🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, obfuscation, and credential handling. However, the metadata risk is moderately high due to an anonymous maintainer and low repository activity, raising concerns about its legitimacy.

Moderate metadata risk
Anonymous maintainer and low repository activity

Per-check LLM notes

Network: The observed network patterns are likely legitimate for making HTTP requests, possibly for API interactions, but should be reviewed for destinations and data types exchanged.
Shell: No shell execution patterns detected, suggesting no immediate risk from this aspect.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The package shows some red flags such as an anonymous maintainer and low activity in the git repository, but no clear evidence of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

*kwargs) self._http = httpx.Client(timeout=self._timeout) self.chat = _ChatSync(self)
*kwargs) self._http = httpx.AsyncClient(timeout=self._timeout) self.chat = _ChatAsync(self)
agc_test") client._http = httpx.Client(transport=_mock_transport(handler)) out = client.chat.c
agc_test") client._http = httpx.Client(transport=_mock_transport(handler)) with pytest.raises(

✓ 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: gmail.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 agcms

Create a Python-based mini-application that leverages the 'agcms' package to monitor and ensure compliance with AI governance standards in real-time. This application will serve as a dashboard for developers and compliance officers to track various aspects of AI models in use within their organization. Here are the key functionalities and steps to develop this application:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing the necessary packages, including 'agcms'. Ensure all dependencies are properly installed and configured.

2. **Authentication Mechanism**: Implement a secure authentication mechanism using API keys or OAuth tokens provided by AGCMS. This ensures that only authorized users can access the monitoring data.

3. **Real-Time Monitoring Dashboard**: Develop a real-time dashboard that displays key metrics such as data privacy adherence, model fairness, and overall compliance status. Use 'agcms' functions to fetch and display these metrics dynamically.

4. **Alert System**: Integrate an alert system that notifies users via email or SMS when there is a potential violation of compliance standards. Utilize 'agcms' to define thresholds for alerts and trigger notifications based on real-time data.

5. **Compliance Reports**: Create a feature that generates detailed compliance reports for each AI model. These reports should include historical data and trends, helping organizations maintain long-term compliance.

6. **User Management**: Implement user management capabilities to allow different roles (e.g., developers, compliance officers) to have varying levels of access to the dashboard and its features.

7. **Customizable Compliance Rules**: Allow users to set up customizable compliance rules through a user-friendly interface. These rules can then be enforced and monitored using the 'agcms' package.

8. **Integration Testing**: Conduct thorough integration testing to ensure that all components of the application work seamlessly together and that the 'agcms' package integrates correctly with your application's architecture.

9. **Documentation and Support**: Provide comprehensive documentation and support resources for users of your application, detailing how to install, configure, and effectively utilize the application's features.

Throughout the development process, focus on leveraging the 'agcms' package's core functionalities to enhance the application's ability to monitor and enforce AI governance and compliance standards efficiently.