AI Analysis
Final verdict: SUSPICIOUS
The package shows minimal risk indicators such as no network calls, shell executions, obfuscations, or credential harvesting. However, the metadata risk score is elevated due to the maintainer having only one package, which raises some suspicion.
- No network calls, shell executions, obfuscations, or credential harvesting detected.
- Metadata risk is elevated due to the maintainer having only one package.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities like command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "AndiEcker" 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 ae-core
Develop a fully functional mini-application using the 'ae-core' Python package. This application will serve as a basic task management system where users can create, view, update, and delete tasks. Additionally, implement a feature that allows users to categorize their tasks into different categories such as 'Work', 'Personal', 'Study', etc. Step 1: Setup your development environment and install the necessary packages including 'ae-core'. Step 2: Define the structure of your application, ensuring it leverages 'ae-core' for its core functionalities such as constants, helper functions, and base classes. Step 3: Implement user authentication to ensure only registered users can manage their tasks. Use 'ae-core' helper functions for secure password hashing and storage. Step 4: Create a command-line interface (CLI) for users to interact with the application. Utilize 'ae-core' constants for standardizing input/output messages and formats. Step 5: Develop CRUD operations for tasks within the application, making use of 'ae-core' base classes to handle common patterns and validations. Step 6: Add the ability to categorize tasks. Users should be able to assign each task to a category and filter tasks based on these categories. Step 7: Test the application thoroughly, ensuring all functionalities work as expected and adhere to best practices for security and data handling, leveraging 'ae-core' helper functions for testing and validation. Suggested Features: - User registration and login functionality - Secure password storage and verification - Task creation, viewing, updating, and deletion - Categorization of tasks - Filtering tasks by category - Standardized input/output messages and formats - Testing and validation utilities By following these steps and incorporating the suggested features, you'll create a robust, user-friendly task management system that showcases the capabilities of the 'ae-core' package.