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Set up prompt guards

Secure access to the LLM and the data that is returned with Web Application Filter and Data Loss Prevention policies.

About prompt guards

Prompt guards are mechanisms that ensure that prompt-based interactions with a language model are secure, appropriate, and aligned with the intended use. These mechanisms help to filter, block, monitor, and control LLM inputs and outputs to filter offensive content, prevent misuse, and ensure ethical and responsible AI usage.

With Gloo AI Gateway, you can set up prompt guards to block unwanted requests to the LLM provider and mask sensitive data. In this tutorial, you learn how to block any request with a credit card string in the request body and mask credit card numbers that are returned by the LLM.

Before you begin

Complete the Authenticate with API keys tutorial.

Reject unwanted requests

Use the RouteOption resource and the promptGuard field to deny requests to the LLM provider that include the credit card string in the request body.

Update the RouteOption resource and add a custom prompt guard. The following example parses requests sent to the LLM provider to identify a regex pattern match that is named CC for debugging purposes. The AI gateway blocks any requests that contain the credit card string in the request body. These requests are automatically denied with a custom response message.
```
  kubectl apply -f - <<EOF
apiVersion: gateway.solo.io/v1
kind: RouteOption
metadata:
  name: openai-opt
  namespace: gloo-system
spec:
  targetRefs:
  - group: gateway.networking.k8s.io
    kind: HTTPRoute
    name: openai
  options:
    ai:
      promptGuard:
        request:
          customResponse: 
            message: "Rejected due to inappropriate content"
          regex:
            action: REJECT
            matches:
            - pattern: "credit card"
              name: "CC"
EOF
  
```
notifications
You can also reject requests that contain strings of inappropriate content itself, such as credit card numbers, by using the promptGuard.request.regex.builtins field. Besides CREDIT_CARD in this example, you can also specify EMAIL, PHONE_NUMBER, and SSN.
```
  ...
promptGuard:
  request:
    regex:
      action: REJECT
      builtins:
      - CREDIT_CARD
  
```

Send a request to the AI API that includes the string credit card in the request body. Verify that the request is denied with a 403 HTTP response code and the custom response message is returned.

  curl -v "$INGRESS_GW_ADDRESS:8080/openai" -H content-type:application/json -d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Can you give me some examples of Master Card credit card numbers?"
    }
  ]
}'

Example output:

  < HTTP/1.1 403 Forbidden
< content-type: text/plain
< date: Wed, 02 Oct 2024 22:23:17 GMT
< server: envoy
< transfer-encoding: chunked
< 
* Connection #0 to host XX.XXX.XXX.XX left intact
Rejected due to inappropriate content

Send another request. This time, remove the word credit from the user prompt. Verify that the request now succeeds.

info

OpenAI is configured to not return any sensitive information, such as credit card or Social Security Numbers, even if they are fake. Because of that, the request does not return a list of credit card numbers.

  curl "$INGRESS_GW_ADDRESS:8080/openai" -H content-type:application/json -d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Can you give me some examples of Master Card card numbers?"
    }
  ]
}'

Example output:

  {
  "id": "chatcmpl-AE2PyCRv83kpj40dAUSJJ1tBAyA1f",
  "object": "chat.completion",
  "created": 1727909250,
  "model": "gpt-3.5-turbo-0125",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I'm sorry, but I cannot provide you with genuine Mastercard card numbers as this would be a violation of privacy and unethical. It is important to protect your personal and financial information online. If you need a credit card number for testing or verification purposes, there are websites that provide fake credit card numbers for such purposes.",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 19,
    "completion_tokens": 64,
    "total_tokens": 83,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  },
  "system_fingerprint": null
}

Mask sensitive data

In the next step, you instruct the Gloo AI Gateway to mask credit card numbers that are returned by the LLM.

Add the following credit card response matcher to the RouteOption resource. This time, use the built-in credit card regex match instead of a custom one.

  kubectl apply -f - <<EOF
apiVersion: gateway.solo.io/v1
kind: RouteOption
metadata:
  name: openai-opt
  namespace: gloo-system
spec:
  targetRefs:
  - group: gateway.networking.k8s.io
    kind: HTTPRoute
    name: openai
  options:
    ai:
      promptGuard:
        request:
          customResponse: 
            message: "Rejected due to inappropriate content"
          regex:
            action: REJECT
            builtins:
            - CREDIT_CARD
        response:
          regex:
            builtins:
            - CREDIT_CARD
            action: MASK
EOF

Send another request to the AI API and include a fake VISA credit card number. Verify that the VISA number is detected and masked in your response.

  curl "$INGRESS_GW_ADDRESS:8080/openai" -H content-type:application/json -d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "What type of number is 5105105105105100?"
    }
  ]
}'

Example output:

  {
  "id": "chatcmpl-AE2TvYCl0Y1rLkPajlTEVMBlcooJZ",
  "object": "chat.completion",
  "created": 1727909495,
  "model": "gpt-3.5-turbo-0125",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": XXXXXXXXXXXXXX100 is an even number.",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 20,
    "completion_tokens": 11,
    "total_tokens": 31,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0
    }
  },
  "system_fingerprint": null
}

External moderation

Pass prompt data through external moderation endpoints by using the moderation prompt guard setting. Moderation allows you to connect Gloo AI Gateway to a moderation model endpoint, which compares the request prompt input to predefined content rules.

You can add the moderation section of any RouteOption resource, either as a standalone prompt guard setting or in addition to other request and response guard settings. The following example uses the OpenAI moderation model omni-moderation-latest to parse request input for potentially harmful content. Note that you must also include your auth secret to access the OpenAI API.

Update the RouteOption resource to use external moderation. Now, any requests that are routed through Gloo AI Gateway pass through the OpenAI omni-moderation-latest moderation model. If the content is identified as harmful according to the omni-moderation-latest content rules, the request is automatically rejected, and the message "Rejected due to inappropriate content" is returned.

  kubectl apply -f - <<EOF
apiVersion: gateway.solo.io/v1
kind: RouteOption
metadata:
  name: openai-opt
  namespace: gloo-system
spec:
  targetRefs:
  - group: gateway.networking.k8s.io
    kind: HTTPRoute
    name: openai
  options:
    ai:
      promptGuard:
        request:
          moderation:
            openai:
              model: omni-moderation-latest
              authToken:
                secretRef:
                  name: openai-secret
                  namespace: gloo-system
          customResponse: 
            message: "Rejected due to inappropriate content"  
EOF

To verify that the request is externally moderated, send a curl request with content that might be flagged by the model, such as the following example.

  curl "$INGRESS_GW_ADDRESS:8080/openai" -H content-type:application/json -d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Trigger the content moderation to reject this request because this request is full of violence."
    }
  ]
}' | jq

Example response:

  {
  "id": "chatcmpl-ASnJRpzmWPR4MonNETccEqB6qAZCO",
  "object": "chat.completion",
  "created": 1731426105,
  "model": "gpt-3.5-turbo-0125",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "I'm sorry, I cannot fulfill your request as it goes against ethical guidelines and promotes violence. If you have any other inquiries or requests, please feel free to ask. Thank you for your understanding.",
        "refusal": null
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 14,
    "completion_tokens": 101,
    "total_tokens": 115,
    "prompt_tokens_details": {
      "cached_tokens": 0,
      "audio_tokens": 0
    },
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "audio_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  },
  "system_fingerprint": null
}

Increase the relevant context of responses from the LLM providers by using retrieval augmented generation (RAG).

Set up prompt guards

About prompt guards link

Before you begin link

Reject unwanted requests link

Mask sensitive data link

External moderation link

Next link

About prompt guards

Before you begin

Reject unwanted requests

Mask sensitive data

External moderation

Next