AI Analysis
Final verdict: SAFE
The package appears safe with minimal risks detected. It does not engage in network calls, shell executions, or any form of obfuscation that could be indicative of malicious intent.
- No network calls
- No shell execution
- No obfuscation patterns
- No credential harvesting
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API access.
- Shell: No shell execution detected, indicating no direct system command execution from the package.
- 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 might indicate a new or less active account, but no other suspicious activities are flagged.
Package Quality Overall: Low (3.2/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (10908 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
18 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
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: ifca.unican.es
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 "Judith Sáinz-Pardo Díaz" 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 anjana
Create a privacy-focused mini-application using the 'anjana' Python package. This application will serve as a simple data anonymization tool for users who wish to share sensitive information without exposing personal details. Here’s a detailed guide on how to build this application: 1. **Project Setup**: Begin by setting up your Python environment and installing the 'anjana' package via pip. Ensure you have a virtual environment set up for managing dependencies. 2. **Application Structure**: Design a clean and user-friendly interface where users can input their data. The application should support various types of data including text, numerical values, and even structured data like CSV files. 3. **Data Anonymization Features**: - Implement a feature to automatically detect sensitive data within the input (e.g., names, email addresses, phone numbers). - Offer different anonymization techniques available in the 'anjana' package such as k-anonymity, l-diversity, t-closeness, etc. - Allow users to choose which anonymization technique they prefer for their specific dataset. 4. **User Interface**: Develop a GUI or CLI (Command Line Interface) for interacting with the application. For simplicity, a CLI might be easier to implement but feel free to explore GUI options if you're comfortable with frameworks like Tkinter or PyQt. 5. **Output Presentation**: After processing, the application should display the anonymized data alongside a brief explanation of how each piece of data was anonymized. Optionally, provide an option to export the anonymized data into a new file format (e.g., CSV, JSON). 6. **Testing and Validation**: Before finalizing the application, ensure thorough testing of all anonymization methods on various types of datasets. Validate that the anonymization process does not leak any identifiable information. 7. **Documentation**: Write comprehensive documentation explaining how to use the application, the importance of data anonymization, and the specific anonymization techniques implemented. 8. **Deployment**: Consider deploying the application online so others can easily access it. If deploying, focus on ensuring that no user data is stored or logged during usage. Throughout the development process, make sure to utilize the 'anjana' package effectively by integrating its core functionalities for detecting and anonymizing sensitive data. This project aims to provide a practical example of how real-world applications can benefit from robust data anonymization tools.