AI Analysis
The package has low risks in terms of network usage, shell execution, and code obfuscation. However, its metadata suggests low maintenance and potential low effort, which raises some concerns about its reliability and potential for supply-chain attacks.
- Low maintenance and potential low effort indicated by metadata.
- No significant risks detected in network calls, shell execution, or obfuscation.
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 direct system command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows signs of low maintenance and potential low effort, which could indicate risk.
Package Quality Overall: Low (1.2/10)
No test suite detected
No test files or test-runner configuration detected
No documentation detected
No documentation URL, doc files, or meaningful description found
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked β contributor count unavailable
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
No GitHub repository linked
No GitHub repository link found
4 maintainer concern(s) found
Only one version has ever been released β brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a simple web application using Django that implements a secure login system with rate limiting and blocking capabilities. This application will allow users to register, log in, and log out. Additionally, it will feature enhanced security measures to prevent brute-force attacks by utilizing the 'axes' package. Axes will monitor failed login attempts and block IP addresses after a certain number of consecutive failures. Your task is to design and implement this application from scratch, ensuring it adheres to best practices for web development and security. Hereβs a detailed breakdown of the requirements: 1. **Project Setup**: Start by setting up a new Django project. Ensure you have Django and axes installed. 2. **User Authentication**: Implement user registration and login functionalities. Users should be able to create accounts, log in, and log out securely. 3. **Security Features**: Integrate axes into your application. Configure axes to track failed login attempts and automatically block IP addresses that exceed a specified threshold of consecutive failures. Customize the settings as necessary to fit your applicationβs needs. 4. **Admin Interface**: Utilize Djangoβs admin interface to manage blocked IPs and other security-related information provided by axes. 5. **Logging**: Set up logging to keep track of login attempts, successful and failed, and any actions taken by axes. 6. **Testing**: Write tests to ensure that your application functions correctly under various scenarios, including multiple login attempts and blocked IP handling. 7. **Documentation**: Provide clear documentation on how to set up and use the application, including how to configure axes for different security levels. Your goal is to demonstrate a robust, secure, and user-friendly login system that leverages the power of axes to protect against unauthorized access attempts.
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