azure-ai-agentserver-responses

v1.0.0b7 suspicious
5.0
Medium Risk

Python SDK for building servers implementing the Azure AI Responses protocol

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation, which could be used to hide malicious activities, despite having low risks in other areas like network and shell execution.

  • High obfuscation risk
  • Single-package maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell execution patterns detected, indicating no suspicious system command executions.
  • Obfuscation: The use of base64 decoding and obfuscated code paths suggests potential for hiding malicious content or logic.
  • Credentials: No clear patterns indicative of credential harvesting were found, but caution is advised.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, raising slight suspicion.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. __init__.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (10681 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

  • 385 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 35 unique contributor(s) across 100 commits in Azure/azure-sdk-for-python
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • 1 :] try: return base64.b64decode(payload) except Exception as exc: raise ValueErr
  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore __path__ =
  • ) # type: ignore __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore # Copyrigh
✓ 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

Repository Azure/azure-sdk-for-python appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Microsoft Corporation License-Expression: MIT" 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 azure-ai-agentserver-responses 
Create a fully-functional mini-application that utilizes the 'azure-ai-agentserver-responses' Python package to implement a simple chatbot server capable of handling conversational requests and providing intelligent responses. Your task is to develop a command-line interface (CLI) chatbot that users can interact with to ask questions or make requests, and receive relevant responses based on pre-defined rules and conditions. Here are the steps and features you need to implement:

1. **Setup**: Install the 'azure-ai-agentserver-responses' package using pip and set up your development environment.
2. **Server Initialization**: Use the package to initialize a server that listens for incoming requests according to the Azure AI Responses protocol.
3. **Request Handling**: Implement logic within the server to handle different types of user inputs and process them accordingly.
4. **Response Generation**: Based on the input received, generate appropriate responses using predefined rules or conditions. For example, if the user asks a question about weather, provide a generic response mentioning sunny or rainy conditions.
5. **Interactive CLI**: Develop a CLI that allows users to type in their queries and see the bot's responses printed out.
6. **Logging**: Integrate logging capabilities to track interactions and debug any issues that arise during testing.
7. **Customization**: Allow for customization of responses through configuration files or command-line arguments.
8. **Testing**: Write tests to ensure that the chatbot responds correctly to various types of inputs and that the server functions as expected.

This project will demonstrate the ability to use the 'azure-ai-agentserver-responses' package to create a basic but functional AI-driven application, showcasing its potential for more complex projects involving natural language processing and AI.

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