AI Analysis
Final verdict: SUSPICIOUS
The package KapoorLabs-MTrack has low risks for network, shell, and obfuscation activities but shows moderate suspicion due to its novelty and lack of version history or community engagement.
- Metadata risk due to being a new package with limited activity.
- Only one version available with no clear development trajectory.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network functionality.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is new with low activity and a single version, which raises some suspicion.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://opensource.org/licenses/BSD-3-Clause
Git Repository History
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Varun Kapoor" 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 KapoorLabs-MTrack
Your task is to develop a fully functional mini-application that leverages the 'KapoorLabs-MTrack' Python package to track microtubules in microscopy images. This application will serve as a tool for researchers and scientists to analyze cell structures more efficiently. Here are the steps and features your project should include: 1. **Setup and Environment**: Begin by setting up a virtual environment for your project. Install the necessary dependencies including 'KapoorLabs-MTrack'. Ensure you have a working Python 3.x environment. 2. **User Interface**: Create a simple but intuitive GUI using Tkinter or PyQt5. This UI should allow users to upload microscopy images, select tracking parameters, and view results. 3. **Image Processing**: Utilize 'KapoorLabs-MTrack' to preprocess the uploaded images. This might involve noise reduction, contrast enhancement, and other preprocessing techniques to prepare the images for tracking. 4. **Microtubule Tracking**: Implement the core functionality of microtubule tracking using 'KapoorLabs-MTrack'. Users should be able to specify parameters such as detection sensitivity and object size ranges. 5. **Visualization**: Develop a feature within your application that visualizes the tracked microtubules on the original image. Highlighting detected microtubules and their paths can help in better understanding of the data. 6. **Results Export**: Allow users to export the results in a format such as CSV or JSON. This exported data should contain information about each tracked microtubule, including its path coordinates and any relevant measurements. 7. **Documentation and Testing**: Write comprehensive documentation explaining how to use the application and how it works internally. Conduct thorough testing to ensure accuracy and reliability of the tracking algorithm. Additional Features: - Provide an option for batch processing multiple images at once. - Include a feature to save the processed images alongside the tracked microtubules highlighted. - Implement machine learning models to improve the accuracy of microtubule detection over time. Ensure your application is user-friendly and efficient, making it a valuable tool for anyone studying microtubule dynamics.