backend.ai-appproxy-common

v26.4.3 safe
2.0
Low Risk

Backend.AI AppProxy Common

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity with very low risks across all categories. The only slight concern is the maintainer's limited presence with just one package.

  • No network calls
  • No shell execution
  • No obfuscation
  • No credential harvesting
  • Maintainer has only one package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution 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 maintainer has only one package, suggesting a new or less active account, but no other suspicious activities were flagged.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.backend.ai/
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

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

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in lablup/backend.ai
  • Active community — 5 or more distinct contributors

🔬 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

All external links appear legitimate

✓ Git Repository History

Repository lablup/backend.ai appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Lablup Inc. and contributors" 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 backend.ai-appproxy-common 
Your task is to develop a simple yet powerful utility named 'AppProxy Manager' using the Backend.AI AppProxy Common package. This tool will streamline the management of your applications by providing an intuitive interface for deploying, managing, and monitoring various types of applications. The goal is to create a user-friendly application that integrates seamlessly with Backend.AI services to manage application proxies efficiently.

Step 1: Set Up Your Environment
- Ensure you have Python installed on your system.
- Install the Backend.AI AppProxy Common package via pip.
- Set up a virtual environment for your project to avoid dependency conflicts.

Step 2: Define Core Features
- **Deployment**: Allow users to deploy applications with different configurations.
- **Management**: Provide options to start, stop, and restart applications.
- **Monitoring**: Implement real-time monitoring capabilities to track the status and performance of deployed applications.

Step 3: Develop the Application
- Use Flask or Django as the web framework for the frontend.
- Utilize the Backend.AI AppProxy Common package to handle backend operations such as authentication, configuration, and communication with Backend.AI services.
- Design a RESTful API to interact with the Backend.AI services for deploying, managing, and monitoring applications.
- Implement user authentication to ensure only authorized users can access and manage their applications.

Step 4: User Interface
- Create a clean and responsive user interface using HTML/CSS/JavaScript.
- Integrate Bootstrap or another CSS framework to enhance the UI.
- Use AJAX to enable dynamic updates without reloading the page.

Step 5: Testing and Deployment
- Write unit tests for both the backend and frontend components.
- Deploy the application on a cloud platform like AWS or Heroku.
- Ensure all functionalities work as expected in a production environment.

Additional Suggestions:
- Add support for multiple environments (e.g., development, staging, production).
- Implement logging to record important events and errors.
- Consider adding documentation for users and administrators.
- Explore integration with other Backend.AI packages for enhanced functionality.

💬 Discussion Feed

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