azure-functions-validation

v0.7.6 safe
4.0
Medium Risk

Validation and serialization layer for Azure Functions Python v2 programming model

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across most categories, with only minor concerns about network and obfuscation activities. There is no strong evidence of malicious intent.

  • Network and metadata risks are moderately high, but not indicative of malicious activity.
  • Potential code obfuscation through compile with exec needs further investigation.
Per-check LLM notes
  • Network: The observed network calls appear to be related to health checks and API interactions, which are common in cloud-based services like Azure Functions.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The use of compile with exec may indicate an attempt to obfuscate code, but without further context, it's unclear if this is malicious or part of a legitimate feature like dynamic code execution.
  • Credentials: No suspicious patterns for credential harvesting were detected.
  • Metadata: The author information is incomplete, which may indicate a lack of transparency.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://yeongseon.github.io/azure-functions-validation-pytho
  • Detailed PyPI description (13307 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
  • 240 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-validation-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.post( f"{BASE_URL}{path}", json=body, hea
  • try: r = requests.get(f"{BASE_URL}/api/health", timeout=10) if r.statu
  • eturns_200() -> None: r = requests.get(f"{BASE_URL}/api/health", timeout=30) assert r.status_co
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • T_ROOT / "README.md") compile(code, "README.md-quick-start", "exec") def test_quickstart_imports_resolve(self) -> None:
✓ 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-validation-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-validation 
Create a simple weather data validation and transformation service using Azure Functions in Python v2, leveraging the 'azure-functions-validation' package. This service will accept incoming weather data from various sources and ensure it adheres to specific criteria before processing it further. Here’s a detailed breakdown of the project steps and features:

1. **Project Setup**: Initialize your project environment with Azure Functions Python v2 and install the necessary packages, including 'azure-functions', 'azure-functions-validation', and 'requests'.

2. **Data Schema Definition**: Define a schema for the expected weather data structure, which includes fields such as temperature, humidity, wind speed, and location.

3. **Function Creation**: Develop an Azure Function that listens for HTTP requests containing weather data. Use 'azure-functions-validation' to validate incoming data against the defined schema. If the data does not conform to the schema, return an error message; otherwise, proceed to the next step.

4. **Transformation Logic**: Implement logic within the function to transform the validated data into a standardized format suitable for storage or further analysis. For example, convert temperatures from Fahrenheit to Celsius if needed.

5. **Error Handling**: Ensure robust error handling is in place to manage unexpected inputs or errors during the validation and transformation processes.

6. **Testing**: Write unit tests to verify the functionality of your validation and transformation logic. Test with both valid and invalid data sets to ensure reliability.

7. **Deployment**: Deploy your Azure Function to Azure cloud services for real-world testing and usage.

**Suggested Features**:
- Support for multiple input formats (JSON, XML).
- Logging of validation errors and successful transformations.
- Integration with an external API to fetch additional weather data for comparison or enrichment.
- Periodic health checks to ensure the function remains operational.

This project will demonstrate how 'azure-functions-validation' simplifies the process of validating and transforming data in Azure Functions, making it easier to handle complex data requirements efficiently.

💬 Discussion Feed

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