azure-storage-queue

v12.17.0 safe
2.0
Low Risk

Microsoft Azure Azure Queue Storage Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe as it shows low risks across all categories, with no indications of malicious activities or supply-chain attacks.

  • Low network risk due to expected communication with Azure Storage Queue service.
  • No signs of shell execution, obfuscation, or credential harvesting.
Per-check LLM notes
  • Network: Expected to communicate with Azure Storage Queue service.
  • Shell: No shell execution patterns detected, which is normal.
  • Obfuscation: The observed patterns are likely related to standard encoding and decoding practices rather than malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The author has only one package, which might indicate a new or less active account, but no other suspicious elements were found.

📦 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 5.0

Some documentation present

  • Detailed PyPI description (37582 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 277 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • ta.encode("utf-8") return base64.b64decode(data) def decode_base64_to_text(data): decoded_bytes =
  • print(" Message: {!r}".format(base64.b64decode(message.content))) except (binascii.Error, ValueErro
  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # --------
✓ 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: microsoft.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corporation" 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 azure-storage-queue 
Create a Python-based mini-application that leverages the 'azure-storage-queue' package to manage tasks in a distributed environment. This application will serve as a simple task queue manager where users can submit tasks, and workers can pick up and process these tasks. The app will be built using Flask for the web interface and will interact with Azure Queue Storage to handle task submission and processing.

### Core Functionality:
1. **Task Submission:** Users should be able to submit new tasks via a web form. Each task will include a unique identifier, description, and priority level.
2. **Task Processing:** Workers can connect to the queue to retrieve and process tasks. Once a task is processed, it should be marked as completed.
3. **Task Monitoring:** Provide a dashboard where users can view all submitted tasks, their current status (queued, in progress, completed), and any errors encountered during processing.

### Suggested Features:
- Implement user authentication to restrict access to only authorized users.
- Add error handling for failed tasks, allowing them to be retried or marked as failed.
- Integrate logging to track task submissions, processing times, and errors.
- Allow users to filter tasks based on status and priority.

### Utilization of 'azure-storage-queue':
- Use the package to create a queue for storing tasks.
- Submit tasks to the queue from the Flask application when they are added.
- Process tasks by reading messages from the queue and marking them as complete once processed.
- Ensure tasks are deleted from the queue after successful processing or moved to a separate 'failed' queue if processing fails.

### Expected Outcome:
By the end of this project, you will have a fully functional task queue manager that demonstrates the use of Azure Queue Storage for managing asynchronous tasks in a distributed system. This project will provide hands-on experience with both Flask for building web applications and Azure Queue Storage for backend task management.

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