nemo-guardrails

Prevent prompt injection attacks by implementing runtime guardrails that detect jailbreaks and validate inputs against defined safety policies using a Colang 2.0 DSL. This blocks unauthorized behavior modifications before they reach the model.

Filter PII from LLM outputs by applying runtime safety rules that detect and mask Personally Identifiable Information. This protects sensitive user data by ensuring it never appears in the final generated response.

Detect hallucinations in LLM responses by applying runtime fact-checking guardrails. This verifies factual accuracy during generation and reduces the output of false information without modifying the core model.

Use Colang 2.0 to programmatically define input and output validation rules for chatbots. This ensures both user inputs and LLM outputs strictly adhere to your defined safety policies.

Runtime LLM security supports production deployments with GPU acceleration. This allows you to scale safety mechanisms like toxicity detection and jailbreak prevention in high-traffic environments.

Customer service chatbots generate inappropriate content when lacking runtime toxicity detection. Implementing guardrails filters harmful language and blocks unsafe requests, ensuring outputs remain safe and reliable.