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mcp-security

Secure MCP servers and multi-agent pipelines against prompt injection and unauthorized access.

14|1|Updated Dec 5, 2025
Search Tags:#authorization#rbac#multi-agent#mcp#ai security#prompt injection#abac
One-click install
npx skills add https://github.com/jpoutrin/product-forge --skill mcp-security
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-security
Source: https://github.com/jpoutrin/product-forge/tree/main/plugins/security-compliance/skills/mcp-security
Command: npx skills add https://github.com/jpoutrin/product-forge --skill mcp-security

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Securing Multi-Agent Communication Protocol (MCP) servers and multi-agent AI pipelines against prompt injection, unauthorized access, and other vulnerabilities is a critical and evolving challenge. This Skill provides a robust defense architecture.

Core Features & Use Cases

  • 5-Layer Defense Architecture: A comprehensive security model covering Input Validation, Prompt Injection Prevention, SQL/NoSQL Validation, User Context Propagation, and Authorization.
  • Prompt Injection Prevention: Guidance and code examples for sanitizing user inputs and avoiding direct concatenation into prompts.
  • User Context Propagation: Patterns for maintaining user identity and permissions throughout complex multi-agent pipelines.
  • Authorization Patterns: Implementation details for Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC).
  • Use Case: Designing a secure multi-agent AI application, implementing authorization for an AI-driven workflow, or protecting an MCP server from malicious user inputs.

Quick Start

Use the mcp-security skill to generate Python code for sanitizing user input before it's used in an AI prompt.

Frequently Asked Questions about mcp-security

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent prompt injection attacks in multi-agent AI systems?▼

Prompt injection prevention sanitizes user inputs before they enter AI prompts, blocking malicious commands embedded in user data. This Skill provides a 5-layer defense architecture including input validation, prompt-injection-specific sanitization, SQL/NoSQL validation, user context propagation, and RBAC/ABAC authorization with code patterns and a security checklist.

What's the best way to implement authorization in an MCP server or multi-agent pipeline?▼

Implement authorization by enforcing Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC) patterns across your multi-agent pipeline. This Skill covers authorization implementation details, user context propagation to maintain identity and permissions throughout complex workflows, and code patterns for secure MCP servers.

How do I secure user input in AI applications to avoid vulnerabilities?▼

Secure user input through input validation and SQL/NoSQL validation layers before processing. This Skill teaches sanitization techniques to prevent prompt injection and unauthorized access, with practical code examples for generating Python code that validates and sanitizes user input before it enters AI prompts.

Can I apply prompt injection protection to existing AI workflows without redesigning them?▼

Yes, the 5-layer defense architecture applies to existing MCP servers, multi-agent systems, or any pipeline handling user input. You can integrate prompt-injection prevention, user context propagation, and authorization checks incrementally using the provided code patterns and security checklist.

What vulnerabilities does multi-agent communication need protection against?▼

Multi-agent systems face prompt injection, unauthorized access, and SQL/NoSQL injection attacks. This Skill addresses these with a comprehensive defense model covering input validation, prompt-injection prevention, SQL/NoSQL validation, user context propagation, and RBAC/ABAC authorization patterns.

Do I need prior security experience to implement these authorization patterns?▼

The Skill provides code patterns and a security checklist designed for practical implementation, suitable for intermediate implementation depth. It covers RBAC and ABAC fundamentals with concrete examples, making it accessible for developers building secure multi-agent AI applications without deep security expertise.

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