azure-functions-db

v0.4.3 suspicious
4.0
Medium Risk

Database trigger and input/output bindings for Azure Functions Python v2, powered by SQLAlchemy

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risks in terms of network, shell, obfuscation, and credential harvesting activities. However, the metadata risk is elevated due to the maintainer's new or inactive account and lack of detailed author information.

  • Elevated metadata risk due to maintainer's profile
  • Limited package history and author details
Per-check LLM notes
  • Network: The observed network patterns are likely related to normal API calls and testing activities.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account with limited package history and lacks author details, which raises some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 25 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: pyproject.toml
  • 25 test file(s) detected (e.g. test_adapter_sqlalchemy.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://yeongseon.github.io/azure-functions-db-python/
  • Detailed PyPI description (27887 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

  • Classifier: Typing :: Typed
  • 622 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in yeongseon/azure-functions-db-python
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • requests.Response: return requests.get(f"{BASE_URL}{path}", timeout=30, **kwargs) def _post(path:
  • requests.Response: return requests.post(f"{BASE_URL}{path}", timeout=30, **kwargs) @pytest.fixture
  • try: r = requests.get(f"{BASE_URL}/api/health", timeout=10) if r.statu
✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository yeongseon/azure-functions-db-python 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 azure-functions-db 
Create a mini-application that serves as a simple inventory management system using the 'azure-functions-db' Python package. This application will be hosted on Azure Functions and utilize SQLAlchemy for database interactions. The goal of this project is to demonstrate the integration of Azure Functions with databases through triggers and bindings provided by the 'azure-functions-db' package. Here’s a detailed breakdown of the steps and features to implement:

1. **Setup Environment**: Ensure you have an Azure account and the necessary tools installed (Azure CLI, Azure Functions Core Tools). Also, install the 'azure-functions-db' package and set up your local development environment.

2. **Database Setup**: Use Azure SQL Database or Cosmos DB for storing inventory data. Define tables for products (with fields such as product ID, name, description, quantity, price) and transactions (for tracking sales and purchases).

3. **Function App Creation**: Create a new Azure Function App in Python v2. Configure it to use the 'azure-functions-db' package for database interactions.

4. **Implement CRUD Operations**: Develop functions to perform basic CRUD (Create, Read, Update, Delete) operations on the product table. For example, create a function to add a new product, another to update product details, one to delete a product, and another to retrieve all products or specific products based on ID.

5. **Inventory Transactions**: Implement a feature to record transactions. Each transaction could either be a sale or a purchase, affecting the quantity of the product. Use database triggers to automatically adjust the stock levels when a transaction is recorded.

6. **Trigger and Binding Configuration**: Utilize the 'azure-functions-db' package to configure database triggers and bindings. Set up triggers to automatically log transactions and update stock levels whenever a product is added, updated, or deleted. Bindings should allow for easy access to database records within your functions.

7. **User Interface**: Although not required, consider building a simple UI using Flask or any other web framework to interact with the Azure Function API endpoints. This UI should allow users to view the current inventory, make transactions, and manage product information.

8. **Testing and Deployment**: Thoroughly test your application locally before deploying it to Azure. Ensure all CRUD operations work as expected and that transactions are correctly logged and processed. After testing, deploy your Function App to Azure.

By completing this project, you'll gain hands-on experience with Azure Functions, SQLAlchemy, and the 'azure-functions-db' package, showcasing your ability to develop scalable and robust applications leveraging cloud services.

💬 Discussion Feed

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