AI Analysis
Final verdict: SUSPICIOUS
The package has minimal risks associated with network calls, shell executions, obfuscation, and credential harvesting. However, the incomplete metadata and potential inactivity of the maintainer raise concerns that warrant further investigation.
- Maintainer's author name is missing
- Potential inactivity of the maintainer
Per-check LLM notes
- Network: No network calls detected, which is normal for a library focused on database operations without external service dependencies.
- Shell: No shell execution patterns detected, indicating the package does not attempt to execute system commands, which aligns with typical library behavior.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author name is missing and they appear to be new or inactive, which raises some suspicion but not enough to conclusively label it as malicious.
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
Repository penguinolog/sqlalchemy_jsonfield appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 SQLAlchemy-JSONField
Create a simple task management application using Python and SQLAlchemy-JSONField. This application will allow users to create, read, update, and delete tasks. Each task can have additional metadata stored as a dictionary, which will be saved in the database using the JSONField provided by SQLAlchemy-JSONField. ### Application Features: 1. **Task Creation:** Users should be able to add new tasks with a title, description, and any additional metadata they wish to store (e.g., due date, priority). 2. **Task Listing:** Display all tasks with their details. 3. **Task Update:** Allow users to edit any part of the task, including its title, description, and metadata. 4. **Task Deletion:** Provide functionality to remove tasks from the system. 5. **Metadata Storage:** Use the JSONField from SQLAlchemy-JSONField to store complex data structures as part of each task. 6. **User Interface:** Develop a simple command-line interface (CLI) for interacting with the application. ### Steps to Implement: 1. **Setup Environment:** Set up a virtual environment and install necessary packages including `SQLAlchemy`, `SQLAlchemy-JSONField`, and any other required libraries. 2. **Database Configuration:** Configure a SQLite database to store the tasks. Ensure the `JSONField` is properly set up to handle JSON data within the database schema. 3. **Model Definition:** Define a Task model using SQLAlchemy ORM that includes fields for the task title, description, and a JSONField for storing additional metadata. 4. **CRUD Operations:** Implement functions for creating, reading, updating, and deleting tasks. Ensure these functions interact correctly with the JSONField to store and retrieve complex metadata. 5. **CLI Development:** Build a CLI that allows users to perform CRUD operations on tasks. The CLI should also demonstrate how the JSONField can be used to add and retrieve complex data structures. 6. **Testing:** Write tests to ensure the application works as expected, focusing on the correct handling of JSON data. 7. **Documentation:** Document the setup process, usage instructions, and any caveats related to using SQLAlchemy-JSONField in your application.