This document outlines the security measures and best practices implemented in our LLM-Powered Microservice. It covers authentication, authorization, data protection, and LLM-specific security considerations.

• All API endpoints require an API key for authentication.
• API keys are managed through a secure key management system.
• Keys are rotated regularly and can be revoked at any time.

Implementation:

from fastapi import Security, HTTPException
from fastapi.security import APIKeyHeader

api_key_header = APIKeyHeader(name="X-API-Key")

async def get_api_key(api_key_header: str = Security(api_key_header)):
    if api_key_header == API_KEY:
        return api_key_header
    raise HTTPException(status_code=403, detail="Could not validate credentials")

• Role-based access control (RBAC) is implemented to restrict access to certain endpoints.
• Roles are defined as: Admin, Developer, and User.
• Each role has specific permissions related to LLM usage and management.

Implementation:

from fastapi import Depends

def check_admin(api_key: str = Depends(get_api_key)):
    if not is_admin(api_key):
        raise HTTPException(status_code=403, detail="Admin access required")

• All data in transit is encrypted using TLS 1.3.
• Sensitive data at rest (e.g., API keys, user data) is encrypted using AES-256.

• Only necessary data is collected and stored.
• Personal Identifiable Information (PII) is anonymized when possible.

• LLM input and output data is retained for a maximum of 30 days for debugging purposes.
• Users can request immediate deletion of their data.

• All user inputs are sanitized before being used in LLM prompts.
• A allowlist of allowed characters and patterns is enforced.

Implementation:

import re

def sanitize_input(input_text: str) -> str:
    # Remove any potential control characters or non-printable characters
    sanitized = re.sub(r'[^\x20-\x7E]', '', input_text)
    # Additional sanitization logic...
    return sanitized

• LLM outputs are scanned for potentially sensitive or inappropriate content.
• A content moderation system is in place to flag or filter problematic outputs.

• Strict rate limits are enforced to prevent abuse.
• Limits are set on a per-user and per-endpoint basis.

Implementation:

from fastapi import Request
from fastapi.responses import JSONResponse
from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)

@app.exception_handler(RateLimitExceeded)
async def rate_limit_handler(request: Request, exc: RateLimitExceeded):
    return JSONResponse(status_code=429, content={"detail": "Rate limit exceeded"})

• All API requests and LLM interactions are logged for auditing purposes.
• Anomaly detection systems are in place to identify unusual patterns of usage.
• Regular security audits are conducted on the codebase and infrastructure.

• A detailed incident response plan is in place for handling security breaches.
• The team is trained on the incident response procedures.
• All incidents are documented and reviewed to improve security measures.

• The service is designed to be compliant with GDPR and CCPA regulations.
• Regular compliance audits are conducted.

1. Never hard-code sensitive information (API keys, passwords) in the codebase.
2. Use environment variables for configuration, especially for sensitive data.
3. Regularly update dependencies to patch known vulnerabilities.
4. Follow the principle of least privilege when assigning permissions.
5. Implement proper error handling to avoid information leakage.
6. Use parameterized queries to prevent SQL injection (if applicable).
7. Implement proper session management with secure cookie settings.

By adhering to these security measures and best practices, we aim to provide a secure environment for our LLM-Powered Microservice. Security is an ongoing process, and this document will be regularly reviewed and updated to address new threats and best practices.