backend.ai-cli

v26.4.3 safe
3.0
Low Risk

Backend.AI Command Line Interface Helper

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, no obfuscation, and no credential harvesting. The shell execution risk warrants caution but is likely benign given the nature of a CLI tool.

  • No network calls detected.
  • Low risk of credential harvesting.
  • Potential for shell execution needs monitoring.
Per-check LLM notes
  • Network: No network calls detected, which is normal and not indicative of malicious activity.
  • Shell: Shell execution may be part of the package's functionality but requires further investigation to ensure it does not lead to unauthorized 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 on PyPI, suggesting a new or less active account which could be suspicious but not necessarily malicious.

📦 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 (2013 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

  • 32 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 score 2.0

Found 1 shell execution pattern(s)

  • ript result = subprocess.run( [prog_name], env={*
✓ 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-cli 
Your task is to create a fully functional mini-application that leverages the 'backend.ai-cli' package to manage and monitor a set of computational tasks in a cloud environment. This application will serve as a user-friendly interface for managing tasks such as running machine learning models, data processing jobs, and other compute-intensive operations. Here’s a detailed breakdown of what your application should achieve:

1. **Task Management**: Allow users to submit new tasks to the cloud environment using the 'backend.ai-cli'. These tasks could include running specific scripts, training machine learning models, or executing any other compute-intensive jobs.
2. **Status Monitoring**: Implement a feature that allows users to check the status of their submitted tasks in real-time. This includes details like task ID, start time, end time, and current status (queued, running, completed, failed).
3. **Resource Allocation**: Enable users to specify resource requirements for their tasks, such as CPU cores, memory size, and storage space. The application should then allocate these resources through the 'backend.ai-cli' when submitting the task.
4. **Result Retrieval**: After a task completes successfully, the application should provide a way for users to retrieve the results or outputs from the cloud environment.
5. **Error Handling**: Implement robust error handling mechanisms to gracefully handle situations where tasks fail due to issues like insufficient resources, network errors, or code bugs. The application should notify users about failures and provide useful information for troubleshooting.
6. **User Interface**: Design a simple yet intuitive command-line interface for interacting with the application. Users should be able to easily submit tasks, check statuses, and manage their resources without needing deep technical knowledge.
7. **Security Measures**: Ensure that the application securely handles credentials and sensitive information when interfacing with the cloud environment through 'backend.ai-cli'.

To utilize the 'backend.ai-cli' package effectively, you'll need to familiarize yourself with its core functionalities, such as task submission, status checking, and result retrieval. Your goal is to build an application that not only demonstrates proficiency in using 'backend.ai-cli' but also provides significant value to users by simplifying the process of managing computational tasks in a cloud environment.

💬 Discussion Feed

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