anoman-ai

v0.1.0 suspicious
5.0
Medium Risk

Official Python SDK for the Anoman AI LLM gateway — secure every AI call

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to its untraceable repository, lack of maintainer history, and incomplete author information, which raise concerns about its origin and trustworthiness.

  • Untraceable repository
  • Lack of maintainer history
  • Incomplete author information
Per-check LLM notes
  • Network: The network call to set a base URL and headers is typical for making API requests and does not inherently indicate malicious activity.
  • Shell: No shell execution patterns detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows several red flags including an untraceable repository, lack of maintainer history, and incomplete author information.

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.anoman.io
  • Detailed PyPI description (2161 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 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 24 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • p("/") self._client = httpx.AsyncClient( base_url=self.base_url, headers={
✓ 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: anoman.io>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 anoman-ai 
Create a secure chatbot application using the Anoman AI LLM Gateway SDK ('anoman-ai'). Your task is to design and implement a fully functional mini-app that integrates the core security features of the 'anoman-ai' package to ensure all AI interactions are secure. This application will serve as a platform for users to engage in text-based conversations with an AI-powered chatbot while maintaining the highest standards of data protection.

### Step-by-Step Guide:
1. **Setup Environment**: Begin by setting up your development environment. Ensure you have Python installed along with any necessary libraries like Flask or FastAPI for web integration.
2. **Install Dependencies**: Install the 'anoman-ai' package via pip and any other required dependencies such as Flask or FastAPI.
3. **Design the Application Structure**: Plan out the basic structure of your application. Consider having routes for user authentication, chat session management, and interaction with the AI model through the Anoman AI Gateway.
4. **Implement Authentication**: Utilize 'anoman-ai' to handle secure authentication processes. Ensure that all user sessions are authenticated before they can interact with the chatbot.
5. **Secure AI Calls**: Use 'anoman-ai' to secure all calls made to the underlying AI model. This includes encrypting data, handling tokens securely, and ensuring all interactions are logged for auditing purposes.
6. **Integrate the Chatbot**: Connect your application to the Anoman AI Gateway using the 'anoman-ai' SDK. Implement a simple text-based chat interface where users can input queries and receive responses from the AI model.
7. **Test Security Measures**: Thoroughly test the security measures implemented in your application. Verify that all AI calls are properly secured and that user data remains confidential throughout the interaction process.
8. **Deploy the Application**: Once testing is complete, deploy your application on a server or cloud platform of your choice. Make sure to configure it to use HTTPS for all communications.

### Suggested Features:
- **User Authentication**: Allow users to sign up, log in, and manage their accounts securely.
- **Session Management**: Maintain user sessions to keep track of ongoing conversations.
- **Text-Based Chat Interface**: Provide a simple UI where users can type messages and receive AI-generated responses.
- **Logging and Auditing**: Log all AI interactions for compliance and auditing purposes.
- **Error Handling**: Implement robust error handling to manage unexpected situations gracefully.
- **Customizable Responses**: Offer options for users to customize the behavior of the chatbot based on their preferences.

By following these steps and incorporating the suggested features, you'll create a secure and efficient chatbot application that leverages the power of 'anoman-ai' to protect sensitive information during AI interactions.

💬 Discussion Feed

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