Is Care Membrane easy to integrate into existing LLM workflows?

Yes, built on the Model Context Protocol (MCP), it functions as an MCP server, making it straightforward to integrate with LLM applications and tools like Claude Desktop for comprehensive safety evaluations.

How does Care Membrane assess AI ethical alignment and interaction quality?

Care Membrane scores text for care-centered alignment (0-100), analyzes burnout risk from interaction metrics, predicts 30-day relationship trajectories, and offers a 16-probe certification (Gold/Silver/Bronze/Fail).

What is Care Membrane?

Care Membrane is an AI safety and ethical alignment evaluation toolkit for Large Language Model (LLM) applications, using a unique care-centered framework to ensure responsible and ethical AI interactions.

What types of threats can Care Membrane detect?

It identifies critical threats such as jailbreaks, prompt injection attempts, PII (Personally Identifiable Information) extraction, and coercion attempts within LLM prompts and responses.

Care Membrane

Name: Care Membrane
Author: CSOAI-ORG

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Evaluates AI safety and ethical alignment for LLM applications using a care-centered framework.

The Care Membrane tool provides a comprehensive AI safety evaluation toolkit for Large Language Model (LLM) applications. It scores text for care-centered alignment, detects threats like jailbreaks and prompt injections, analyzes relationship health, predicts burnout risk, and certifies AI responses against the 16-probe Care Membrane framework. Built on the Sovereign Temple care ethics framework, this tool is used in production to evaluate over 75 MCP tools across 47 autonomous agents, ensuring robust and ethically aligned AI interactions.

主要功能

01Score text for care-centered alignment (0-100) with manipulation detection

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03Analyze burnout risk from care, energy, and relationship metrics

04Predict 30-day relationship trajectory for AI interactions

05Detect jailbreaks, prompt injection, PII extraction, and coercion attempts

06Certify LLM responses against the 16-probe Care Membrane framework (Gold/Silver/Bronze/Fail)

使用案例

01Identify and mitigate security threats like prompt injection and PII extraction attempts against LLMs.

02Certify LLM responses for ethical compliance and safety according to the Care Membrane framework.

03Validate the care-centered alignment and detect manipulation in LLM outputs.