🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in direct malicious activities but raises concerns due to its new release and single-author maintenance, which could potentially hide malicious intent.

Newly released package
Maintained by a single author with limited history

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate signs of malicious activities.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is newly released and maintained by a single author with limited history, raising some suspicion but not definitive evidence of malice.

🔬 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 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Pristan" 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 agentenclave

Create a secure AI-driven personal finance manager application using the 'agentenclave' package. This application will help users track their expenses, manage budgets, and receive personalized financial advice based on their spending habits. The app will be built with privacy and security in mind, leveraging the 'agentenclave' package to ensure that all user data remains confidential and is processed within a secure environment.

Steps to complete the project:
1. Set up a basic Flask web application as the frontend for user interaction.
2. Integrate the 'agentenclave' package to create a secure enclave where all sensitive operations will take place.
3. Design a simple database schema to store user financial data securely within the enclave.
4. Implement functions for adding new transactions, viewing transaction history, and setting monthly budgets.
5. Use machine learning models, also running inside the enclave, to analyze spending patterns and provide customized financial advice.
6. Ensure that all communication between the frontend and the enclave is encrypted and secure.
7. Test the application thoroughly to verify that all data remains within the enclave and is not exposed outside of it.
8. Document the setup process and any unique challenges faced during development.

Suggested Features:
- User authentication and authorization to protect individual user data.
- Real-time alerts for overspending or reaching budget limits.
- Interactive visualizations of spending trends over time.
- Integration with external financial services for automatic data import.
- Detailed analytics dashboard for advanced users to customize settings and view comprehensive reports.