ai-security-hardening

To protect against prompt injection, this Skill implements input sanitization and guardrails using NeMo Guardrails to prevent malicious prompt manipulation before user input reaches the LLM.

Yes, you can use Presidio to prevent data exfiltration by applying output filtering and PII scrubbing to LLM responses, ensuring sensitive information is removed from generated text.

You can secure LLM API endpoints by implementing rate limiting, JWT token verification, and input validation using PyJWT to control access and prevent API abuse.

The best way to verify model integrity is by scanning model weights for malware and implementing strict access controls to prevent unauthorized model theft in production AI systems.

Yes, this approach works for customer-facing chatbots by sanitizing user inputs and filtering outputs, preventing users from tricking the LLM into revealing sensitive system information or executing unintended commands.

Input validation and output filtering are required for production AI systems to enforce compliance controls, block malicious prompts, and ensure sensitive data is not leaked during LLM interactions.