🤖 AI Analysis

Final verdict: SAFE

The package ViennaLS v5.7.3 has been assessed with a low risk score due to the absence of any network calls, shell executions, obfuscations, or credential harvesting. However, there is a slight concern regarding metadata management.

Low effort in metadata management
No signs of malicious activities

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low effort in metadata management and maintainer history, but there are no direct signs of malicious intent.

🔬 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

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://www.iue.tuwien.ac.at/

✓ Git Repository History

Repository ViennaTools/ViennaLS appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ViennaLS

Create a Python-based mini-application that leverages the 'ViennaLS' library to simulate the evolution of a droplet on a hydrophobic surface over time. This application will serve as an educational tool and a practical demonstration of level set methods in action. Here are the steps and features you need to implement:

1. **Project Setup**: Begin by setting up a virtual environment for your project. Install the necessary packages including ViennaLS. Ensure all dependencies are listed in a requirements.txt file.
2. **Simulation Initialization**: Design a class named `DropletSimulator` which initializes the simulation parameters such as initial droplet shape, surface properties, and time step size.
3. **Level Set Evolution**: Use ViennaLS to evolve the level set function representing the droplet interface over time. Implement methods within the `DropletSimulator` class to handle the level set equations, ensuring accurate representation of droplet dynamics.
4. **Visualization**: Integrate visualization capabilities using matplotlib or a similar library to display the droplet's shape at each time step. Consider adding interactive elements if possible.
5. **User Interaction**: Allow users to input custom parameters like initial droplet radius, surface tension, and adhesion coefficient. Provide a simple command-line interface or a basic GUI for user interaction.
6. **Saving Results**: Implement functionality to save the final state of the simulation as an image file and/or log the simulation data to a CSV file for further analysis.
7. **Documentation**: Write comprehensive documentation explaining how to run the application, customize parameters, and interpret the results. Include examples and use cases.

By completing these steps, you'll create a versatile tool that not only demonstrates the power of ViennaLS but also provides insights into fluid dynamics and surface interactions.