anonymacy

v0.0.11 suspicious
4.0
Medium Risk

DEPRECATED: anonymacy has been renamed to MaskPipe. Please migrate to the maskpipe package.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, and obfuscation activities, but it exhibits signs of low maintenance and potential lack of transparency in its metadata, raising concerns about its integrity.

  • Low maintenance and potential lack of transparency in metadata.
  • No immediate signs of malicious activities such as network calls or shell executions.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of low maintenance and potential lack of transparency, raising concerns about its integrity.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • 3 test file(s) detected (e.g. test_anonymizer.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 65 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 score 3.0

Suspicious email domain flags: Very short email domain: pm.me>

  • Very short email domain: pm.me>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anonymacy 
Create a data privacy utility called 'PrivacyGuard' using the Python package 'MaskPipe'. This tool will help users protect their sensitive data by automatically detecting and masking personal information in various formats. Here's a detailed plan on how to develop this mini-application:

1. **Project Setup**: Begin by setting up your Python environment and installing the MaskPipe package.
2. **Data Input**: Design a user-friendly interface where users can upload files containing sensitive data or directly input text. Supported file types include CSV, Excel, and plain text.
3. **Detection & Masking**: Utilize MaskPipe's capabilities to detect and mask sensitive information such as names, addresses, phone numbers, email addresses, and credit card numbers. Ensure that the detection algorithm is highly accurate and can handle variations in formatting and language.
4. **Output & Export**: Provide options for users to view the masked data and export it into various formats like CSV, Excel, or plain text.
5. **Customization Options**: Allow users to customize which types of data they want to mask and specify the masking patterns.
6. **Security Measures**: Implement security measures to ensure that no original data is stored or transmitted during the process.
7. **User Interface**: Develop an intuitive UI that guides users through each step of the process, from uploading data to viewing and exporting the masked results.
8. **Testing & Validation**: Rigorously test the application with different datasets to ensure accuracy and reliability.
9. **Documentation & Support**: Create comprehensive documentation and provide support channels for users who encounter issues or have questions.

By following these steps and leveraging the advanced features of MaskPipe, PrivacyGuard will become a powerful tool for anyone looking to safeguard sensitive data.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!