nemo-guardrails

You can prevent prompt injection attacks in your LLM application by implementing jailbreak detection and input validation using Colang 2.0 programmable rails, which block unauthorized instruction bypassing at runtime.

The best way to detect and filter PII from LLM outputs is to use a guardrails framework that integrates Presidio, allowing you to detect and mask Personally Identifiable Information to ensure data privacy.

You set up runtime hallucination detection for customer-facing chatbots by installing the nemoguardrails library and configuring fact-checking rails to verify factual accuracy and mitigate false information generation.

Yes, this guardrails framework works with LlamaGuard for toxicity detection. It integrates external tools like Presidio and LlamaGuard alongside Colang 2.0 DSL to identify and block harmful or offensive content.

Yes, you can enforce custom safety policies for LLM input and output validation. The framework leverages Colang 2.0 DSL for programmable rails, ensuring user inputs and LLM outputs adhere to your defined security rules.

A limitation of using Colang 2.0 for LLM security is the requirement to learn its specific DSL syntax for defining programmable rails, and you must install the nemoguardrails Python dependency before implementation.