azure-ai-agentserver-invocations

v1.0.0b4 suspicious
4.0
Medium Risk

Invocations protocol for Azure AI Hosted Agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and lacks author metadata, raising concerns about its origin and intent.

  • Obfuscation risk of 5 out of 10
  • Missing author information and potentially inactive author
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 direct system command execution from the package.
  • Obfuscation: The repeated and fragmented code patterns suggest obfuscation, but without context, it's unclear if this is benign or malicious.
  • Credentials: No clear signs of credential harvesting observed.
  • Metadata: The author's name is missing and they appear to be new or inactive, which raises some suspicion but does not strongly indicate malice.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 24 test file(s) found

  • Test runner config found: conftest.py
  • 24 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (12863 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

  • 134 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)

  • __path__ = __import__("pkgutil").extend_path(__path__, __name__) __path__ = __import__("pkg
  • path__, __name__) __path__ = __import__("pkgutil").extend_path(__path__, __name__) __path__ = __import__("pkg
  • path__, __name__) __path__ = __import__("pkgutil").extend_path(__path__, __name__) # ------------------------
  • app.run() """ __path__ = __import__("pkgutil").extend_path(__path__, __name__) from ._invocation import I
✓ 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: microsoft.com> license-expression: mit

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

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

⚠ 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 azure-ai-agentserver-invocations 
Create a Python-based mini-application named 'AgentTalk' that serves as a simple interface to interact with Azure AI Hosted Agents using the 'azure-ai-agentserver-invocations' package. This application will allow users to send invocation requests to an agent server hosted by Azure and receive responses from it. Here are the key steps and features of the project:

1. **Setup**: Start by installing the necessary packages, including 'azure-ai-agentserver-invocations'. Ensure you have your Azure credentials and endpoint URL ready.
2. **Connection Establishment**: Develop a function that establishes a connection to the Azure AI Hosted Agent server using the provided endpoint URL.
3. **Invocation Requests**: Implement functionality to send invocation requests to the agent server. These requests could include sending text prompts to an AI agent that processes natural language queries or performs specific tasks.
4. **Response Handling**: Design a mechanism to handle responses from the agent server effectively. This includes parsing the response data and presenting it in a user-friendly format.
5. **User Interface**: Create a simple command-line interface (CLI) for users to interact with 'AgentTalk'. Users should be able to input their requests through the CLI and see the responses displayed directly.
6. **Error Handling**: Incorporate robust error handling to manage any issues that may arise during the connection process or while sending/receiving data.
7. **Logging**: Integrate logging capabilities to keep track of all interactions with the agent server, including request details and response statuses.
8. **Documentation**: Provide comprehensive documentation on how to install and use 'AgentTalk', including examples of different types of invocation requests that can be made.

This project leverages the 'azure-ai-agentserver-invocations' package to streamline the interaction between a client application and Azure AI Hosted Agents, showcasing the power and flexibility of Azure's AI services.

💬 Discussion Feed

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