🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a low effort level with missing metadata, raising concerns about its origin and purpose. However, it does not present immediate threats like network calls, shell executions, obfuscations, or credential risks.

Missing package description
Low effort and potential lack of transparency

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows signs of low effort and potential lack of transparency, raising suspicion.

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 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)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ablated-vllm-plugin

Create a conversational AI chatbot using the 'ablated-vllm-plugin' package, which serves as an ablation study plugin for vLLM. This chatbot will allow users to engage in natural language conversations, where the chatbot responds intelligently based on the input provided. The project should include the following features:

1. User Interface: Develop a simple and user-friendly interface using a web framework such as Flask or Django. The UI should allow users to enter their queries and display the chatbot's responses.
2. Conversational Flow: Implement a conversational flow where the chatbot maintains context across multiple turns of dialogue. It should remember previous interactions to provide coherent and relevant responses.
3. Customizable Responses: Allow users to customize the chatbot's behavior through configuration files or environment variables. For example, users can specify whether the chatbot should be more formal or casual in its responses.
4. Error Handling: Incorporate robust error handling to manage unexpected inputs or issues gracefully. The chatbot should provide informative messages when it encounters errors.
5. Logging: Integrate logging to record user interactions and chatbot responses for debugging purposes and future improvements.
6. Documentation: Provide comprehensive documentation that explains how to set up the chatbot, use its features, and extend its functionality.

To utilize the 'ablated-vllm-plugin' package, follow these steps:
1. Install the package using pip: `pip install ablated-vllm-plugin`
2. Import the necessary modules from the package in your Python code.
3. Configure the chatbot to use the plugin by setting up the appropriate parameters and configurations as specified in the package's documentation.
4. Integrate the plugin into your chatbot logic to enable advanced conversational capabilities.
5. Test the chatbot thoroughly to ensure that it functions correctly and provides meaningful responses.

Your goal is to create a fully functional mini-app that demonstrates the power and flexibility of the 'ablated-vllm-plugin' package while providing a useful and engaging conversational experience for users.