aioq

v0.4.0 suspicious
6.0
Medium Risk

Async job queue with multiple backend support and built-in dashboard

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential typosquatting and low maintainer effort, raising concerns about its legitimacy. While there are no confirmed malicious activities, the possibility of unauthorized data exfiltration through network interactions cannot be disregarded.

  • Potential typosquatting targeting 'arq'
  • Low maintainer effort
  • Possible unauthorized network activity
Per-check LLM notes
  • Network: The detected network patterns suggest the package might be making HTTP requests, which could be part of its intended functionality, but further investigation is needed to ensure it's not being used for unauthorized data exfiltration.
  • Shell: No shell execution patterns were detected.
  • Metadata: The package shows signs of low maintainer effort and potential typosquatting, which raises concerns about its legitimacy and intent.
  • ⚠ Typosquatting target: arq

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

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

Some documentation present

  • Detailed PyPI description (902 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 5.0

Partial type annotation coverage

  • 109 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • "w2"}], ) async with httpx.AsyncClient( transport=httpx.ASGITransport(app=app), base_url="h
  • }, workers=[]) async with httpx.AsyncClient( transport=httpx.ASGITransport(app=app), base_url="h
  • kers=[], ) async with httpx.AsyncClient( transport=httpx.ASGITransport(app=app), base_url="h
  • rkers=workers) async with httpx.AsyncClient( transport=httpx.ASGITransport(app=app), base_url="h
  • shboard(aarq) async with httpx.AsyncClient( transport=httpx.ASGITransport(app=app), base_url="h
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • , 0) await self.redis.eval( # type: ignore[attr-defined] _PROMOTE_DEFERRED
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: arq

  • "aioq" is 2 edit(s) from "arq"
✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aioq 
Develop a fully-functional asynchronous task management system using the Python package 'aioq'. This system will allow users to submit tasks for processing, monitor their status, and view historical task data through a simple web interface. Here are the key steps and features for your project:

1. **Setup Environment**: Begin by setting up a Python environment with aioq installed. Ensure you have the necessary dependencies for web development, such as FastAPI for the backend and any frontend framework of your choice.

2. **Task Submission**: Create a form on the web interface where users can input details of their tasks, including parameters required for execution. These tasks could range from simple computational tasks to more complex operations like image processing or data analysis.

3. **Asynchronous Execution**: Utilize aioq to handle the submission and execution of these tasks asynchronously. Configure aioq to use a suitable backend (e.g., Redis or PostgreSQL) based on your requirements for persistence and scalability.

4. **Status Monitoring**: Implement functionality within aioq to track the status of each task (queued, running, completed, failed). Display this information dynamically on the web interface so users can see real-time updates about their submitted tasks.

5. **Dashboard**: Develop a dashboard feature within aioq that provides insights into overall system performance, such as average task completion time, number of tasks processed per day, and error rates. This dashboard should also allow administrators to manage queues and troubleshoot issues.

6. **User Authentication**: To ensure security and privacy, implement user authentication so only registered users can submit and monitor tasks. Consider using OAuth or JWT tokens for secure session management.

7. **Error Handling & Notifications**: Design the system to gracefully handle errors during task execution and notify users via email or SMS when a task fails. Use aioq's error handling capabilities to capture exceptions and log them appropriately.

8. **Testing & Documentation**: Finally, write comprehensive tests for both the backend and frontend components of your application. Ensure all functionalities are thoroughly tested before deployment. Additionally, create detailed documentation explaining how to set up and use the system, including examples of common tasks and best practices.

By following these steps and incorporating aioq's powerful features, you'll create a robust, scalable, and user-friendly asynchronous task management system.

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