing the associated Large Language Model (LLM)—equipped with a suitably defined system prompt—to efficiently delegate wrapped Python code segments to specialized sub-agents or routing systems. This strategy optimizes the use of the LLM's context window, significantly reducing computational overhead in the finalized framework.

Furthermore, the module can also be strategically employed as a post-processing node following LLM-generated outputs. This facilitates streamlined indexing, subsequent automated processing, and additional context economy optimizations.

Note: The NeuroGenesys Framework remains under active development, and features described herein may evolve.

echo_node_system/
├── main.py
└── echo_graph_flow_editor/
    └── heuristics/
        ├── __init__.py
        ├── python_heuristic_detector.py
        └── unit_tests/
            ├── __init__.py
            └── test_python_heuristics.py
Documentation/
├── algo_pseudo_code.md
└── PythonHeuristicDetector_Final_simplified_organigram.svg

• main.py: A small PyQt-based program providing a graphical interface to input text and see heuristic detection results live as the text changes.
• python_heuristic_detector.py: Main implementation of the heuristic detector.
• test_python_heuristics.py: Comprehensive unit tests using unittest.

• Detects Python code in free text or JSON structures.
• Supports dangerous code pattern recognition (e.g., os.system, eval, pickle.loads).
• Handles nested JSON with optional parallel processing.
• Adjustable confidence threshold for wrapping decisions.
• Fully thread-safe implementation using QMutex and QMutexLocker.
• Minimal dependencies (only qtpy and PyQt5).

• Python 3.8 or higher
• qtpy
• PyQt5

This project uses a Python virtual environment to isolate dependencies.

On Linux/macOS:

bash install_linux.sh

On Windows:

install_windows.bat

This script will:

• Create a virtual environment in the .venv/ folder
• Upgrade pip
• Install all required packages (qtpy, PyQt5)

After successful setup, the environment is ready for use.

On Linux/macOS:

source .venv/bin/activate

On Windows:

call .venv\Scripts\activate.bat

Once the virtual environment is activated, you can run the unit tests using:

make test

This command will:

• Ensure that the virtual environment exists
• Use .venv/bin/python3 to run all unit tests in echo_node_system/echo_graph_flow_editor/heuristics/unit_tests/
• Display a detailed test report

You can also manually run:

python -m unittest discover -s echo_node_system/echo_graph_flow_editor/heuristics/unit_tests/ -v

However, make test is preferred for consistency.

To remove all Python cache files (.pyc, __pycache__) and reset the workspace:

make clean

from echo_node_system.echo_graph_flow_editor.heuristics.python_heuristic_detector import PythonHeuristicDetector

detector = PythonHeuristicDetector()
text = "def greet():\n    print('Hello')"
wrapped_text = detector.detectPythonInString(text)
print(wrapped_text)

• Algorithm Pseudo-Code: Documentation/algo_pseudo_code.md
• Simplified Organigram: Documentation/PythonHeuristicDetector_Final_simplified_organigram.svg

Both resources offer insights into the internal workings of the heuristic detector.

This project was developed by LoganSeven.

This project is licensed under the MIT (modified) License.

Researchers, security experts, and enthusiasts are strongly encouraged to attempt to defeat, fuzz, and challenge the heuristic detection system.

Feedback, reports, and contributions are highly welcome to continually improve its resilience and reliability.