asqav-pydantic

v0.1.2 suspicious
4.0
Medium Risk

PydanticAI integration for asqav - cryptographic audit trails for AI agent tool calls

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks but shows signs of potential maintenance issues and low community engagement, raising concerns about its long-term support and security.

  • Metadata risk due to low maintainer activity and engagement
  • Potential supply-chain attack risk due to insufficient community involvement
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating the package does not execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository's low engagement and the maintainer's limited activity suggest potential risks.

📦 Package Quality Overall: Medium (5.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://asqav.com/docs
  • Detailed PyPI description (4037 chars)
○ 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

  • 3 type-annotated function signatures (partial)
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 24 commits in jagmarques/asqav-pydantic
  • Two distinct contributors found

🔬 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: asqav.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 asqav-pydantic 
Your task is to develop a mini-application named 'CryptoAuditTrail' which will serve as a robust tool for managing cryptographic audit trails specifically for AI agent interactions. This application will utilize the 'asqav-pydantic' package to ensure data integrity and secure handling of audit logs. Here’s a detailed plan on how to proceed:

1. **Project Setup**: Start by setting up your Python environment. Install the necessary packages including 'asqav-pydantic', 'pydantic', and any other dependencies required.

2. **Define Data Models**: Use Pydantic models to define the structure of your audit logs. These models should include fields such as timestamp, user ID, action performed, and a hash of the data involved in the action to ensure immutability.

3. **Integration with asqav-pydantic**: Utilize 'asqav-pydantic' to create cryptographic signatures for each log entry. This ensures that every interaction logged cannot be tampered with without detection.

4. **Logging Mechanism**: Implement a logging mechanism where each time an AI agent performs an action, a corresponding log entry is created and signed using the cryptographic functions provided by 'asqav-pydantic'.

5. **Querying and Verification**: Allow users to query the audit trail based on various parameters such as date range, user ID, or specific actions. Additionally, implement a verification function to check the integrity of the logs by validating their signatures.

6. **Security Enhancements**: Consider adding features like encryption for sensitive information within the logs, and secure storage solutions to protect the audit trail from unauthorized access.

7. **User Interface**: Develop a simple command-line interface (CLI) or a basic web interface for interacting with the application. This should allow users to easily view logs, perform queries, and verify entries.

8. **Testing and Documentation**: Finally, thoroughly test your application to ensure all functionalities work as expected. Document your code and provide clear instructions on how to install and use the application.

💬 Discussion Feed

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