AI Analysis
Final verdict: SAFE
The package shows minimal risk indicators and does not exhibit any suspicious behavior such as network calls, shell executions, or obfuscation. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not indicate malicious intent.
- No network calls or shell executions detected.
- Low risk of obfuscation and credential harvesting.
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 the package likely does not execute external 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, indicating a potentially 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
No author email provided
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 "Attested Intelligence Holdings LLC" 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 aga-governance
Create a mini-application that allows users to securely manage and track the usage of AI agents through cryptographic governance receipts. This application will serve as a tool to ensure transparency and accountability in AI tool calls by generating, verifying, and storing governance receipts. Here’s a step-by-step guide on how to build this application using the 'aga-governance' package: 1. **Setup Project Environment**: Initialize a new Python project and install the 'aga-governance' package along with other necessary dependencies like Flask for web framework. 2. **User Interface Design**: Develop a simple user interface where users can input details about AI tool calls they wish to make, such as the tool name, parameters, and expected outputs. 3. **Receipt Generation**: Implement functionality within your application to generate cryptographic governance receipts for each AI tool call made. Use 'aga-governance' to create these receipts which include metadata about the tool call. 4. **Storage Solution**: Integrate a storage solution (such as SQLite or PostgreSQL) to securely store these governance receipts. Ensure data integrity and confidentiality. 5. **Verification Module**: Build a module within the application that allows users to verify the authenticity and integrity of stored governance receipts using 'aga-governance'. 6. **Reporting Feature**: Add a reporting feature that generates summaries of tool call activities based on the stored governance receipts, providing insights into usage patterns and compliance. 7. **Security Enhancements**: Incorporate additional security measures such as encryption for sensitive data and secure authentication methods to protect against unauthorized access. 8. **Testing & Deployment**: Thoroughly test all functionalities of the application before deploying it. Consider deploying the application on platforms like Heroku or AWS to make it accessible online. Suggested Features: - Real-time receipt generation and verification - Detailed analytics dashboard for reporting - User-friendly interface for easy interaction - Integration with popular AI tools and services - Compliance with regulatory standards for data privacy and security