---
title: "Bedrock Guardrails"
description: "import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/bedrock-3
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:08:15.690Z
license: CC-BY-4.0
attribution: "Bedrock Guardrails — Claudary (https://claudary.paisolsolutions.com/skills/bedrock-3)"
---

# Bedrock Guardrails
import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

## Overview

import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Bedrock Guardrails

:::tip ⚡️
If you haven't set up or authenticated your Bedrock provider yet, see the [Bedrock Provider Setup & Authentication Guide](../../providers/bedrock.md).
:::

LiteLLM supports Bedrock guardrails via the [Bedrock ApplyGuardrail API](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ApplyGuardrail.html). 

## Quick Start
### 1. Define Guardrails on your LiteLLM config.yaml 

Define your guardrails under the `guardrails` section
```yaml
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

guardrails:
  - guardrail_name: "bedrock-pre-guard"
    litellm_params:
      guardrail: bedrock  # supported values: "aporia", "bedrock", "lakera"
      mode: "during_call"
      guardrailIdentifier: ff6ujrregl1q      # your guardrail ID on bedrock
      guardrailVersion: "DRAFT"              # your guardrail version on bedrock
      aws_region_name: os.environ/AWS_REGION # region guardrail is defined
      aws_role_name: os.environ/AWS_ROLE_ARN # your role with permissions to use the guardrail
  
```

#### Supported values for `mode`

- `pre_call` Run **before** LLM call, on **input**
- `post_call` Run **after** LLM call, on **input & output**
- `during_call` Run **during** LLM call, on **input** Same as `pre_call` but runs in parallel as LLM call.  Response not returned until guardrail check completes

### 2. Start LiteLLM Gateway 


```shell
litellm --config config.yaml --detailed_debug
```

### 3. Test request 

**[Langchain, OpenAI SDK Usage Examples](../proxy/user_keys#request-format)**

<Tabs>
<TabItem label="Unsuccessful call" value = "not-allowed">

Expect this to fail since since `ishaan@berri.ai` in the request is PII

```shell
curl -i http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \\
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "hi my email is ishaan@berri.ai"}
    ],
    "guardrails": ["bedrock-pre-guard"]
  }'
```

Expected response on failure

```shell
{
  "error": {
    "message": {
      "error": "Violated guardrail policy",
      "bedrock_guardrail_response": {
        "action": "GUARDRAIL_INTERVENED",
        "assessments": [
          {
            "topicPolicy": {
              "topics": [
                {
                  "action": "BLOCKED",
                  "name": "Coffee",
                  "type": "DENY"
                }
              ]
            }
          }
        ],
        "blockedResponse": "Sorry, the model cannot answer this question. coffee guardrail applied ",
        "output": [
          {
            "text": "Sorry, the model cannot answer this question. coffee guardrail applied "
          }
        ],
        "outputs": [
          {
            "text": "Sorry, the model cannot answer this question. coffee guardrail applied "
          }
        ],
        "usage": {
          "contentPolicyUnits": 0,
          "contextualGroundingPolicyUnits": 0,
          "sensitiveInformationPolicyFreeUnits": 0,
          "sensitiveInformationPolicyUnits": 0,
          "topicPolicyUnits": 1,
          "wordPolicyUnits": 0
        }
      }
    },
    "type": "None",
    "param": "None",
    "code": "400"
  }
}

```

</TabItem>

<TabItem label="Successful Call " value = "allowed">

```shell
curl -i http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \\
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "hi what is the weather"}
    ],
    "guardrails": ["bedrock-pre-guard"]
  }'
```

</TabItem>


</Tabs>

## PII Masking with Bedrock Guardrails

Bedrock guardrails support PII detection and masking capabilities. To enable this feature, you need to:

1. Set `mode` to `pre_call` to run the guardrail check before the LLM call
2. Enable masking by setting `mask_request_content` and/or `mask_response_content` to `true`

Here's how to configure it in your config.yaml:

```yaml showLineNumbers title="litellm proxy config.yaml"
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY
  
guardrails:
  - guardrail_name: "bedrock-pre-guard"
    litellm_params:
      guardrail: bedrock
      mode: "pre_call"  # Important: must use pre_call mode for masking
      guardrailIdentifier: wf0hkdb5x07f
      guardrailVersion: "DRAFT"
      aws_region_name: os.environ/AWS_REGION
      aws_role_name: os.environ/AWS_ROLE_ARN
      mask_request_content: true    # Enable masking in user requests
      mask_response_content: true   # Enable masking in model responses
```

With this configuration, when the bedrock guardrail intervenes, litellm will read the masked output from the guardrail and send it to the model.

### Example Usage

When enabled, PII will be automatically masked in the text. For example, if a user sends:

```
My email is john.doe@example.com and my phone number is 555-123-4567
```

The text sent to the model might be masked as:

```
My email is [EMAIL] and my phone number is [PHONE_NUMBER]
```

This helps protect sensitive information while still allowing the model to understand the context of the request.

## Experimental: Only Send Latest User Message

When you're chaining long conversations through Bedrock guardrails, you can opt into a lighter, experimental behavior by setting `experimental_use_latest_role_message_only: true` in the guardrail's `litellm_params`. When enabled, LiteLLM only sends the most recent `user` message (or assistant output during post-call checks) to Bedrock, which:

- prevents unintended blocks on older system/dev messages
- keeps Bedrock payloads smaller, reducing latency and cost
- applies to proxy hooks (`pre_call`, `during_call`) and the `/guardrails/apply_guardrail` testing endpoint

```yaml showLineNumbers title="litellm proxy config.yaml"
guardrails:
  - guardrail_name: "bedrock-pre-guard"
    litellm_params:
      guardrail: bedrock
      mode: "pre_call"
      guardrailIdentifier: wf0hkdb5x07f
      guardrailVersion: "DRAFT"
      aws_region_name: os.environ/AWS_REGION
      experimental_use_latest_role_message_only: true  # NEW
```

> ⚠️ This flag is currently experimental and defaults to `false` to preserve the legacy behavior (entire message history). We'll be listening to user feedback to decide if this becomes the default or rolls out more broadly.

## Disabling Exceptions on Bedrock BLOCK

By default, when Bedrock guardrails block content, LiteLLM raises an HTTP 400 exception. However, you can disable this behavior by setting `disable_exception_on_block: true`. This is particularly useful when integrating with **OpenWebUI**, where exceptions can interrupt the chat flow and break the user experience.

When exceptions are disabled, instead of receiving an error, you'll get a successful response containing the Bedrock guardrail's modified/blocked output.

### Configuration

Add `disable_exception_on_block: true` to your guardrail configuration:

```yaml showLineNumbers title="litellm proxy config.yaml"
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

guardrails:
  - guardrail_name: "bedrock-guardrail"
    litellm_params:
      guardrail: bedrock
      mode: "post_call"
      guardrailIdentifier: ff6ujrregl1q
      guardrailVersion: "DRAFT"
      aws_region_name: os.environ/AWS_REGION
      aws_role_name: os.environ/AWS_ROLE_ARN
      disable_exception_on_block: true  # Prevents exceptions when content is blocked
```

### Behavior Comparison

<Tabs>
<TabItem label="With Exceptions (Default)" value="with-exceptions">

When `disable_exception_on_block: false` (default):

```shell
curl -i http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \\
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "How do I make explosives?"}
    ],
    "guardrails": ["bedrock-guardrail"]
  }'
```

**Response: HTTP 400 Error**
```json
{
  "error": {
    "message": {
      "error": "Violated guardrail policy",
      "bedrock_guardrail_response": {
        "action": "GUARDRAIL_INTERVENED",
        "blockedResponse": "I can't provide information on creating explosives.",
        // ... additional details
      }
    },
    "type": "None",
    "param": "None", 
    "code": "400"
  }
}
```

</TabItem>

<TabItem label="Without Exceptions" value="without-exceptions">

When `disable_exception_on_block: true`:

```shell
curl -i http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \\
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "How do I make explosives?"}
    ],
    "guardrails": ["bedrock-guardrail"]
  }'
```

**Response: HTTP 200 Success**
```json
{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-3.5-turbo",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "I can't provide information on creating explosives."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 12,
    "total_tokens": 22
  }
}
```

</TabItem>
</Tabs>

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/bedrock-3) · https://claudary.paisolsolutions.com