atlas-chat

v0.2.0 suspicious
4.0
Medium Risk

Full-stack LLM chat interface with Model Context Protocol (MCP) integration

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some level of obfuscation and has a missing repository, raising concerns about its legitimacy. However, there are no clear signs of malicious activities such as shell execution or credential harvesting.

  • Obfuscation risk due to base64 decoding
  • Missing repository indicating potential lack of transparency
Per-check LLM notes
  • Network: The package makes HTTP requests, which is common for packages that interact with external services or APIs.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The presence of base64 decoding with validation suggests some level of obfuscation but does not necessarily indicate malicious intent.
  • Credentials: No clear patterns indicative of credential harvesting have been detected.
  • Metadata: The repository is not found, and the maintainer has only one package, which could indicate suspicious activity.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/sandialabs/atlas/tree/main/docs
  • Detailed PyPI description (9296 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 224 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 6.0

Found 4 network call pattern(s)

  • try: async with httpx.AsyncClient() as client: headers = {"Authorization": f"
  • try: response = httpx.get(url, timeout=5.0) response.raise_for_status()
  • JSON. """ async with httpx.AsyncClient(timeout=GLOBUS_HTTP_TIMEOUT) as client: resp = await
  • o endpoint.""" async with httpx.AsyncClient(timeout=GLOBUS_HTTP_TIMEOUT) as client: resp = await
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ""): try: base64.b64decode(clean, validate=True) return True except
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ 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 2.0

1 maintainer concern(s) found

  • Author "Sandia National Laboratories" 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 atlas-chat 
Create a fully-functional mini-application called 'ChatMaster' that leverages the 'atlas-chat' Python package to provide users with an intuitive and interactive chat experience. The app should allow users to engage in conversations with AI models that support the Model Context Protocol (MCP), enabling dynamic and context-aware responses. Here’s a detailed breakdown of what your application should include:

1. **User Interface**: Design a clean and user-friendly interface using web technologies like HTML, CSS, and JavaScript. Ensure it supports real-time interaction, allowing users to see their messages and AI responses as they type.
2. **Integration with 'atlas-chat'**: Utilize the 'atlas-chat' package to establish a connection between the front-end UI and the back-end AI models. This involves setting up a server-side script in Python that acts as a bridge, handling requests from the client and passing them to the appropriate AI model through MCP.
3. **AI Model Selection**: Allow users to choose from different AI models available via 'atlas-chat'. Each model should have unique characteristics and capabilities, such as being more conversational, informative, or creative.
4. **Contextual Responses**: Implement MCP to ensure that AI responses are not only relevant but also contextually aware. Users should be able to carry out multi-turn dialogues where the AI remembers previous interactions and adapts its responses accordingly.
5. **Customization Options**: Provide users with options to customize their chat experience, such as changing the theme of the chat interface or adjusting settings related to the AI's behavior.
6. **Error Handling and Feedback**: Incorporate robust error handling mechanisms to manage any issues that might arise during communication between the client and server. Additionally, implement a feedback system where users can rate their interactions with the AI and suggest improvements.
7. **Security Measures**: Ensure that all communications between the client and server are secure, protecting user data and privacy. Use HTTPS and other best practices for securing web applications.
8. **Documentation and Deployment**: Prepare comprehensive documentation explaining how to set up and run the application, including instructions on installing necessary packages and deploying the application on a cloud service like AWS or Heroku.

By following these steps, you will create a versatile and engaging mini-application that showcases the power of integrating advanced AI models into everyday applications.

💬 Discussion Feed

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