MyoGen

v0.10.1 suspicious
6.0
Medium Risk

Modular and extensible neuromuscular simulation framework for generating physiologically grounded motor-unit activity, muscle force, and EMG signals (surface and intramuscular)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of obfuscation and performs shell executions, raising concerns about its true intent. While there is no direct evidence of malicious activities, the incomplete metadata and unusual coding practices warrant caution.

  • High obfuscation risk
  • Potential shell execution
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution patterns suggest the package may be performing system-specific tasks, which could be legitimate but warrants further investigation into its purpose and permissions.
  • Obfuscation: The use of __import__ with a variable for the module name suggests an attempt to hide or dynamically load modules, which is unusual and may indicate obfuscation.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The author's information is incomplete and the author seems to be new or inactive, which raises some suspicion but not enough to conclusively determine malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: __import__(module_name) except ImportError: all_compile
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • .") try: result = subprocess.run( ["where", "mknrndll.bat"], capture_output=True,
  • join(cmd)}") result = subprocess.run(cmd, capture_output=True, text=True, check=True) pri
  • output there result = subprocess.run( ["nrnivmodl", "."], cwd=nmodl_path,
  • ll_file.unlink() subprocess.run( ["cmd", "/c", str(mknrndll_path)],
  • Unix-like systems.""" subprocess.run( ["nrnivmodl", "."], cwd=nmodl_path, capture_out
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository NsquaredLab/MyoGen appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with MyoGen 
Develop a neuromuscular simulation tool using the MyoGen package. This tool will allow users to simulate muscle activities based on user-defined parameters such as neural activation patterns, muscle fiber types, and contraction dynamics. The application should provide a graphical interface where users can input these parameters and visualize the resulting motor-unit activity, muscle forces, and EMG signals both surface and intramuscular.

Step 1: Setup the Project
- Initialize a new Python project.
- Install MyoGen along with any necessary dependencies.
- Set up a virtual environment for the project.

Step 2: Design the User Interface
- Create a simple GUI using a library like PyQt5 or Tkinter.
- Design the UI to include input fields for neural activation patterns, muscle fiber type selection, and contraction dynamics.
- Add buttons for running simulations and visualizing results.

Step 3: Implement Core Simulation Logic
- Use MyoGen to define the neuromuscular model based on user inputs.
- Integrate the model into the GUI so that changes in the input fields dynamically update the simulation parameters.
- Implement functions to run the simulation and generate output data.

Step 4: Visualization
- Develop plots within the GUI to display motor-unit activity over time.
- Create graphs to show muscle force generation during contractions.
- Provide real-time visualization of surface and intramuscular EMG signals.

Step 5: Export Results
- Allow users to export the simulation results as CSV files or graphs.
- Include options to save the current simulation setup for future reference.

Features:
- Adjustable neural activation patterns through sliders or input boxes.
- Selection of different muscle fiber types from a dropdown menu.
- Real-time updates of simulation outputs as parameters change.
- Detailed documentation explaining the underlying neuromuscular model and how MyoGen is utilized.

This project aims to provide an accessible way for researchers and students to explore the complexities of neuromuscular interactions, leveraging the advanced capabilities of the MyoGen package.