🤖 AI Analysis

Final verdict: SAFE

The package has low risks across network, shell, obfuscation, and credential fronts. While there are some metadata concerns, they do not strongly indicate malicious activity.

Low network risk
No signs of shell execution
No obfuscation detected
No credential risk
Metadata concerns but no direct evidence of malicious intent

Per-check LLM notes

Network: The observed network calls are likely intended for API interactions, which is normal for an SDK package.
Shell: No shell execution patterns were detected, indicating low risk for direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some red flags such as an author with a missing name and a new or inactive account, but there's no direct evidence of malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 19 test file(s) found

Test runner config found: pyproject.toml
19 test file(s) detected (e.g. test_approval.py)

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://docs.airaproof.com
Detailed PyPI description (6438 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 5.0

Partial type annotation coverage

337 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 6.0

Limited contributor diversity

2 unique contributor(s) across 92 commits in aira-proof/python-sdk
Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

pi_key self._client = httpx.Client( base_url=f"{self.base_url}/api/v1",
self._public_client = httpx.Client( base_url=f"{self.base_url}/api/v1",
pi_key self._client = httpx.AsyncClient( base_url=f"{self.base_url}/api/v1",
self._public_client = httpx.AsyncClient( base_url=f"{self.base_url}/api/v1",

✓ 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: airaproof.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 aira-sdk

Create a mini-application called 'AI Agent Auditor' that leverages the 'aira-sdk' package to manage and monitor AI agents within a secure environment. This application will serve as a comprehensive tool for developers and system administrators to ensure compliance, security, and efficiency of AI agents deployed in their systems. Here’s a detailed breakdown of the project requirements:

1. **User Authentication**: Implement a user authentication system where users can sign up, log in, and log out. Utilize 'aira-sdk' for securing these operations.
2. **Agent Registration**: Allow users to register new AI agents. Each agent should have unique credentials and metadata such as name, description, and capabilities.
3. **Audit Logs**: Maintain detailed logs for all actions performed by registered AI agents, including creation, modification, and deletion of resources. Use 'aira-sdk' to enforce logging policies and ensure data integrity.
4. **Compliance Checks**: Integrate 'aira-sdk' to periodically run compliance checks on AI agents against predefined security and performance standards. Users should receive notifications about any non-compliance issues.
5. **Access Control**: Implement role-based access control (RBAC) using 'aira-sdk' to manage permissions for different types of users and AI agents.
6. **Dashboard**: Develop a dashboard that provides real-time insights into the status of AI agents, including health metrics, recent activity, and compliance status.
7. **Documentation**: Provide comprehensive documentation for the application, detailing how to install, configure, and use it effectively.
8. **Testing**: Ensure thorough testing of the application, focusing on security, functionality, and usability. Use 'aira-sdk' to validate test scenarios and results.

By following these steps and utilizing the core functionalities of 'aira-sdk', you will create a robust and secure platform for managing AI agents, enhancing both productivity and security in AI-driven environments.