AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious intent, but its metadata suggests it may be newly created with limited maintainer history and lacks a linked GitHub repository, raising suspicion.
- Metadata risk score of 5 out of 10 due to newness and lack of maintainer history
- No linked GitHub repository
Per-check LLM notes
- Network: No network calls detected, which is normal for a logging package.
- Shell: No shell execution patterns detected, aligning with the expected behavior of a logging package.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
- Metadata: The package is newly created with limited maintainer history and no linked GitHub repository, which raises some concerns.
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: allfly.io
Suspicious Page Links
All external links appear legitimate
Git Repository History
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Allfly" 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 af-fastapi-logging
Create a simple weather forecasting API using FastAPI that integrates with OpenWeatherMap API for real-time weather data. This API will allow users to retrieve current weather conditions for any city around the world. Utilize the 'af-fastapi-logging' package to enhance logging capabilities, ensuring that all API requests and responses are logged effectively for debugging and monitoring purposes.
Steps to follow:
1. Set up a FastAPI project and install necessary dependencies including 'af-fastapi-logging', 'fastapi', 'uvicorn', and 'requests'.
2. Configure 'af-fastapi-logging' to log both INFO and ERROR level messages to a file, with timestamps and request/response details.
3. Create an endpoint '/weather/{city}' that accepts a city name as a path parameter and returns the current weather conditions.
4. Use the OpenWeatherMap API to fetch weather data based on the provided city name.
5. Implement error handling to manage cases where the city name is not found or the API call fails.
6. Ensure that every incoming request and its response are logged using the 'af-fastapi-logging' package, providing insights into the operation of your API.
7. Run your API locally using Uvicorn and test it thoroughly with different cities.
Suggested Features:
- Support for multiple languages in the response.
- Ability to cache responses to improve performance and reduce API calls.
- Optional authentication mechanism to limit API usage.
- Detailed logging of all errors and exceptions to help with troubleshooting.
By following these steps and implementing the suggested features, you'll create a robust, well-documented, and efficient weather forecasting API that leverages the power of 'af-fastapi-logging' for enhanced logging.