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PII Masking

LiteLLM supports Microsoft Presidio for PII masking.

Quick Start​

Step 1. Add env​

export PRESIDIO_ANALYZER_API_BASE="http://localhost:5002"
export PRESIDIO_ANONYMIZER_API_BASE="http://localhost:5001"

Step 2. Set it as a callback in config.yaml​

litellm_settings: 
    callbacks = ["presidio", ...] # e.g. ["presidio", custom_callbacks.proxy_handler_instance]

Step 3. Start proxy​

litellm --config /path/to/config.yaml

This will mask the input going to the llm provider

Output parsing​

LLM responses can sometimes contain the masked tokens.

For presidio 'replace' operations, LiteLLM can check the LLM response and replace the masked token with the user-submitted values.

Just set litellm.output_parse_pii = True, to enable this. 

litellm_settings:
    output_parse_pii: true

Expected Flow:

  1. User Input: "hello world, my name is Jane Doe. My number is: 034453334"

  2. LLM Input: "hello world, my name is [PERSON]. My number is: [PHONE_NUMBER]"

  3. LLM Response: "Hey [PERSON], nice to meet you!"

  4. User Response: "Hey Jane Doe, nice to meet you!"

Ad-hoc recognizers​

Send ad-hoc recognizers to presidio /analyze by passing a json file to the proxy

Example ad-hoc recognizer

litellm_settings: 
  callbacks: ["presidio"]
  presidio_ad_hoc_recognizers: "./hooks/example_presidio_ad_hoc_recognizer.json"

You can see this working, when you run the proxy: 

litellm --config /path/to/config.yaml --debug

Make a chat completions request, example:

{
  "model": "azure-gpt-3.5",
  "messages": [{"role": "user", "content": "John Smith AHV number is 756.3026.0705.92. Zip code: 1334023"}]
}

And search for any log starting with Presidio PII Masking, example:

Presidio PII Masking: Redacted pii message: <PERSON> AHV number is <AHV_NUMBER>. Zip code: <US_DRIVER_LICENSE>

Turn on/off per key​

Turn off PII masking for a given key.

Do this by setting permissions: {"pii": false}, when generating a key. 

curl --location 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer sk-1234' \
--header 'Content-Type: application/json' \
--data '{
    "permissions": {"pii": false}
}'

Turn on/off per request​

The proxy support 2 request-level PII controls:

  • no-pii: Optional(bool) - Allow user to turn off pii masking per request.
  • output_parse_pii: Optional(bool) - Allow user to turn off pii output parsing per request.

Usage​

Step 1. Create key with pii permissions

Set allow_pii_controls to true for a given key. This will allow the user to set request-level PII controls.

curl --location 'http://0.0.0.0:4000/key/generate' \
--header 'Authorization: Bearer my-master-key' \
--header 'Content-Type: application/json' \
--data '{
    "permissions": {"allow_pii_controls": true}
}'

Step 2. Turn off pii output parsing

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key=os.environ.get("OPENAI_API_KEY"),
        base_url="http://0.0.0.0:4000"
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "My name is Jane Doe, my number is 8382043839",
        }
    ],
    model="gpt-3.5-turbo",
    extra_body={
        "content_safety": {"output_parse_pii": False} 
    }
)

Step 3: See response

{
  "id": "chatcmpl-8c5qbGTILZa1S4CK3b31yj5N40hFN",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "content": "Hi [PERSON], what can I help you with?",
        "role": "assistant"
      }
    }
  ],
  "created": 1704089632,
  "model": "gpt-35-turbo",
  "object": "chat.completion",
  "system_fingerprint": null,
  "usage": {
    "completion_tokens": 47,
    "prompt_tokens": 12,
    "total_tokens": 59
  },
  "_response_ms": 1753.426
}

Turn on for logging only​

Only apply PII Masking before logging to Langfuse, etc.

Not on the actual llm api request / response.

note

This is currently only applied for

  • /chat/completion requests
  • on 'success' logging
  1. Setup config.yaml
litellm_settings:
  presidio_logging_only: true 

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY
  1. Start proxy
litellm --config /path/to/config.yaml
  1. Test it! 
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "user",
      "content": "Hi, my name is Jane!"
    }
  ]
  }'

Expected Logged Response

Hi, my name is <PERSON>!