> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/NVIDIA-NeMo/Guardrails/llms.txt
> Use this file to discover all available pages before exploring further.

# Guardrails Library

> Pre-built guardrails for protecting LLM applications from jailbreaks, hallucinations, sensitive data leaks, and unsafe content.

# Guardrails Library

NeMo Guardrails includes a library of pre-built guardrails that can be activated in any configuration without writing custom code. These guardrails provide enterprise-grade protection against common LLM risks.

## What is the Guardrails Library?

The guardrails library is located in `nemoguardrails/library/` and contains ready-to-use implementations for:

* **Input validation** - Detecting jailbreaks, prompt injections, and unsafe user inputs
* **Output filtering** - Checking for hallucinations, policy violations, and unsafe responses
* **Retrieval validation** - Fact-checking against retrieved documents
* **Dialog control** - Managing conversation flows and enforcing policies
* **Execution safety** - Validating tool calls and action parameters

<Note>
  The library contains pre-built rails that can be activated in any config. You don't need to write custom actions or prompts - just enable them in your `config.yml`.
</Note>

## Categories of Built-in Guardrails

Guardrails are organized by the stage where they execute:

### Input Rails

Execute **before** the LLM processes user input:

* **Jailbreak Detection** - Heuristic and model-based detection
* **Content Safety** - Using models like Llama Guard or NeMoGuard
* **Self Check Input** - LLM-based input validation
* **Sensitive Data Detection** - PII masking with Presidio
* **Prompt Injection Detection** - Protection against injection attacks

### Output Rails

Execute **after** the LLM generates a response:

* **Content Safety** - Validating bot responses
* **Self Check Output** - LLM-based output validation
* **Hallucination Detection** - Self-consistency checking
* **Sensitive Data Masking** - Removing PII from responses

### Retrieval Rails

Execute during **RAG pipelines**:

* **Fact Checking** - Using AlignScore or self-check methods
* **Self Check Facts** - LLM-based factual validation
* **Sensitive Data Detection** - Filtering PII from retrieved chunks

### Dialog Rails

Execute during **multi-turn conversations**:

* **Topic Control** - Enforcing allowed conversation topics
* **Flow Management** - Guided conversation patterns
* **Policy Enforcement** - Custom business rules

### Execution Rails

Execute during **tool/action calls**:

* **Action Input Validation** - Checking parameters
* **Action Output Validation** - Verifying results
* **Tool Call Authorization** - Access control

## Available Integrations

The library includes integrations with third-party services:

| Provider             | Rail Type              | Use Case                       |
| -------------------- | ---------------------- | ------------------------------ |
| **Llama Guard**      | Input/Output           | Meta's content safety model    |
| **NeMoGuard**        | Input/Output           | NVIDIA's content safety models |
| **Presidio**         | Input/Output/Retrieval | Microsoft's PII detection      |
| **AlignScore**       | Retrieval              | Fact checking with alignment   |
| **AutoAlign**        | Output                 | Automated fact verification    |
| **ActiveFence**      | Input/Output           | Content moderation API         |
| **Cleanlab**         | Input/Output           | Data quality checking          |
| **Clavata**          | Input                  | Prompt injection detection     |
| **CrowdStrike AIDR** | Input                  | AI threat detection            |
| **Fiddler**          | Input/Output           | ML monitoring                  |
| **GLiNER**           | Input/Output           | Entity recognition             |
| **Guardrails AI**    | Input/Output           | Guardrails Hub integration     |
| **Pangea**           | Input/Output           | Security platform              |
| **PatronusAI**       | Output                 | LLM evaluation                 |
| **Private AI**       | Input/Output           | PII detection                  |
| **Prompt Security**  | Input                  | Injection detection            |
| **Regex**            | Input/Output           | Pattern matching               |
| **Trend Micro**      | Input/Output           | Security scanning              |

## Quick Start

Enable a built-in guardrail in your `config.yml`:

```yaml theme={null}
models:
  - type: main
    engine: openai
    model: gpt-3.5-turbo

  - type: content_safety
    engine: nim
    model: nvidia/llama-3.1-nemoguard-8b-content-safety

rails:
  input:
    flows:
      - content safety check input $model=content_safety
  output:
    flows:
      - content safety check output $model=content_safety
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Input Rails" icon="shield-check" href="/guardrails/input-rails">
    Validate and sanitize user inputs before processing
  </Card>

  <Card title="Output Rails" icon="filter" href="/guardrails/output-rails">
    Filter and validate LLM responses before delivery
  </Card>

  <Card title="Dialog Rails" icon="message" href="/guardrails/dialog-rails">
    Control conversation flows and enforce policies
  </Card>

  <Card title="Retrieval Rails" icon="database" href="/guardrails/retrieval-rails">
    Validate retrieved knowledge and check facts
  </Card>

  <Card title="Execution Rails" icon="code" href="/guardrails/execution-rails">
    Secure tool calls and action execution
  </Card>
</CardGroup>