🤖 AI Analysis

Final verdict: SUSPICIOUS

The package abstract-database presents a moderate risk due to its low activity level and potential network risks associated with using requests.get.

Low number of commits and possibly inactive maintainer
Use of requests.get which could be a legitimate feature but requires further verification

Per-check LLM notes

Network: The use of requests.get to fetch images is common for packages that handle web content, but should be reviewed for legitimacy.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has very few commits and the maintainer seems to be new or inactive.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

mage data. response = requests.get(image_url) response.raise_for_status() # Ch
This function directly uses requests.get (without getRequest) to obtain the image, resize it, and
" try: response = requests.get(image_url) response.raise_for_status() image

✓ 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: abstractendeavors.com

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Very few commits: 2 total

Very few commits: 2 total

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "putkoff" 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 abstract-database

Create a Python-based mini-application that manages a simple inventory system using the 'abstract-database' package. This application will allow users to add, update, delete, and view products in a PostgreSQL database. Additionally, it should provide an export feature to convert the current state of the inventory into a CSV file for easy sharing or backup purposes.

The application should follow these steps:
1. Set up the environment by installing 'abstract-database' and any necessary dependencies.
2. Define a Product model using the 'abstract-database' toolkit that includes fields such as product_id, name, description, quantity, and price.
3. Implement CRUD operations (Create, Read, Update, Delete) for managing products within the inventory system.
4. Utilize the 'table helpers' provided by 'abstract-database' to streamline common database tasks like querying and updating records.
5. Add functionality to export the entire inventory to a CSV file using the Pandas export capabilities integrated into 'abstract-database'.
6. Ensure the application supports multiple environments (development, testing, production) by leveraging environment variables for database connection settings.
7. Write tests for the core functionalities to ensure reliability and correctness of the application.

Suggested Features:
- User-friendly command-line interface for interacting with the inventory system.
- Validation checks for input data to prevent errors.
- Support for pagination when listing products to handle large inventories efficiently.
- Optional search functionality to find specific products based on criteria like name or price range.
- Logging mechanisms to track important events and issues within the application.