apache-airflow-providers-openfaas

v3.9.4 safe
2.0
Low Risk

Provider package apache-airflow-providers-openfaas for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories, indicating that it is likely safe and does not exhibit any signs of malicious behavior or supply-chain attacks.

  • Low network risk
  • No shell execution detected
  • Standard obfuscation practices
  • No credential harvesting attempts
Per-check LLM notes
  • Network: The observed network patterns are likely legitimate for making HTTP requests to external services, possibly for task execution or service interaction.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern is likely a standard practice for extending module search path and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting have been detected.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • 5 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-ope
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3440 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
  • 5 type-annotated function signatures (partial)
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • , url) response = requests.post(url, body) if response.status_code != OK_STATUS_
  • %s", url) response = requests.post(url, body) if response.ok: self.log.info
  • %s", url) response = requests.put(url, body) if response.status_code != OK_STATUS_CODE
  • tion_name response = requests.get(url) if response.ok: return True
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
✓ 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: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ 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 apache-airflow-providers-openfaas 
Create a fully functional mini-application using Apache Airflow and the 'apache-airflow-providers-openfaas' package to automate the deployment of simple web applications to OpenFaaS. Your application will serve as a CI/CD pipeline, where developers can trigger the deployment process via a GitHub webhook. Here’s a detailed breakdown of what your mini-application should achieve:

1. **GitHub Integration**: Set up a GitHub webhook that listens for pushes to a specific branch (e.g., main). When a push event occurs, the webhook should trigger an Airflow DAG.
2. **DAG Design**: Design an Airflow Directed Acyclic Graph (DAG) that includes tasks for fetching the latest code from the GitHub repository, building a Docker image, and deploying the Docker image to OpenFaaS.
3. **Code Fetching Task**: Implement a task within the DAG that clones the latest version of the code from the GitHub repository.
4. **Docker Image Build Task**: Create a task that builds a Docker image based on the fetched code. Ensure that the Dockerfile is included in the repository.
5. **Deployment Task**: Use the 'apache-airflow-providers-openfaas' package to create a task that deploys the built Docker image to an OpenFaaS cluster.
6. **Logging and Monitoring**: Integrate logging into each task to monitor progress and troubleshoot issues. Additionally, implement a task that checks the status of the deployed function after deployment.
7. **User Interface**: Develop a simple user interface (UI) that allows users to trigger the deployment process manually if needed. This UI should also display the current status of the deployment process.
8. **Security Considerations**: Ensure that all sensitive information, such as GitHub credentials and OpenFaaS API keys, are stored securely. Use environment variables or encrypted secrets within Airflow.
9. **Testing and Validation**: Include a task in the DAG that runs automated tests on the code before proceeding with the deployment process.
10. **Documentation**: Provide comprehensive documentation on setting up the environment, running the DAG, and troubleshooting common issues.

Your mini-application should showcase the capabilities of Apache Airflow in orchestrating complex workflows and the integration with OpenFaaS through the 'apache-airflow-providers-openfaas' package.

💬 Discussion Feed

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