azure-monitor-opentelemetry

v1.8.8 safe
2.0
Low Risk

Microsoft Azure Monitor Opentelemetry Distro Client Library for Python

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with low risks across all categories assessed. There is minimal concern for any malicious activity or supply-chain attack.

  • Low network risk
  • No shell execution detected
  • Patterns observed are likely legitimate
Per-check LLM notes
  • Network: The observed network calls to 'https://azure.microsoft.com/' and 'http://example.com' appear to be benign telemetry or health check requests rather than malicious activities.
  • Shell: No shell execution patterns were detected, indicating low risk.
  • Obfuscation: The observed patterns are likely legitimate for extending package paths and do not indicate malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The author has only one package, which may indicate a new or less active account, but no other suspicious flags were found.

📦 Package Quality Overall: Medium (5.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (23224 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 29 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • request") response = requests.get("https://azure.microsoft.com/", timeout=5) try:
  • INTERNAL span response = requests.get("https://azure.microsoft.com/", timeout=5) print("Hello,
  • y captured response = requests.get("https://azure.microsoft.com/", timeout=5) # Set the
  • figuration response = requests.get("http://example.com", timeout=5) logger.warning("Req
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) __path__ = __import__("pkg
  • path__, __name__) __path__ = __import__("pkgutil").extend_path(__path__, __name__) # ------------------------
  • try: module = __import__(module_path, fromlist=[class_name]) instrumentor_imported = True except Exc
✓ 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: microsoft.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Azure/azure-sdk-for-python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corporation" 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-monitor-opentelemetry 
Create a Python-based logging and monitoring tool that leverages the 'azure-monitor-opentelemetry' package to send telemetry data to Azure Monitor. Your application should simulate a simple web service with various endpoints that perform different operations such as adding two numbers, fetching user details, and generating a random number. Each operation should generate logs and metrics which are then sent to Azure Monitor using the OpenTelemetry protocol. Here are the key steps and features of your project:

1. **Setup Environment**: Ensure you have Python installed on your machine. Install necessary packages including 'azure-monitor-opentelemetry', Flask (for building the web service), and any other dependencies required.
2. **Configure Azure Monitor**: Set up an Azure Monitor instance where the telemetry data will be stored. Configure your application to connect to this instance securely.
3. **Build Web Service Endpoints**: Create a Flask app with at least three endpoints - one for adding two numbers, another for fetching simulated user details, and a third for generating random numbers.
4. **Implement Logging and Metrics**: For each endpoint, implement logic that generates logs and metrics. Logs should include information about the request, response time, and any errors encountered. Metrics could include the number of requests processed per minute, average response times, etc.
5. **Integrate 'azure-monitor-opentelemetry'**: Use the 'azure-monitor-opentelemetry' package to configure your application to send these logs and metrics to Azure Monitor. Ensure that all data is properly formatted and adheres to OpenTelemetry standards.
6. **Test Application**: Write tests to ensure each endpoint functions correctly and that logs and metrics are being sent to Azure Monitor as expected. Test under various conditions, including high load scenarios, to validate performance and reliability.
7. **Documentation**: Provide clear documentation on how to set up the environment, run the application, and interpret the data in Azure Monitor. Include examples and explanations for each step of the process.
8. **Security Considerations**: Discuss any security measures taken during the development process, such as securing connections to Azure Monitor and handling sensitive data appropriately.

This project aims to demonstrate the power and flexibility of the 'azure-monitor-opentelemetry' package in real-world applications, while also providing valuable insights into logging and monitoring practices.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!