> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/guardrails/llms.txt.
> For full documentation content, see https://docs.nvidia.com/nemo/guardrails/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/guardrails/_mcp/server.

# Guardrail Catalog

> Reference for pre-built guardrails including content safety, jailbreak detection, topic control, PII handling, agentic security, and third party APIs.

The NeMo Guardrails library ships with a catalog of pre-built guardrails that you can activate out of the box. These guardrails span the most common safety and security concerns in LLM-powered applications from blocking harmful content and detecting jailbreak attempts to masking personally identifiable information and grounding responses in evidence.

Each guardrail is implemented as a configurable rail flow that you add to the `input`, `output`, or `retrieval` section of your `config.yml`. You can use NVIDIA-trained safety models, open-source community models, LLM self-check prompts, or third-party managed APIs, and combine multiple approaches for defense in depth.

Browse the catalog below to find the guardrail that fits your use case.

Reference for pre-built content safety guardrails for protecting against violence, criminal activity, hate speech, sexually explicit content, and similar areas.

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Reference for jailbreak protection guardrails that help prevent adversarial attempts from bypassing safety measures.

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Reference for topic control guardrails that ensure conversations stay within predefined subject boundaries.

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Reference for PII detection guardrails that protect user privacy by detecting and masking sensitive data.

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Reference for agentic security guardrails that protect LLM-based agents using tools and interacting with external systems.

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Reference for fact-checking and hallucination detection guardrails that ensure LLM output is grounded in evidence.

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Reference for LLM self-checking guardrails that prompt the LLM to perform input checking, output checking, or fact-checking.

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Reference for third-party API integrations that connect with managed services for guardrail use cases.

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