apache-airflow-providers-ailake

v0.0.10 suspicious
6.0
Medium Risk

Apache Airflow provider for AI-Lake Format — hook, operators, and snapshot sensor

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to the presence of shell execution risks and the lack of maintainer history and a non-existent git repository.

  • Potential shell execution via subprocess.run
  • No maintainer history or git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: The use of subprocess.run indicates potential shell execution, but without additional context about cmd content and usage, it's hard to determine if it's malicious. It could be part of legitimate functionality.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being potentially malicious due to lack of maintainer history and a non-existent git repository.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_provider.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1203 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

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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 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)

  • ".join(cmd)) result = subprocess.run( cmd, capture_output=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 apache-airflow-providers-ailake 
Create a mini-application that leverages the 'apache-airflow-providers-ailake' package to automate data ingestion from various sources into an AI-Lake, ensuring the data is properly formatted and stored for AI model training. The application will consist of several components:

1. **Data Sources**: Define at least three different data sources (e.g., CSV files, API endpoints, database queries). Each source will have its own specific schema.
2. **Data Ingestion Workflow**: Implement a workflow using Apache Airflow that periodically ingests data from these sources. Use the 'apache-airflow-providers-ailake' package to define custom operators that handle the extraction and transformation of data into the AI-Lake format.
3. **Data Validation**: Integrate data validation steps within the workflow to ensure that incoming data conforms to expected schemas before it is ingested into the AI-Lake.
4. **Snapshot Sensor**: Utilize the snapshot sensor provided by the 'apache-airflow-providers-ailake' package to monitor changes in the AI-Lake and trigger actions based on these changes, such as retraining models or archiving old data.
5. **Visualization Dashboard**: Develop a simple dashboard that visualizes key metrics about the data ingestion process (e.g., number of records ingested per day, error rates).
6. **Documentation and Setup Instructions**: Provide comprehensive documentation and setup instructions for deploying and running the application locally and in a cloud environment.

The goal is to create a robust, scalable system that showcases the capabilities of the 'apache-airflow-providers-ailake' package while providing real-world value in managing and preparing data for AI applications.

💬 Discussion Feed

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