LLM Authorization Guide

Introduction

Large Language Models (LLMs) require robust authorization mechanisms to ensure secure operations across different workflows. This guide covers implementing fine-grained authorization for various LLM interactions, from prompt filtering to external service access.

1. Prompt Filtering

Ensures only authorized and validated inputs reach LLM models.

# Example of prompt filtering with authorization using permit.io
from permit.sdk import Permit
 
def filter_prompt(user_id, prompt, role):
    permit = Permit()
    
    # Check if user has permission to use specific prompt types
    if not permit.check(user_id, "use", "ai_prompt", {"role": role}):
        raise UnauthorizedError("User not authorized for this prompt type")
    
    # Validate prompt content
    filtered_prompt = validate_prompt_content(prompt)
    return filtered_prompt

Key Features:

Input validation for both user and system prompts
Role-based access control
Real-time enforcement
Prompt injection prevention

2. RAG Data Protection

Controls access to knowledge bases and vector databases.

# Example of RAG data protection using permit.io
from langchain import RAGRetriever
from permit.sdk import Permit
 
class SecureRAGRetriever(RAGRetriever):
    def __init__(self, base_retriever, user_context):
        self.permit = Permit()
        self.user_context = user_context
        
    async def get_relevant_documents(self, query):
        # Pre-query filtering
        if not self.permit.check(
            self.user_context.user_id,
            "read",
            "knowledge_base",
            {"confidentiality_level": query.metadata.confidentiality}
        ):
            raise UnauthorizedError("Access denied to knowledge base")
            
        docs = await self.base_retriever.get_relevant_documents(query)
        
        # Post-query filtering
        return self.filter_sensitive_data(docs)

Implementation Features:

Pre-query and post-query filtering
Fine-grained data access control
Relationship-based access control (ReBAC)
Audit logging

Advanced RAG Authorization Patterns

Vector Store Query Authorization
- Apply authorization filters at query time
- Use metadata-based access control
- Implement department and role-based segregation
Document-Level Security
- Enforce access control at document level
- Maintain proper classification levels
- Support for multi-tenant data isolation
Policy-Driven Access Control
- Centralized policy management
- Consistent authorization rules
- Support for complex access patterns

3. External Service Access

Manages AI agent interactions with external services.

# Example of secure external service access using permit.io
from permit.sdk import Permit
from typing import Dict
 
class SecureAIAgent:
    def __init__(self, agent_id: str):
        self.permit = Permit()
        self.agent_id = agent_id
        
    async def execute_action(self, action: str, params: Dict):
        # Check agent authorization
        if not self.permit.check(
            self.agent_id,
            action,
            "external_service",
            {"service_type": params.get("service_type")}
        ):
            raise UnauthorizedError(f"Agent not authorized for {action}")
            
        # Execute action with audit logging
        result = await self.perform_action(action, params)
        self.log_action(action, params, result)
        return result

Security Features:

Machine identity management
Action-based authorization
Audit trails
Human-in-the-loop approval workflows

Implementation Example: Role-Based Data Access with Permify Platform

Here’s a complete example using Permify for role-based access control in a RAG system:

entity user {}
 
entity organization {
    relation director @user
    relation member @user
}
 
entity knowledge_base {
    relation parent @organization
    attribute confidentiality_level integer
    
    permission view_director = check_confidentiality(confidentiality_level, 4) and parent.director
    permission view_member = check_confidentiality(confidentiality_level, 1) and parent.member
    
    action view = view_director or view_member
}
 
rule check_confidentiality(level integer, required integer) {
    level <= required
}

Best Practices

Layered Security
- Implement multiple authorization checkpoints
- Combine different access control models (RBAC, ABAC, ReBAC)
- Apply authorization at both vector store and document levels
Audit Logging
- Track all authorization decisions
- Monitor AI agent actions
- Maintain compliance records
- Log vector store query access patterns
Performance Optimization
- Cache authorization decisions
- Implement efficient filtering mechanisms
- Use appropriate indexing for RAG systems
- Optimize vector store query performance

References

🔒 AI Security Safety Ethics 📚 Cookbooks, Courses, and Learning Paths