approck-fastapi-utils

v0.1.11 suspicious
4.0
Medium Risk

Small helpers for FastAPI: JWT header auth, JSON responses, exception handlers, and Pydantic query types.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is high due to the low engagement of the repository and suspicious maintainer account.

  • High metadata risk due to low repository engagement and suspicious maintainer account
  • Low risks in other categories such as network, shell execution, obfuscation, and credential harvesting
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository has minimal engagement and the maintainer shows signs of a new or throwaway account, raising suspicion.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5560 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
  • Type checker (mypy / pyright / pytype) referenced in project
  • 14 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 3 commits in adalekin/approck-fastapi-utils
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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 score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 3 commit(s) — possibly throwaway account
⚠ 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 approck-fastapi-utils 
Create a mini-application that serves as a simple task management system using FastAPI and the 'approck-fastapi-utils' package. This application will allow users to register, log in, and manage tasks such as adding new tasks, updating existing ones, marking them as completed, and deleting tasks. The application should also include basic user profile management capabilities.

### Key Features:
- **User Authentication**: Implement registration and login functionality using JWT tokens for secure authentication. Utilize the 'approck-fastapi-utils' package for handling JWT in headers.
- **Task Management**: Users should be able to add, update, mark as completed, and delete tasks. Each task should have a title, description, due date, and status.
- **User Profiles**: Allow users to view and edit their profiles including name, email, and password.
- **Exception Handling**: Use 'approck-fastapi-utils' to define custom exception handlers to gracefully handle errors like unauthorized access or missing fields.
- **JSON Responses**: Ensure all API responses are formatted as JSON using the utilities provided by 'approck-fastapi-utils'.

### Steps to Build the Application:
1. **Setup Project Environment**: Create a new Python project and install necessary packages including FastAPI, 'approck-fastapi-utils', and any database adapter of your choice (e.g., SQLAlchemy).
2. **Define Models**: Define models for User and Task using Pydantic schemas provided by 'approck-fastapi-utils'. These models should validate input data ensuring it conforms to expected formats.
3. **Implement User Authentication**: Set up endpoints for user registration and login. Use 'approck-fastapi-utils' to generate and validate JWT tokens for authenticated requests.
4. **Develop Task Management Endpoints**: Create endpoints for CRUD operations on tasks. Include validation and error handling using the utilities from 'approck-fastapi-utils'.
5. **Profile Management**: Add endpoints for managing user profiles. Ensure these endpoints use appropriate authorization checks.
6. **Custom Exception Handlers**: Define custom exception handlers using 'approck-fastapi-utils' to provide meaningful error messages to clients.
7. **Testing**: Write tests for each endpoint to ensure they work as expected under various conditions.
8. **Deployment**: Prepare the application for deployment by setting up environment variables for secrets and configuring the server.

### How 'approck-fastapi-utils' is Used:
- For JWT authentication, use the package's utilities to generate and validate tokens.
- For JSON response formatting, leverage the package's JSON response utilities to ensure consistent output across all endpoints.
- Use Pydantic query types provided by the package to validate and parse incoming request data.
- Implement custom exception handlers using the package's exception handling utilities to catch and respond to errors in a standardized way.

💬 Discussion Feed

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