Guardrail
GUARDRAIL - MCP Security - Gateway for Unified Access, Resource Delegation, and Risk-Attenuating Information Limits
What is Guardrail?
Guardrail is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to guardrail - mcp security - gateway for unified access, resource delegation, and risk-attenuating information limits
GUARDRAIL - MCP Security - Gateway for Unified Access, Resource Delegation, and Risk-Attenuating Information Limits
This server falls under the Security category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- GUARDRAIL - MCP Security - Gateway for Unified Access, Resou
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx guardrailConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Guardrail
GUARDRAIL is a security framework and architectural specification for protecting Large Language Model applications built on the Model Context Protocol. It defines layered security controls covering traditional web security, data and infrastructure protection, LLM-specific defenses (prompt injection, jailbreaking, output sanitization), and Agent/MCP-specific controls such as message classification, trust scoring, and flow control between agent components. The project is currently in active development providing architectural design, technical specifications, and implementation documentation for teams that need to reason about and plan LLM application security holistically.
Prerequisites
- Familiarity with MCP (Model Context Protocol) and LLM application architecture
- Node.js and TypeScript development environment for implementation work
- Understanding of application security fundamentals (authentication, authorization, input validation)
- An MCP client such as Claude Desktop for testing security configurations
Review the GUARDRAIL security model
GUARDRAIL organizes security into four layers: Traditional Web Security (authentication, input validation, transport), Data and Infrastructure Security, LLM Application Security (prompt injection, output sanitization), and Agent/MCP Security (message classification, trust scoring, flow control). Assess your current coverage across these layers before implementing.
Clone the repository and review documentation
Clone the repository to access the architectural specs, security checklists, and implementation documentation. The primary value at this stage is the security framework design rather than runnable code.
git clone https://github.com/nshkrdotcom/GUARDRAIL
cd GUARDRAILEstablish baseline security fundamentals first
GUARDRAIL documentation recommends starting with foundational controls: proper containerization, secrets management (never hardcode API keys), input validation on all LLM inputs, and output sanitization before rendering. These provide more immediate security value than complex framework implementation.
Implement LLM-specific protections
Add targeted protections against the most common LLM attack vectors. Sanitize user inputs before including them in prompts, validate and filter model outputs before using them in downstream operations, and limit the capabilities exposed to agents to only what is necessary.
Configure MCP server access control
When deploying MCP servers, scope tool access using least-privilege principles, validate all tool parameters server-side, log tool invocations for audit, and use separate MCP server instances per trust domain when mixing user-facing and internal tools.
{
"mcpServers": {
"guardrail": {
"command": "node",
"args": ["dist/server.js"],
"env": {
"GUARDRAIL_TRUST_LEVEL": "restricted",
"GUARDRAIL_LOG_LEVEL": "audit"
}
}
}
}Guardrail Examples
Client configuration
Example MCP client configuration for integrating GUARDRAIL as a security middleware layer. Adjust the command to match your build output path.
{
"mcpServers": {
"guardrail": {
"command": "node",
"args": ["/path/to/GUARDRAIL/dist/server.js"],
"env": {
"GUARDRAIL_TRUST_LEVEL": "restricted",
"GUARDRAIL_LOG_LEVEL": "audit"
}
}
}
}Prompts to try
Use GUARDRAIL to reason about and evaluate the security posture of your LLM application stack.
- "Assess the security coverage of my current MCP server setup"
- "What are the prompt injection risks in my agent pipeline and how should I mitigate them?"
- "Review my MCP tool definitions for overly broad permissions"
- "What authentication controls should I add to my LLM application?"Troubleshooting Guardrail
Implementation code is not yet available for all framework components
GUARDRAIL is in active development; the repository currently focuses on architectural design and specifications. Production-ready code components are being released incrementally. Check GitHub Releases and the repository's issue tracker for the latest available implementation artifacts.
Unsure which security layer to prioritize first
GUARDRAIL's own documentation recommends an outside-in approach: establish traditional web security controls (auth, HTTPS, input validation) before implementing LLM-specific or agent-specific controls. Secrets management — never storing API keys in code — should be the first priority.
Framework complexity creates implementation uncertainty
Start with selective adoption of the most relevant GUARDRAIL concepts for your use case rather than implementing the full framework at once. The Information Gateway Layer and trust scoring concepts can be applied incrementally. Document your security decisions against the Application Security Onion model to track coverage.
Frequently Asked Questions about Guardrail
What is Guardrail?
Guardrail is a Model Context Protocol (MCP) server that guardrail - mcp security - gateway for unified access, resource delegation, and risk-attenuating information limits It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Guardrail?
Follow the installation instructions on the Guardrail GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Guardrail?
Guardrail works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Guardrail free to use?
Yes, Guardrail is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
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