🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct signs of malicious intent or high-risk activities such as network calls or shell executions. However, it is new, lacks maintainer details, and does not have an associated GitHub repository, which raises concerns about its origin and legitimacy.

Metadata risk due to lack of maintainer information
No associated GitHub repository

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
Shell: No shell execution detected, which is expected and safe.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package is new, lacks maintainer information, and has no associated GitHub repository, raising concerns about its legitimacy.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: 163.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 agent-guardrails-zhuyt

Create a mini-application called 'SafeQuery' that acts as a secure interface between users and a local language model (LLM). This application will use the 'agent-guardrails-zhuyt' package to ensure that queries processed by the LLM adhere to predefined guardrails, enhancing reliability and safety for production workflows. Here’s a detailed plan on how to develop this application:

1. **Setup Environment**: Start by setting up a Python environment where you install the 'agent-guardrails-zhuyt' package along with any necessary dependencies for your local LLM.
2. **Define Guardrails**: Create a set of rules or guardrails that the LLM must follow when processing queries. These could include restrictions on sensitive information, adherence to specific policies, or limitations on response length and complexity.
3. **User Interface**: Develop a simple user interface (UI) through which users can input their queries. This UI can be web-based using Flask or Django, or a command-line interface (CLI).
4. **Integration with LLM**: Integrate your chosen local LLM into the application. Ensure that the LLM can receive inputs from the UI and provide outputs back to it.
5. **Implement Guardrails**: Utilize 'agent-guardrails-zhuyt' to apply the defined guardrails to the LLM’s responses. This involves setting up the package to monitor and adjust the LLM’s output based on the guardrails you’ve established.
6. **Testing and Validation**: Thoroughly test the application with various types of queries to ensure that the guardrails are effectively applied and that the LLM’s responses remain within acceptable parameters.
7. **Deployment**: Once tested successfully, deploy the application either locally or to a server depending on its intended use.

**Suggested Features**:
- A logging system to record all interactions and guardrail applications for auditing purposes.
- An admin panel where guardrails can be modified or new ones added without redeploying the application.
- Real-time feedback to users if their query is outside the allowed scope, guiding them to reformulate it.
- Support for multiple languages to broaden the application’s usability.

By following these steps and incorporating the 'agent-guardrails-zhuyt' package, 'SafeQuery' will not only provide a secure way for users to interact with a local LLM but also demonstrate the practical application of guardrails in real-world scenarios.