armos

v1.5.0 suspicious
6.0
Medium Risk

Automatic PII masking for OpenAI and Anthropic SDKs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package Armos v1.5.0 is flagged as suspicious due to its potential typosquatting behavior targeting 'arrow' and having an author with limited historical contributions.

  • Potential typosquatting targeting 'arrow'
  • Author with limited history
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell executions 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 potential typosquatting and has an author with limited history, raising some concerns.
  • ⚠ Typosquatting target: arrow

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • Test runner config found: pyproject.toml
  • 14 test file(s) detected (e.g. test_base_mixin.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (13499 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

  • 84 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 35 commits in armos-ai/armos-python
  • Single author but highly active (35 commits)

🔬 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 score 3.0

Possible typosquat of: arrow

  • "armos" is 2 edit(s) from "arrow"
✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository armos-ai/armos-python appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 armos 
Develop a privacy-focused data handling tool named 'PIIMasker' using the Python package 'armos'. This tool will automatically mask Personally Identifiable Information (PII) from text inputs before sending them to AI models provided by OpenAI and Anthropic SDKs. The goal is to ensure that sensitive data is protected while still allowing for the analysis and processing of textual information.

### Key Features:
- **User Input Interface**: Allow users to input text via a command-line interface or a simple web form.
- **Automatic PII Detection & Masking**: Utilize the 'armos' package to automatically detect and mask PII such as names, addresses, phone numbers, emails, and other sensitive data.
- **Output Display**: Show the masked version of the text back to the user along with any detected PII types.
- **Integration with AI Models**: Integrate with OpenAI and Anthropic APIs to demonstrate how the masked data can be safely processed without exposing PII.
- **Logging & Reporting**: Keep logs of all inputs and outputs, including timestamps and details of masked data, for auditing purposes.
- **Customization Options**: Provide options for users to specify which types of PII they want to mask, and allow for customization of masking patterns.

### Steps to Develop PIIMasker:
1. **Setup Environment**: Install necessary packages including 'armos', 'openai', and 'anthropic'.
2. **Design User Interface**: Create a simple command-line interface or a basic web form for user interaction.
3. **Implement PII Masking Logic**: Use 'armos' to process the input text and mask PII. Ensure that the output retains readability while protecting sensitive data.
4. **Integrate with AI Models**: Demonstrate the usage of masked data with at least one model from each provider (OpenAI and Anthropic).
5. **Develop Logging Mechanism**: Implement logging to record interactions and masked data for compliance and auditing.
6. **Testing & Validation**: Test the application thoroughly to ensure that it correctly identifies and masks PII and integrates seamlessly with AI services.
7. **Documentation & Deployment**: Write comprehensive documentation for users and deploy the application on a server accessible via the internet.

### Utilizing 'armos':
- Import 'armos' in your Python script to access its functions for PII detection and masking.
- Pass the user input text through 'armos' functions to get the masked output.
- Use 'armos' to customize the masking behavior based on user preferences and requirements.

By following these steps, you'll create a robust tool that enhances privacy and security when dealing with sensitive data in AI applications.

💬 Discussion Feed

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