backend.ai-common

v26.4.3 safe
2.0
Low Risk

Backend.AI commons library

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories with no signs of malicious activity. The low score is slightly influenced by the metadata indicating a possibly new or less active maintainer account.

  • Low network and shell execution risks
  • No obfuscation or credential harvesting attempts detected
  • Single package from maintainer
Per-check LLM notes
  • Network: The network patterns indicate legitimate HTTP requests likely for API interactions, which is common for backend services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution risk.
  • 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 flags are present.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.backend.ai/
  • Detailed PyPI description (5618 chars)
○ 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

  • 469 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 score 3.0

Found 2 network call pattern(s)

  • er_connector() async with aiohttp.ClientSession(connector=connector.connector) as sess: async with s
  • it_per_host, ) return aiohttp.ClientSession( connector=connector, base_url=key.endpoint,
✓ 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-common 
Create a fully functional mini-application that integrates the Backend.AI commons library to manage remote computing resources efficiently. Your application will serve as a simplified interface for users to execute computational tasks on remote servers without needing to know the underlying complexities of server management. Here’s a step-by-step guide to building your application:

1. **Project Setup**: Initialize your Python environment and install the `backend.ai-common` package along with any other necessary dependencies.
2. **Authentication Module**: Develop a secure authentication module using Backend.AI commons library features to allow users to log in and access their computing resources securely. Utilize the library’s capabilities to handle user credentials and session management.
3. **Resource Management Interface**: Implement an intuitive UI/CLI where users can view, start, stop, and manage their remote computing resources. Use Backend.AI commons to interact with the remote servers, handling tasks such as resource allocation, execution, and monitoring.
4. **Task Execution System**: Allow users to submit computational tasks to be executed on remote servers through the app. The system should automatically manage task queues, ensuring efficient use of server resources. Leverage Backend.AI commons for task scheduling and execution.
5. **Monitoring and Logging**: Integrate real-time monitoring and logging functionalities to track the status of running tasks and server health. Backend.AI commons can be used to fetch and display logs and performance metrics.
6. **Customization and Scalability**: Enable users to customize their environments and scale up or down based on their needs. Use Backend.AI commons to dynamically adjust resource allocation according to user requirements.
7. **Documentation and User Guide**: Provide comprehensive documentation and a user guide explaining how to use the application effectively. Include examples and best practices for utilizing Backend.AI commons.

By following these steps, you will create a powerful yet easy-to-use tool that leverages the Backend.AI commons library to simplify remote computing resource management.

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