autotel

v0.4.1 suspicious
6.0
Medium Risk

Write One, Observe Everywhere: OpenTelemetry for Python

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential credential misuse and metadata concerns, despite being generally clean in terms of obfuscation and shell execution risks.

  • credential risk due to environment variable handling
  • metadata issues with the author's profile
Per-check LLM notes
  • Network: The package makes network calls to an external API, which is common but should be verified against the package's intended functionality.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected in the provided code snippet.
  • Credentials: The code snippet checks for environment variables that are commonly used in AWS Lambda functions, which could indicate legitimate usage but also potential credential harvesting depending on how these values are handled.
  • Metadata: The package shows some red flags such as an author with no details and a new/inactive account, but there's no clear evidence of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_examples.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://github.com/jagreehal/autotel-python#readme
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (47123 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
  • 358 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 11 commits in jagreehal/autotel-python
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ... async with httpx.AsyncClient() as client: ... return await client.pos
  • ... res = await httpx.get(f'https://api.example.com/users/{user_id}') ...
  • () ... response = httpx.get(url, headers=headers) ... ctx.set_attribute("htt
  • text() >>> response = httpx.get(url, headers=headers) """ headers: dict[str, str] =
  • }" ... async with httpx.AsyncClient() as client: ... response = await client.get
  • ): ... async with httpx.AsyncClient() as client: ... response = await client.pos
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • """ return bool( os.environ.get("AWS_LAMBDA_FUNCTION_NAME") or os.environ.get("FUNCTION_N
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: jagreehal.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 autotel 
Create a simple web application using Flask that monitors and reports HTTP requests and database queries. This application will serve as a basic content management system (CMS) where users can add, view, and delete blog posts. Utilize the 'autotel' package to automatically instrument your application for observability, allowing you to easily trace HTTP requests and database interactions without manually adding instrumentation code. Here’s a detailed breakdown of the steps and features:

1. **Setup Environment**: Start by setting up a virtual environment and installing necessary packages including Flask for the web framework and SQLAlchemy for ORM-based database interaction. Additionally, install the 'autotel' package which will handle the OpenTelemetry setup.

2. **Design Database Models**: Define models for User and BlogPost using SQLAlchemy. Ensure these models include fields such as title, content, author, and timestamp.

3. **Build Web Application**: Using Flask, create routes for listing all blog posts, viewing a single post, adding a new post, and deleting a post. Each route should interact with the database via the defined models.

4. **Instrumentation with 'autotel'**: Integrate 'autotel' into your Flask app to automatically track HTTP requests and database queries. Configure 'autotel' to send traces to a supported backend like Jaeger or Prometheus.

5. **User Interface**: Develop a simple frontend using HTML/CSS/JavaScript for a user-friendly interface. Ensure CRUD operations (Create, Read, Update, Delete) are accessible through this UI.

6. **Testing & Deployment**: Test your application thoroughly to ensure all functionalities work as expected. Consider deploying your application on a cloud platform like Heroku or AWS to make it publicly accessible.

This project aims to demonstrate the power of 'autotel' in simplifying observability in Python applications, making it easier to monitor and debug complex systems.

💬 Discussion Feed

Leave a comment

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