🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a low risk profile for common threats like network calls, shell execution, obfuscation, and credential harvesting. However, the metadata risk score is elevated due to its recent creation and lack of an associated GitHub repository, making it suspicious.

Metadata risk score is elevated.
No associated GitHub repository.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external communications.
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 appears to be newly created with limited activity and no associated GitHub repository, 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 "AgentVault Authors" 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 agent-vault-py

Create a web-based application that allows users to securely manage and interact with their AI agents on the Web3 network using the 'agent-vault-py' package. This application should serve as a user-friendly interface for setting up AI agents with unique identities based on ERC-7579/ERC-7715 standards, managing their security through vaults, and facilitating interactions between these agents in a decentralized manner. Here’s a step-by-step guide to building this application:

1. **Setup Environment**: Begin by setting up a Python environment with Flask or Django for the backend and React or Vue.js for the frontend. Ensure you have 'agent-vault-py' installed.
2. **User Authentication**: Implement a user authentication system allowing users to register, log in, and securely store their credentials.
3. **Agent Management**: Enable users to create, delete, and modify AI agents. Each agent should be associated with a unique identity compliant with ERC-7579/ERC-7715 standards.
4. **Secure Execution Layer**: Use 'agent-vault-py' to set up secure execution layers for each agent. These layers will ensure that any interaction or transaction involving the agent is executed securely.
5. **Identity Verification**: Integrate functionality for verifying the identity of agents before they can perform actions or transactions within the application.
6. **Inter-Agent Communication**: Allow agents to communicate and transact with one another in a secure and decentralized manner, leveraging the capabilities of 'agent-vault-py'.
7. **Monitoring & Analytics**: Provide tools for monitoring the activities of agents and generating analytics about their performance and usage.
8. **Documentation & Testing**: Document your code thoroughly and conduct rigorous testing to ensure the application functions as expected.

By following these steps, you'll create a robust platform that leverages the power of 'agent-vault-py' to facilitate secure and efficient interactions among Web3 AI agents.