arrakis-backend-frames

v0.2.0 suspicious
6.0
Medium Risk

Frame backend for the Arrakis server

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package is suspected of having potential network risks due to its communication with external services and incomplete metadata, including missing author details and lack of a GitHub repository.

  • Network risk due to external URL calls
  • Incomplete metadata and author information
Per-check LLM notes
  • Network: Network calls to external URLs suggest the package communicates with an external service, which could be legitimate but requires further investigation into the purpose and security of these communications.
  • Shell: No shell execution patterns were detected, indicating low risk of direct system command execution from this package.
  • Obfuscation: No obfuscation patterns detected, suggesting legitimate use.
  • Credentials: No credential harvesting patterns detected, indicating safe handling of secrets.
  • Metadata: The package has no associated GitHub repository and the author's information is incomplete, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • 12 test file(s) detected (e.g. create_samples.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://docs.ligo.org/ngdd/backends/arrakis-backend-frames
  • 1 documentation file(s) (e.g. gen_ref_nav.py)
  • Brief PyPI description (746 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

  • 116 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 3.0

Found 2 network call pattern(s)

  • : return HttpResponse(requests.post(self.__conn_str + url, data=data, timeout=30)) def get(
  • : return HttpResponse(requests.get(self.__conn_str + url, timeout=30)) class ChanInfoBuilder:
✓ 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

Email domain looks legitimate: ligo.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 arrakis-backend-frames 
Create a real-time data visualization dashboard using Python and the 'arrakis-backend-frames' package. This dashboard will allow users to visualize live data streams from various sources such as sensors, financial markets, or social media feeds. The goal is to provide a dynamic and interactive experience where users can customize their view of the data by selecting different time frames, data sources, and visualization types.

Steps to create the project:
1. Set up a virtual environment and install the necessary packages including 'arrakis-backend-frames'.
2. Use 'arrakis-backend-frames' to establish a backend service that can handle incoming data streams from multiple sources.
3. Design a frontend interface using a Python web framework like Flask or Django to display the visualizations.
4. Implement real-time updates on the dashboard using WebSocket technology to ensure the data is always current.
5. Integrate popular visualization libraries like Plotly or Matplotlib to render graphs and charts based on user preferences.
6. Allow users to select different data sources and adjust the time frame for the data they want to visualize.
7. Add features such as exporting the visualized data into CSV or image formats.
8. Test the application thoroughly to ensure it handles large data volumes efficiently and securely.
9. Deploy the application to a cloud service provider like AWS or Heroku for wider accessibility.

Suggested Features:
- User authentication and authorization to control access to specific data streams.
- Support for multiple visualization types (line graphs, bar charts, pie charts, etc.).
- Customizable alerts based on data thresholds.
- Ability to overlay different data streams on the same chart for comparative analysis.
- Historical data storage and retrieval for trend analysis.

How 'arrakis-backend-frames' is Utilized:
- The package is used to manage and process the incoming data streams efficiently. It provides a robust backend framework that supports real-time data handling and ensures low latency between data ingestion and visualization.
- Users can leverage the package's capabilities to scale the application horizontally as the number of data streams increases, ensuring the system remains responsive even under high load conditions.

💬 Discussion Feed

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