aitviewer

v1.14.2 suspicious
4.0
Medium Risk

Viewing and rendering of sequences of 3D data.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some suspicious traits, particularly concerning shell execution and potential obfuscation, though it lacks clear indicators of malicious intent.

  • Shell execution detected
  • Potential obfuscation via pickle.loads
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package relies on internet services.
  • Shell: Shell execution suggests potential runtime environment interaction, which could be benign but requires further investigation into package functionality.
  • Obfuscation: The use of 'pickle.loads' might indicate obfuscation or encoding, but it is also commonly used for serialization in legitimate applications.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer appears new and there are no PyPI classifiers, indicating potential low effort or poor metadata quality.

📦 Package Quality Overall: Medium (6.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://eth-ait.github.io/aitviewer/
  • Detailed PyPI description (5414 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

  • 87 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in eth-ait/aitviewer
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • ocket: data = pickle.loads(message) # Equeue data for the main thread t
  • data = pickle.loads(message) # Equeue data for the main
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • er process. process = subprocess.Popen( popen_args, stdout=subprocess.PIPE,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository eth-ait/aitviewer appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Manuel Kaufmann, Velko Vechev, Dario Mylonopoulos" 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 aitviewer 
Create a 3D viewer application using the 'aitviewer' Python package. This application will allow users to visualize and manipulate sequences of 3D data, such as point clouds, meshes, and animations. The app should have a user-friendly interface where users can load their own 3D data files or select from a set of preloaded examples. Key functionalities include:

1. Loading and displaying various types of 3D data (point clouds, meshes, animations).
2. Interactive manipulation of the 3D view (rotation, zooming, panning).
3. Animation playback control (play, pause, rewind, fast-forward).
4. Saving the current view state as an image.
5. Basic data filtering options (e.g., color-based filtering for point clouds).
6. Integration of custom shaders for enhanced visual effects.

Utilize the 'aitviewer' package to handle the loading, rendering, and interaction aspects of the 3D data. The application should leverage 'aitviewer' to provide real-time feedback and smooth performance during user interactions. Additionally, explore the use of 'aitviewer' for advanced features like multi-view rendering and support for different file formats commonly used in 3D data visualization.

💬 Discussion Feed

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