758 Quick Start 18c22f02

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# Guardrails - Quick Start

Setup Prompt Injection Detection, PII Masking on LiteLLM Proxy (AI Gateway)

## 1. Define guardrails on your LiteLLM config.yaml

Set 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: general-guard
    litellm_params:
      guardrail: aim
      mode: [pre_call, post_call]
      api_key: os.environ/AIM_API_KEY
      api_base: os.environ/AIM_API_BASE
      default_on: true # Optional
  
  - guardrail_name: "aporia-pre-guard"
    litellm_params:
      guardrail: aporia  # supported values: "aporia", "lakera"
      mode: "during_call"
      api_key: os.environ/APORIA_API_KEY_1
      api_base: os.environ/APORIA_API_BASE_1
  - guardrail_name: "aporia-post-guard"
    litellm_params:
      guardrail: aporia  # supported values: "aporia", "lakera"
      mode: "post_call"
      api_key: os.environ/APORIA_API_KEY_2
      api_base: os.environ/APORIA_API_BASE_2
    guardrail_info: # Optional field, info is returned on GET /guardrails/list
      # you can enter any fields under info for consumers of your guardrail
      params:
        - name: "toxicity_score"
          type: "float"
          description: "Score between 0-1 indicating content toxicity level"
        - name: "pii_detection"
          type: "boolean"

# Example Presidio guardrail config with entity actions + confidence score thresholds
  - guardrail_name: "presidio-pii"
    litellm_params:
      guardrail: presidio
      mode: "pre_call"
      presidio_language: "en"
      pii_entities_config:
        CREDIT_CARD: "MASK"
        EMAIL_ADDRESS: "MASK"
        US_SSN: "MASK"
      presidio_score_thresholds:  # minimum confidence scores for keeping detections
        CREDIT_CARD: 0.8
        EMAIL_ADDRESS: 0.6

# Example Pillar Security config via Generic Guardrail API
  - guardrail_name: "pillar-security"
    litellm_params:
      guardrail: generic_guardrail_api
      mode: [pre_call, post_call]
      api_base: https://api.pillar.security/api/v1/integrations/litellm
      api_key: os.environ/PILLAR_API_KEY
      additional_provider_specific_params:
        plr_mask: true
        plr_evidence: true
        plr_scanners: true
```


### Supported values for `mode` (Event Hooks)

- `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
- A list of the above values to run multiple modes, e.g. `mode: [pre_call, post_call]`

### Load Balancing Guardrails

Need to distribute guardrail requests across multiple accounts or regions? See [Guardrail Load Balancing](./guardrail_load_balancing.md) for details on:
- Load balancing across multiple AWS Bedrock accounts (useful for rate limit management)
- Weighted distribution across guardrail instances
- Multi-region guardrail deployments


## 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": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```

Expected response on failure

```shell
{
  "error": {
    "message": {
      "error": "Violated guardrail policy",
      "aporia_ai_response": {
        "action": "block",
        "revised_prompt": null,
        "revised_response": "Aporia detected and blocked PII",
        "explain_log": null
      }
    },
    "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": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```

</TabItem>


</Tabs>


## **Default On Guardrails**

Set `default_on: true` in your guardrail config to run the guardrail on every request. This is useful if you want to run a guardrail on every request without the user having to specify it.

**Note:** These will run even if user specifies a different guardrail or empty guardrails array.

```yaml
guardrails:
  - guardrail_name: "aporia-pre-guard"
    litellm_params:
      guardrail: aporia
      mode: "pre_call"
      default_on: true
```

**Test Request**

In this request, the guardrail `aporia-pre-guard` will run on every request because `default_on: true` is set.


```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"}
    ]
  }'
```

**Expected response**

Your response headers will include `x-litellm-applied-guardrails` with the guardrail applied 

```
x-litellm-applied-guardrails: aporia-pre-guard
```

### Guardrail Policies

Need more control? Use [Guardrail Policies](./guardrail_policies.md) to:
- Group guardrails into reusable policies
- Enable/disable guardrails for specific teams, keys, or models
- Inherit from existing policies and override specific guardrails

## **Using Guardrails Client Side**

### Test yourself **(OSS)**

Pass `guardrails` to your request body to test it


```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": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```

### Expose to your users **(Enterprise)**

Follow this simple workflow to implement and tune guardrails:

### 1. View Available Guardrails

First, check what guardrails are available and their parameters:


Call `/guardrails/list` to view available guardrails and the guardrail info (supported parameters, description, etc)

```shell
curl -X GET 'http://0.0.0.0:4000/guardrails/list'
```

Expected response

```json
{
    "guardrails": [
        {
        "guardrail_name": "aporia-post-guard",
        "guardrail_info": {
            "params": [
            {
                "name": "toxicity_score",
                "type": "float",
                "description": "Score between 0-1 indicating content toxicity level"
            },
            {
                "name": "pii_detection",
                "type": "boolean"
            }
            ]
        }
        }
    ]
}
```

>
This config will return the `/guardrails/list` response above. The `guardrail_info` field is optional and you can add any fields under info for consumers of your guardrail
>
```yaml
- guardrail_name: "aporia-post-guard"
    litellm_params:
      guardrail: aporia  # supported values: "aporia", "lakera"
      mode: "post_call"
      api_key: os.environ/APORIA_API_KEY_2
      api_base: os.environ/APORIA_API_BASE_2
    guardrail_info: # Optional field, info is returned on GET /guardrails/list
      # you can enter any fields under info for consumers of your guardrail
      params:
        - name: "toxicity_score"
          type: "float"
          description: "Score between 0-1 indicating content toxicity level"
        - name: "pii_detection"
          type: "boolean"
```


### 2. Apply Guardrails
Add selected guardrails to your chat completion request:
```shell
curl -i http://localhost:4000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "your message"}],
    "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```

### 3. Test with Mock LLM completions

Send `mock_response` to test guardrails without making an LLM call. More info on `mock_response` [here](../../completion/mock_requests)

```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"}
    ],
    "mock_response": "This is a mock response",
    "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```


### 4. ✨ Pass Dynamic Parameters to Guardrail

:::info

✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)

:::

Use this to pass additional parameters to the guardrail API call. e.g. things like success threshold. **[See `guardrails` spec for more details](#spec-guardrails-parameter)**


<Tabs>

<TabItem value="openai" label="OpenAI Python v1.0.0+">

Set `guardrails={"aporia-pre-guard": {"extra_body": {"success_threshold": 0.9}}}` to pass additional parameters to the guardrail

In this example `success_threshold=0.9` is passed to the `aporia-pre-guard` guardrail request body

```python
import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ],
    extra_body={
      "guardrails": [
        "aporia-pre-guard": {
          "extra_body": {
            "success_threshold": 0.9
          }
        }
      ]
    }

)

print(response)
```
</TabItem>


<TabItem value="Curl" label="Curl Request">

```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
    "guardrails": [
      "aporia-pre-guard": {
        "extra_body": {
          "success_threshold": 0.9
        }
      }
    ]
}'
```
</TabItem>


</Tabs>




## **Proxy Admin Controls**

### Monitoring Guardrails

Monitor which guardrails were executed and whether they passed or failed. e.g. guardrail going rogue and failing requests we don't intend to fail

:::

#### Setup

1. Connect LiteLLM to a [supported logging provider](../logging)
2. Make a request with a `guardrails` parameter
3. Check your logging provider for the guardrail trace

#### Traced Guardrail Success

<Image img={require('../../../img/gd_success.png')} />

#### Traced Guardrail Failure

<Image img={require('../../../img/gd_fail.png')} />




### ✨ Control Guardrails per API Key

:::info

✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)

:::

Use this to control what guardrails run per API Key. In this tutorial we only want the following guardrails to run for 1 API Key
- `guardrails`: ["aporia-pre-guard", "aporia-post-guard"]

**Step 1** Create Key with guardrail settings

<Tabs>
<TabItem value="/key/generate" label="/key/generate">

```shell
curl -X POST 'http://0.0.0.0:4000/key/generate' \
    -H 'Authorization: Bearer sk-1234' \
    -H 'Content-Type: application/json' \
    -d '{
            "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
        }
    }'
```

</TabItem>
<TabItem value="/key/update" label="/key/update">

```shell
curl --location 'http://0.0.0.0:4000/key/update' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
        "key": "sk-jNm1Zar7XfNdZXp49Z1kSQ",
        "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
        }
}'
```

</TabItem>
</Tabs>

**Step 2** Test it with new key

```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-jNm1Zar7XfNdZXp49Z1kSQ' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "my email is ishaan@berri.ai"
        }
    ]
}'
```

### ✨ Tag-based Guardrail Modes

:::info

✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)

:::

Run guardrails based on the user-agent header. This is useful for running pre-call checks on OpenWebUI but only masking in logs for Claude CLI.

```yaml
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

guardrails:
  - guardrail_name: "guardrails_ai-guard"
    litellm_params:
      guardrail: guardrails_ai
      guard_name: "pii_detect" # 👈 Guardrail AI guard name
      mode:
        tags:
            "User-Agent: claude-cli": "logging_only"                 # Claude CLI - only mask in logs
        default: "pre_call"               # Default mode when no tags match
      api_base: os.environ/GUARDRAILS_AI_API_BASE # 👈 Guardrails AI API Base. Defaults to "http://0.0.0.0:8000"
      default_on: true # run on every request
```


### ✨ Model-level Guardrails

:::info

✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)

:::


This is great for cases when you have an on-prem and hosted model, and just want to run prevent sending PII to the hosted model.


```yaml
model_list:
  - model_name: claude-sonnet-4
    litellm_params:
      model: anthropic/claude-sonnet-4-20250514
      api_key: os.environ/ANTHROPIC_API_KEY
      api_base: https://api.anthropic.com/v1
      guardrails: ["azure-text-moderation"]
  - model_name: openai-gpt-4o
    litellm_params:
      model: openai/gpt-4o

guardrails:
  - guardrail_name: "presidio-pii"
    litellm_params:
      guardrail: presidio  # supported values: "aporia", "bedrock", "lakera", "presidio"
      mode: "pre_call"
      presidio_language: "en"  # optional: set default language for PII analysis
      pii_entities_config:
        PERSON: "BLOCK"  # Will mask credit card numbers
  - guardrail_name: azure-text-moderation
    litellm_params:
      guardrail: azure/text_moderations
      mode: "post_call" 
      api_key: os.environ/AZURE_GUARDRAIL_API_KEY
      api_base: os.environ/AZURE_GUARDRAIL_API_BASE 
```

### ✨ Disable team from turning on/off guardrails

:::info

✨ This is an Enterprise only feature [Get a free trial](https://www.litellm.ai/enterprise#trial)

:::


#### 1. Disable team from modifying guardrails 

```bash
curl -X POST 'http://0.0.0.0:4000/team/update' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "team_id": "4198d93c-d375-4c83-8d5a-71e7c5473e50",
    "metadata": {"guardrails": {"modify_guardrails": false}}
}'
```

#### 2. Try to disable guardrails for a call 

```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $LITELLM_VIRTUAL_KEY' \
--data '{
"model": "gpt-3.5-turbo",
    "messages": [
      {
        "role": "user",
        "content": "Think of 10 random colors."
      }
    ],
    "metadata": {"guardrails": {"hide_secrets": false}}
}'
```

#### 3. Get 403 Error

```
{
    "error": {
        "message": {
            "error": "Your team does not have permission to modify guardrails."
        },
        "type": "auth_error",
        "param": "None",
        "code": 403
    }
}
```

Expect to NOT see `+1 412-612-9992` in your server logs on your callback. 

:::info
The `pii_masking` guardrail ran on this request because api key=sk-jNm1Zar7XfNdZXp49Z1kSQ has `"permissions": {"pii_masking": true}`
:::


## Specification 

### `guardrails` Configuration on YAML

```yaml
guardrails:
  - guardrail_name: string     # Required: Name of the guardrail
    litellm_params:            # Required: Configuration parameters
      guardrail: string        # Required: One of "aporia", "bedrock", "guardrails_ai", "lakera", "presidio", "hide-secrets"
      mode: Union[string, List[string], Mode]             # Required: One or more of "pre_call", "post_call", "during_call", "logging_only"
      api_key: string          # Required: API key for the guardrail service
      api_base: string         # Optional: Base URL for the guardrail service
      default_on: boolean      # Optional: Default False. When set to True, will run on every request, does not need client to specify guardrail in request
    guardrail_info:            # Optional[Dict]: Additional information about the guardrail
      
```

Mode Specification

```python
from litellm.types.guardrails import Mode

mode = Mode(
    tags={"User-Agent: claude-cli": "logging_only"},
    default="logging_only"
)
```

### `guardrails` Request Parameter

The `guardrails` parameter can be passed to any LiteLLM Proxy endpoint (`/chat/completions`, `/completions`, `/embeddings`).

#### Format Options

1. Simple List Format:
```python
"guardrails": [
    "aporia-pre-guard",
    "aporia-post-guard"
]
```

2. Advanced Dictionary Format:

In this format the dictionary key is `guardrail_name` you want to run
```python
"guardrails": {
    "aporia-pre-guard": {
        "extra_body": {
            "success_threshold": 0.9,
            "other_param": "value"
        }
    }
}
```

#### Type Definition
```python
guardrails: Union[
    List[str],                              # Simple list of guardrail names
    Dict[str, DynamicGuardrailParams]       # Advanced configuration
]

class DynamicGuardrailParams:
    extra_body: Dict[str, Any]              # Additional parameters for the guardrail
```