🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, and obfuscation activities. However, it has minimal maintainer history and an inactive repository, which raises concerns about its legitimacy.

Minimal maintainer history
Inactive repository

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network functionality.
Shell: No shell execution detected, which is expected and indicates no immediate signs of malicious shell command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has minimal maintainer history and an inactive repository, raising concerns about its legitimacy.

🔬 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: meok.ai>

✓ 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 agent-policy-enforcement-mcp

Create a mini-application that simulates a microservices architecture where different services communicate with each other under strict policy enforcement. This application will use the 'agent-policy-enforcement-mcp' package to ensure that all inter-service communications adhere to predefined security policies. Your goal is to develop a system where service requests are intercepted, evaluated against these policies, and either allowed or denied based on the outcome. Here's a detailed breakdown of your task:

1. **Setup**: Begin by installing the 'agent-policy-enforcement-mcp' package and setting up a basic microservices environment using Docker Compose or similar.
2. **Define Services**: Create three distinct microservices: 'Orchestrator', 'Billing', and 'Storage'. Each service will have its own container and will communicate over HTTP.
3. **Policy Definition**: Using the 'agent-policy-enforcement-mcp' package, define policies such as 'The Orchestrator can call Billing only if the transaction amount is less than 1000'. These policies should be stored in a configuration file.
4. **Policy Enforcement**: Implement a middleware in each service that uses the 'evaluate_call' function from 'agent-policy-enforcement-mcp' to check incoming requests against the defined policies before processing them.
5. **Evidence Collection**: Ensure that the system generates signed policy-decision attestations as per the EU AI Act Art 14 and ISO 42001 Annex A.7 requirements. Store these attestations in a secure log.
6. **Testing**: Develop a series of test cases to validate the behavior of the system under various conditions. For example, test what happens when the Orchestrator tries to call the Billing service with amounts both below and above the threshold.
7. **Documentation**: Write comprehensive documentation explaining how to set up the application, how the policy enforcement works, and how to interpret the logs.
8. **Security Audit**: Conduct a mock security audit of the system, focusing on the effectiveness of the policy enforcement mechanism and the integrity of the evidence collection process.

This project aims to showcase the capabilities of 'agent-policy-enforcement-mcp' in a practical scenario, ensuring that all inter-service communications are secure and compliant with relevant regulations.