apache-airflow-providers-slack

v9.10.0 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minor risks related to credential handling and metadata issues but lacks clear indicators of malicious intent or supply-chain compromise.

  • Low obfuscation risk
  • Moderate credential risk due to environment variable usage
  • Minor metadata concerns
Per-check LLM notes
  • Obfuscation: The observed pattern is a standard method for extending module search paths and does not indicate malicious obfuscation.
  • Credentials: The code snippet shows retrieval of environment variables which may contain sensitive information such as connection IDs and URLs, indicating potential risk if proper handling and protection mechanisms are not in place.
  • Metadata: The package has some minor issues with maintainer history and an insecure link, but no clear signs of malice.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 25 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: conftest.py
  • 25 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-sla
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4031 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
  • 40 type-annotated function signatures detected in source
✦ 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

No suspicious network call patterns found

⚠ 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 score 7.5

Found 3 credential access pattern(s)

  • mple_dag" SLACK_API_CONN_ID = os.environ.get("SLACK_API_CONN_ID", "slack_conn_id") SLACK_CHANNEL = os.environ.ge
  • ack_conn_id") SLACK_CHANNEL = os.environ.get("SLACK_CHANNEL", "#general") IMAGE_URL = ( "https://raw.githubu
  • _dag" SLACK_WEBHOOK_CONN_ID = os.environ.get("SLACK_WEBHOOK_CONN_ID", "slack_default") IMAGE_URL = ( "https:
✓ 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-slack 
Your task is to develop a mini-application that leverages the 'apache-airflow-providers-slack' package to create an automated workflow management system for Slack notifications. This system will allow users to set up tasks within an Airflow DAG (Directed Acyclic Graph), which upon completion or failure, will trigger corresponding Slack messages to notify relevant team members of the status updates. Here’s a step-by-step guide on how to approach this project:

1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with Apache Airflow and the 'apache-airflow-providers-slack' package. You may need to install other dependencies as required.
2. **Create a Slack App**: Before diving into coding, create a Slack app that has the necessary permissions to post messages. Obtain the API token for this app, which will be used to authenticate your Airflow DAGs with Slack.
3. **Design the Workflow**: Design a simple yet effective workflow where different tasks represent various stages of a process. For instance, these could be data extraction, transformation, loading, and validation steps in a typical ETL (Extract, Transform, Load) pipeline.
4. **Implement the DAG**: Using Airflow, implement a DAG that incorporates these tasks. Each task should be designed to perform specific functions within the workflow.
5. **Integrate Slack Notifications**: Utilize the 'apache-airflow-providers-slack' package to integrate Slack notifications into your DAG. Set up operators within your DAG to send messages to Slack at key points such as when a task starts, completes successfully, or fails.
6. **Testing and Deployment**: Once your DAG is implemented, test it thoroughly to ensure all tasks execute as expected and Slack notifications are correctly sent. After successful testing, deploy your DAG to an Airflow server.
7. **Enhancements**: Consider adding enhancements like logging details of each message sent to Slack, customizing notification messages based on task outcomes, or implementing retry mechanisms for failed tasks.

This project not only showcases the power of integrating external services with Airflow but also highlights the importance of real-time communication in modern DevOps environments.

💬 Discussion Feed

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