auroraviz

v0.2.0 suspicious
6.0
Medium Risk

A world-class Python visualization framework with WASM HTML export and WebGL point-cloud rendering.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and lacks essential metadata such as a maintainer and a valid git repository, raising concerns about its legitimacy and potential for misuse.

  • High obfuscation risk due to base64 encoding
  • Missing maintainer information and git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: The use of base64 encoding and decoding suggests potential obfuscation practices which may hide malicious code or data.
  • Credentials: No clear evidence of credential harvesting is present, but further investigation into the purpose of encoded data is recommended.
  • Metadata: The package has a missing maintainer and the git repository is not found, raising suspicion.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_core.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4643 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 17 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • de("ascii") decoded = base64.b64decode(b64) self.assertEqual(raw, decoded) def test_he
  • en(b64), 0) decoded = base64.b64decode(b64) self.assertEqual(len(decoded), n * 6 * 4) # -
✓ 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 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 auroraviz 
Create a fully-functional mini-application called 'AuroraViewer' using the 'auroraviz' Python package. This application will allow users to visualize complex 3D data sets with interactive WebGL point-cloud rendering and export these visualizations as standalone HTML files that can run in any modern web browser. The application should include the following core functionalities:

1. **Data Import**: Users should be able to import their own 3D data sets in common formats such as .csv, .txt, or .json.
2. **Visualization Settings**: Allow users to customize the appearance of the point cloud visualization, including color schemes, point size, and lighting effects.
3. **Interactive Exploration**: Implement mouse and keyboard controls for zooming, panning, and rotating the view around the point cloud.
4. **Export Functionality**: Provide an option to export the current view as a standalone HTML file, which includes all necessary WASM libraries and assets for rendering.
5. **Example Data Sets**: Include several example 3D data sets to demonstrate the capabilities of the application.
6. **User Interface**: Develop a simple yet intuitive user interface using HTML/CSS/JavaScript to interact with the Python backend running 'auroraviz'.

The 'auroraviz' package will be utilized to handle the heavy lifting of rendering the point clouds and exporting the visualizations to HTML. Your task is to integrate this functionality into a seamless user experience, ensuring that both novice and experienced users can easily visualize and share their 3D data.

💬 Discussion Feed

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