AI Analysis
Final verdict: SUSPICIOUS
The package shows some potential for misuse due to its shell execution capabilities, though no concrete evidence of malicious intent was found. Further scrutiny is advised.
- shell risk due to potential interaction with system commands
- maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal and does not indicate any risk.
- Shell: Detection of shell execution suggests potential interaction with system commands, which could be legitimate if the package is designed to interact with PostgreSQL databases via pg_dump. However, it requires further investigation to ensure there's no misuse.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 2.0
Found 1 shell execution pattern(s)
{filepath}") subprocess.run( [ "pg_dump",
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: live.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository adebayopeter/sqlpyhelper appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Adebayo Olaonipekun" 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 SQLPyHelper
Create a simple inventory management system using the Python package 'SQLPyHelper'. This application will allow users to manage a list of items in stock, including adding new items, updating existing ones, deleting items, and searching for specific items based on various criteria. The system should also have the ability to generate reports on inventory levels and notify users when stock levels fall below a certain threshold. Steps to build the application: 1. Set up a basic SQLite database using SQLPyHelper to store item information such as ID, name, description, quantity, and price. 2. Implement functions to add, update, delete, and search for items in the database. 3. Develop a user-friendly command-line interface (CLI) to interact with the inventory management system. 4. Add functionality to generate reports on the current state of the inventory, including total value and average price per item. 5. Integrate alerts that inform the user via console messages when an item's stock falls below a predefined threshold. 6. Ensure data integrity by implementing constraints and validations where necessary. 7. Test the application thoroughly to ensure all functionalities work as expected. 8. Document the code and provide instructions for running the application. Features: - CRUD operations for items (Create, Read, Update, Delete) - Search functionality with filters based on item name, category, or price range - Reporting capabilities to show inventory status, total value, and average price - Alert system to notify low stock levels - User-friendly CLI interface Utilization of 'SQLPyHelper': - Use SQLPyHelper to establish a connection to the SQLite database and perform SQL queries efficiently. - Leverage SQLPyHelper's built-in methods for executing queries, handling transactions, and managing connections to simplify database interactions. - Take advantage of any additional utilities provided by SQLPyHelper to enhance the robustness and performance of the inventory management system.