🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks across network, shell, obfuscation, and credential fronts. However, the metadata risk score due to potential inactivity or newness of the package warrants cautious monitoring.

Low risk scores across multiple categories suggest benign use.
Metadata risk indicates the need for further scrutiny regarding the package's activity level and maintenance.

Per-check LLM notes

Network: The network call to localhost suggests internal health checks and is generally benign.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package shows signs of being potentially new or inactive due to the maintainer's lack of history and repository engagement.

📦 Package Quality Overall: Low (4.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_server.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (4482 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

17 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 54 commits in CSOAI-ORG/ai-self-audit-mcp
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

try: resp = urllib.request.urlopen("http://localhost:8000/health", timeout=2)

✓ 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 ai-self-audit-mcp

Develop a Python-based mini-application named 'AI Auditor' that leverages the 'ai-self-audit-mcp' package from MEOK AI Labs to perform comprehensive audits on artificial intelligence systems. The application should provide users with the ability to self-audit their AI models, review past audit conversations, and retrieve certificates of compliance or non-compliance.

Step-by-Step Instructions:
1. Begin by installing the 'ai-self-audit-mcp' package using pip.
2. Design a user-friendly command-line interface (CLI) for interacting with the application.
3. Implement functionality to initiate a self-audit process, which involves feeding the AI model through a series of predefined checks and tests.
4. Ensure the application logs all interactions and results during the audit process, allowing users to review these conversations at a later time.
5. After completing an audit, the application should generate a certificate summarizing the findings, indicating whether the AI model passed or failed the audit.
6. Integrate error handling to manage potential issues such as connection failures or invalid input data.
7. Provide clear documentation explaining how to use each feature of the 'AI Auditor' application.

Suggested Features:
- Support for multiple types of AI models (e.g., classification, regression).
- Customizable audit criteria to cater to different AI system requirements.
- Detailed report generation including visual graphs and charts.
- Integration with cloud storage services for secure backup and sharing of audit logs and certificates.
- Automated scheduling of regular audits for continuous monitoring.

The 'ai-self-audit-mcp' package will be utilized throughout the development process to handle the core functionalities such as initiating audits, logging conversations, and generating certificates. Your task is to create a robust and scalable solution that enhances transparency and accountability in AI deployments.