azure-functions-doctor

v0.17.1 suspicious
4.0
Medium Risk

Diagnostic tool for Azure Functions Python v2 programming model

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks in network and shell usage, which could potentially be exploited. However, there's no clear evidence of malicious intent based on the current analysis.

  • moderate network risk
  • moderate shell execution risk
  • incomplete author metadata
Per-check LLM notes
  • Network: The network call is likely for testing purposes, but should be reviewed for its necessity and the security of the endpoint.
  • Shell: Shell execution may be used for functionality checks, but poses higher risk due to potential code injection or privilege escalation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting the package is not likely involved in stealing secrets or credentials.
  • Metadata: The author details are incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 29 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://yeongseon.github.io/azure-functions-doctor/
  • Detailed PyPI description (11987 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
  • 187 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-doctor
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • try: r = requests.get(f"{BASE_URL}/api/HttpExample", timeout=10) if r.
  • rves HTTP traffic.""" r = requests.get(f"{BASE_URL}/api/HttpExample", params={"name": "e2e"}, timeo
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • try: result = subprocess.run( # nosec B603 B607 ["git", "-C", str(path),
  • try: output = subprocess.check_output([func_path, "--version"], text=True, timeout=10) # nosec B6
  • exit code 0.""" result = subprocess.run( [ "azure-functions-doctor",
✓ 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-doctor 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-doctor 
Create a diagnostic utility called 'Azure Function Health Check' using the Python package 'azure-functions-doctor'. This utility will serve as a comprehensive health monitoring tool for Azure Functions written in Python v2. The application should perform the following tasks:

1. **Initialization**: Allow users to input connection details such as the Azure Function App name, resource group, and subscription ID.
2. **Function Scanning**: Automatically scan all functions within the specified Azure Function App to identify any potential issues.
3. **Health Assessment**: Use the 'azure-functions-doctor' package to diagnose each function, looking for common issues like misconfigured bindings, runtime errors, and performance bottlenecks.
4. **Report Generation**: Generate a detailed report summarizing the findings from the health assessment. This report should include recommendations on how to resolve identified issues.
5. **Alerting Mechanism**: Implement a simple alerting system that notifies users via email if critical issues are found during the health check process.
6. **User Interface**: Develop a basic command-line interface (CLI) for user interaction, making it easy for users to run the health checks and view results.
7. **Logging and Persistence**: Ensure that logs of each health check run are stored persistently, either locally or in a cloud-based storage solution.

The 'azure-functions-doctor' package will be central to the health assessment process, providing the diagnostic capabilities necessary to identify and report on issues within Azure Functions. Users of this utility will gain valuable insights into the health and performance of their Azure Functions, enabling them to proactively address issues before they impact service availability.

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