Custom Code Guardrail

Write custom guardrail logic using Python-like code that runs in a sandboxed environment.

Quick Start

1. Define the guardrail in config

model_list:
    - model_name: gpt-4
        litellm_params:
        model: gpt-4
        api_key: os.environ/OPENAI_API_KEY

guardrails:
    - guardrail_name: block-ssn
        litellm_params:
        guardrail: custom_code
        mode: pre_call
        custom_code: |
            def apply_guardrail(inputs, request_data, input_type):
                for text in inputs["texts"]:
                    if regex_match(text, r"\\d{3}-\\d{2}-\\d{4}"):
                        return block("SSN detected")
                return allow()

2. Start proxy

litellm --config config.yaml

3. Test

curl -X POST http://localhost:4000/chat/completions \\
  -H "Authorization: Bearer sk-1234" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "My SSN is 123-45-6789"}],
    "guardrails": ["block-ssn"]
  }'

Configuration

Writing Custom Code

Function Signature

Your code must define an apply_guardrail function. It can be either sync or async:

# Sync version
def apply_guardrail(inputs, request_data, input_type):
    # inputs: see table below
    # request_data: {"model": "...", "user_id": "...", "team_id": "...", "metadata": {...}}
    # input_type: "request" or "response"
    
    return allow()  # or block() or modify()

# Async version (recommended when using HTTP primitives)
async def apply_guardrail(inputs, request_data, input_type):
    response = await http_post("https://api.example.com/check", body={"text": inputs["texts"][0]})
    if response["success"] and response["body"].get("flagged"):
        return block("Content flagged")
    return allow()

inputs Parameter

request_data Parameter

Return Values

Built-in Primitives

Regex

JSON

URL

Code Detection

Text Utilities

HTTP Requests (Async)

Make async HTTP requests to external APIs for additional validation or content moderation.

Response format:

{
    "status_code": 200,        # HTTP status code
    "body": {...},             # Response body (parsed JSON or string)
    "headers": {...},          # Response headers
    "success": True,           # True if status code is 2xx
    "error": None              # Error message if request failed
}

Note: When using HTTP primitives, define your function as async def apply_guardrail(...) for non-blocking execution.

Examples

Block PII (SSN)

def apply_guardrail(inputs, request_data, input_type):
    for text in inputs["texts"]:
        if regex_match(text, r"\\d{3}-\\d{2}-\\d{4}"):
            return block("SSN detected")
    return allow()

Redact Email Addresses

def apply_guardrail(inputs, request_data, input_type):
    pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}"
    modified = []
    for text in inputs["texts"]:
        modified.append(regex_replace(text, pattern, "[EMAIL REDACTED]"))
    return modify(texts=modified)

Block SQL Injection

def apply_guardrail(inputs, request_data, input_type):
    if input_type != "request":
        return allow()
    for text in inputs["texts"]:
        if contains_code_language(text, ["sql"]):
            return block("SQL code not allowed")
    return allow()

Validate JSON Response

def apply_guardrail(inputs, request_data, input_type):
    if input_type != "response":
        return allow()
    
    schema = {
        "type": "object",
        "required": ["name", "value"]
    }
    
    for text in inputs["texts"]:
        obj = json_parse(text)
        if obj is None:
            return block("Invalid JSON response")
        if not json_schema_valid(obj, schema):
            return block("Response missing required fields")
    return allow()

Check URLs in Response

def apply_guardrail(inputs, request_data, input_type):
    if input_type != "response":
        return allow()
    for text in inputs["texts"]:
        if not all_urls_valid(text):
            return block("Response contains invalid URLs")
    return allow()

Call External Moderation API (Async)

async def apply_guardrail(inputs, request_data, input_type):
    # Call an external moderation API
    for text in inputs["texts"]:
        response = await http_post(
            "https://api.example.com/moderate",
            body={"text": text, "user_id": request_data["user_id"]},
            headers={"Authorization": "Bearer YOUR_API_KEY"},
            timeout=10
        )
        
        if not response["success"]:
            # API call failed - decide whether to allow or block
            return allow()
        
        if response["body"].get("flagged"):
            return block(response["body"].get("reason", "Content flagged"))
    
    return allow()

Combine Multiple Checks

def apply_guardrail(inputs, request_data, input_type):
    modified = []
    
    for text in inputs["texts"]:
        # Redact SSN
        text = regex_replace(text, r"\\d{3}-\\d{2}-\\d{4}", "[SSN]")
        # Redact credit cards
        text = regex_replace(text, r"\\d{16}", "[CARD]")
        modified.append(text)
    
    # Block SQL in requests
    if input_type == "request":
        for text in inputs["texts"]:
            if contains_code_language(text, ["sql"]):
                return block("SQL injection blocked")
    
    return modify(texts=modified)

Sandbox Restrictions

Custom code runs in a restricted environment:

• ❌ No import statements
• ❌ No file I/O
• ❌ No exec() or eval()
• ✅ HTTP requests via built-in http_request, http_get, http_post primitives
• ✅ Only LiteLLM-provided primitives available

Per-Request Usage

Enable guardrail per request:

curl -X POST http://localhost:4000/chat/completions \\
  -H "Authorization: Bearer sk-1234" \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Hello"}],
    "guardrails": ["block-ssn"]
  }'

Default On

Run guardrail on all requests:

litellm_settings:
  guardrails:
    - guardrail_name: block-ssn
      litellm_params:
        guardrail: custom_code
        mode: pre_call
        default_on: true
        custom_code: |
          def apply_guardrail(inputs, request_data, input_type):
              ...