autorender-python

v0.2.0 suspicious
4.0
Medium Risk

The official Python library for the autorender API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate network risk and high metadata risk, raising concerns about its legitimacy and potential for unauthorized data transfer or insecure practices.

  • Moderate network risk due to external communication
  • High metadata risk due to non-secure links and missing author history
Per-check LLM notes
  • Network: The presence of network calls suggests potential external communication, which may indicate legitimate functionality but also raises suspicion for unauthorized data transfer.
  • Shell: No shell execution patterns detected, suggesting low risk of direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The presence of non-secure links and a missing author history increases suspicion.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (13235 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 455 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 score 9.0

Found 6 network call pattern(s)

  • t should be used with httpx.Client(timeout=None) as http_client: client = Autorende
  • he httpx default with httpx.Client() as http_client: client = Autorender(
  • it being ignored with httpx.Client(timeout=HTTPX_DEFAULT_TIMEOUT) as http_client: c
  • arg"): async with httpx.AsyncClient() as http_client: Autorender(
  • True, http_client=httpx.Client(transport=MockTransport(handler=mock_handler)), ) as
  • , http_client=httpx.Client(), ), ], ids=["standard", "custo
✓ 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: autorender.io>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://my.test.server.example.com:8083
  • Non-HTTPS external link: http://my.test.proxy.example.com
⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 autorender-python 
Create a desktop application using Python that allows users to automatically render images from a series of input parameters. The application will utilize the 'autorender-python' package to interface with the autorender API, which provides advanced rendering capabilities. Here are the key steps and features to implement:

1. **Setup**: Begin by installing necessary Python packages including 'autorender-python'. Ensure your development environment is set up correctly.
2. **User Interface Design**: Develop a simple yet intuitive GUI using Tkinter or PyQt. The UI should allow users to input various parameters such as dimensions, colors, textures, and lighting conditions for the image they wish to render.
3. **Parameter Handling**: Implement functionality within the app to handle user inputs and pass them to the autorender API through the 'autorender-python' package. This includes validating inputs to ensure they meet the requirements specified by the API.
4. **Rendering Process**: Integrate the 'autorender-python' package to call the autorender API based on the user's input parameters. Monitor the rendering process and provide feedback to the user about the progress.
5. **Output Display**: Once the rendering is complete, display the rendered image back to the user within the application. Optionally, allow users to save the rendered image to their local file system.
6. **Error Handling**: Implement robust error handling mechanisms to manage any issues that may arise during the rendering process, such as incorrect input formats or API failures.
7. **Enhancements**: Consider adding additional features like a history of previous renders, the ability to load and reuse previous parameter sets, or even a preview mode that simulates what the final render might look like before initiating the full render process.

This project aims to showcase the power and ease of use of the 'autorender-python' package while providing a practical tool for anyone needing to generate custom images programmatically.

💬 Discussion Feed

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