AI Analysis
Final verdict: SAFE
The package shows low risks across all evaluated categories with no network calls, shell executions, or credential harvesting attempts. The only elevated risk is due to the maintainer's limited history with the platform.
- Low network and shell risk
- No signs of obfuscation or credential harvesting
- Maintainer has limited package history
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communication.
- Shell: No shell execution detected, indicating no immediate risk of executing system commands.
- 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.
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 vrcmarcos/elasticmock appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Marcos Cardoso" 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 ElasticMockNew
Your task is to develop a small, fully-functional application that manages a library of books using the 'ElasticMockNew' Python package. This mock Elasticsearch package will simulate real Elasticsearch operations, allowing you to test your application without needing a live Elasticsearch instance. Here’s a detailed breakdown of what your application should achieve: 1. **Application Overview**: Your app should allow users to add, delete, search, and update book records in a simulated Elasticsearch index. 2. **Features**: - Add a new book record with fields such as title, author, publication year, and ISBN. - Search for books based on various criteria including title, author, or publication year. - Update existing book records. - Delete book records. 3. **Utilizing ElasticMockNew**: - Set up a virtual environment and install the 'ElasticMockNew' package along with other necessary Python libraries. - Use 'ElasticMockNew' to create a mock Elasticsearch client that simulates an Elasticsearch cluster. - Implement functions to interact with the mock Elasticsearch client as if it were a real Elasticsearch server. 4. **Development Steps**: - Initialize the project and set up the virtual environment. - Install dependencies including 'ElasticMockNew'. - Create a mock Elasticsearch client instance. - Develop functions to perform CRUD operations on the mock Elasticsearch index. - Write unit tests to ensure your application works correctly with the mock Elasticsearch. 5. **Additional Challenges**: - Implement pagination when searching for books to handle large datasets efficiently. - Allow users to sort search results by different fields such as publication year or title. - Add error handling to gracefully manage cases where a book record might not exist or when data validation fails. 6. **Final Deliverables**: - A fully functional Python application that interacts with a mock Elasticsearch using 'ElasticMockNew'. - Comprehensive documentation explaining how to run the application and use its features. - Unit tests demonstrating the functionality of your application.