NX-5

v1.0.2 suspicious
6.0
Medium Risk

A topology-based visual encoding system that represents text as nested squares with binary edge notches

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network, shell execution, and obfuscation but has a high metadata risk due to rapid commits and suspicious maintainer history, which could indicate potential tampering or malicious intent.

  • High metadata risk
  • Suspicious maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity related to stealing secrets or credentials.
  • Metadata: High risk due to recent rapid commits and suspicious maintainer history.

🔬 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

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: All 6 commits happened within 24 hours

  • All 6 commits happened within 24 hours
⚠ 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 NX-5 
Create a mini-application named 'NotchVisualizer' that leverages the NX-5 package to visually represent text input as nested squares with binary edge notches. This application should allow users to input any textual content and see it transformed into a unique graphical representation based on its topological structure. Here are the steps and features your application should include:

1. **User Input**: Design a simple UI where users can enter their text. This could be a basic web interface using Flask or a command-line interface.
2. **Text Processing**: Implement a function that processes the input text and prepares it for visualization. This includes tokenization, possibly removing stop words, and converting the text into a format suitable for NX-5.
3. **Visualization Generation**: Utilize the NX-5 package to convert the processed text into nested square representations with binary edge notches. Ensure that the output maintains the topological relationships within the text.
4. **Display Results**: Develop a feature to display these visual representations either as images saved locally or directly rendered in the UI. For web interfaces, consider integrating a library like Matplotlib or Plotly for dynamic visualization.
5. **Interactive Elements**: Add interactive elements such as zooming in/out, rotating the view of the nested squares, and toggling between different types of edge notches to enhance user engagement.
6. **Documentation and Help**: Include comprehensive documentation explaining how the application works, the significance of the binary edge notches, and how users can interpret the visual outputs.

Optional Features:
- **Export Functionality**: Allow users to export the generated visualizations as image files (PNG, SVG).
- **Comparison Tool**: Implement a feature that allows users to input multiple texts and compare their visual representations side-by-side.
- **Customization Options**: Provide options for users to customize the appearance of the nested squares, such as changing colors or adding labels.

Ensure that the application is well-documented and easy to install, making use of virtual environments and pip for dependency management.

💬 Discussion Feed

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