Guardrails and Safety

Overview

LiteLLM provides comprehensive guardrails to ensure safe, compliant, and policy-compliant LLM usage. Guardrails can inspect and moderate content before and after LLM calls, block inappropriate requests, and enforce custom policies.

What are Guardrails?

Guardrails are hooks that:

Pre-call: Validate inputs before sending to LLM
Post-call: Validate outputs before returning to user
During call: Monitor streaming responses in real-time

They can:

Block requests/responses
Modify content
Log policy violations
Alert on issues

Built-in Guardrails

LiteLLM includes several pre-built guardrail integrations:

Azure Content Safety

from litellm import Router

router = Router(
    model_list=[...],
    guardrail_list=[
        {
            "guardrail_name": "azure-content-safety",
            "litellm_params": {
                "api_key": "your-azure-key",
                "api_base": "https://<resource>.cognitiveservices.azure.com",
                "api_version": "2023-10-01"
            },
            "mode": "pre_call",  # Check before LLM call
            "default_on": True
        }
    ]
)

response = router.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

Azure Prompt Shield

Protect against prompt injection attacks:

router = Router(
    model_list=[...],
    guardrail_list=[
        {
            "guardrail_name": "azure-prompt-shield",
            "litellm_params": {
                "api_key": "your-azure-key",
                "api_base": "https://<resource>.cognitiveservices.azure.com"
            },
            "mode": "pre_call"
        }
    ]
)

Aporia AI Guardrails

router = Router(
    model_list=[...],
    guardrail_list=[
        {
            "guardrail_name": "aporia",
            "litellm_params": {
                "api_key": "your-aporia-key",
                "api_base": "https://api.aporia.com"
            },
            "mode": "during_call",  # Real-time monitoring
            "default_on": True
        }
    ]
)

LlamaGuard

router = Router(
    model_list=[...],
    guardrail_list=[
        {
            "guardrail_name": "llamaguard",
            "litellm_params": {
                "model": "llamaguard-7b"
            },
            "mode": "post_call"  # Check after LLM response
        }
    ]
)

Custom Guardrails

Create your own guardrail logic:

Basic Custom Guardrail

from litellm.integrations.custom_guardrail import CustomGuardrail
from litellm import Router

class ProfanityGuardrail(CustomGuardrail):
    def __init__(self):
        super().__init__(
            guardrail_name="profanity-filter",
            supported_event_hooks=["pre_call", "post_call"],
            event_hook="pre_call"
        )
        self.banned_words = ["badword1", "badword2"]
    
    async def async_pre_call_hook(
        self,
        user_api_key_dict,
        cache,
        call_type,
        data,
        **kwargs
    ):
        # Check messages for banned words
        messages = data.get("messages", [])
        for message in messages:
            content = message.get("content", "")
            for word in self.banned_words:
                if word in content.lower():
                    raise ValueError(f"Profanity detected: {word}")
        
        return data

# Use the guardrail
profanity_guardrail = ProfanityGuardrail()

router = Router(
    model_list=[...]
)
router.callbacks = [profanity_guardrail]

# This will be blocked
try:
    response = router.completion(
        model="gpt-4",
        messages=[{"role": "user", "content": "This contains badword1"}]
    )
except ValueError as e:
    print(f"Blocked: {e}")

Post-Call Guardrail

class PIIDetectionGuardrail(CustomGuardrail):
    def __init__(self):
        super().__init__(
            guardrail_name="pii-detection",
            event_hook="post_call"
        )
    
    async def async_post_call_success_hook(
        self,
        data,
        user_api_key_dict,
        response,
        **kwargs
    ):
        import re
        
        # Check for email addresses in response
        content = response.choices[0].message.content
        email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
        
        if re.search(email_pattern, content):
            # Mask emails
            masked_content = re.sub(email_pattern, "[EMAIL REDACTED]", content)
            response.choices[0].message.content = masked_content
        
        return response

pii_guardrail = PIIDetectionGuardrail()
router.callbacks = [pii_guardrail]

response = router.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "What's your email?"}]
)
# Emails in response are automatically masked

Streaming Guardrail

class StreamingModerationGuardrail(CustomGuardrail):
    def __init__(self):
        super().__init__(
            guardrail_name="streaming-moderation",
            event_hook="during_call"
        )
    
    async def async_during_call_hook(
        self,
        data,
        user_api_key_dict,
        call_type,
        **kwargs
    ):
        # Monitor streaming chunks
        banned_phrases = ["inappropriate content"]
        
        async for chunk in data:
            content = chunk.choices[0].delta.content or ""
            
            for phrase in banned_phrases:
                if phrase in content.lower():
                    raise ValueError(f"Inappropriate content detected: {phrase}")
            
            yield chunk

moderation_guardrail = StreamingModerationGuardrail()
router.callbacks = [moderation_guardrail]

response = router.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

Guardrail Modes

Pre-Call Mode

Validate before sending to LLM:

guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    event_hook="pre_call"
)

Use cases:

Input validation
Prompt injection detection
PII detection in prompts
Rate limiting by content

Post-Call Mode

Validate after LLM response:

guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    event_hook="post_call"
)

Use cases:

Output content filtering
PII redaction
Fact checking
Citation verification

During-Call Mode

Monitor streaming responses:

guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    event_hook="during_call"
)

Use cases:

Real-time content moderation
Stop generation early
Token-by-token filtering

Guardrail Configuration

Default On/Off

# Guardrail always active
guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    default_on=True
)

# Guardrail opt-in only
guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    default_on=False
)

# Enable per request
response = router.completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}],
    metadata={"guardrails": ["my-guardrail"]}
)

Content Masking

guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    mask_request_content=True,  # Mask in logs
    mask_response_content=True  # Mask in logs
)

Violation Messages

guardrail = CustomGuardrail(
    guardrail_name="my-guardrail",
    violation_message_template="Blocked by {guardrail_name}: {default_message}"
)

Advanced Guardrail Features

Modify Response Exception

Return synthetic response instead of blocking:

from litellm.integrations.custom_guardrail import ModifyResponseException

class PolicyGuardrail(CustomGuardrail):
    async def async_pre_call_hook(self, user_api_key_dict, cache, call_type, data, **kwargs):
        messages = data.get("messages", [])
        content = messages[-1].get("content", "")
        
        if "sensitive topic" in content.lower():
            # Return custom response instead of calling LLM
            raise ModifyResponseException(
                message="I cannot discuss that topic.",
                model=data.get("model"),
                request_data=data,
                guardrail_name=self.guardrail_name
            )
        
        return data

Session-Based Guardrails

Track violations across a session:

class SessionGuardrail(CustomGuardrail):
    def __init__(self):
        super().__init__(
            guardrail_name="session-guardrail",
            end_session_after_n_fails=3,  # End after 3 violations
            on_violation="warn"  # or "end_session"
        )
        self.violations = {}
    
    async def async_pre_call_hook(self, user_api_key_dict, cache, call_type, data, **kwargs):
        session_id = kwargs.get("session_id")
        
        if session_id:
            violation_count = self.violations.get(session_id, 0)
            
            if violation_count >= 3:
                raise ValueError("Session terminated due to policy violations")
        
        return data

Logging Guardrail Events

from litellm.integrations import CustomLogger

class GuardrailLogger(CustomLogger):
    def log_success_event(self, kwargs, response_obj, start_time, end_time):
        guardrail_info = kwargs.get("guardrail_info", [])
        
        for info in guardrail_info:
            print(f"Guardrail: {info.get('name')}")
            print(f"Status: {info.get('status')}")
            print(f"Duration: {info.get('duration')}ms")
    
    def log_failure_event(self, kwargs, response_obj, start_time, end_time):
        exception = kwargs.get("exception")
        if "guardrail" in str(exception).lower():
            print(f"Blocked by guardrail: {exception}")

litellm.callbacks = [GuardrailLogger()]

Best Practices

Guardrail Recommendations

Layer multiple guardrails - Combine different detection methods
Use pre_call for speed - Block bad inputs early
Use post_call for accuracy - Inspect actual LLM output
Monitor performance - Track guardrail latency
Test thoroughly - Verify guardrails don’t block valid requests
Log violations - Track what’s being blocked and why
Set appropriate thresholds - Balance safety vs. false positives

Common Patterns

Multi-Layer Safety

router = Router(
    model_list=[...],
    guardrail_list=[
        # Layer 1: Prompt injection detection
        {
            "guardrail_name": "azure-prompt-shield",
            "litellm_params": {...},
            "mode": "pre_call"
        },
        # Layer 2: Content moderation
        {
            "guardrail_name": "azure-content-safety",
            "litellm_params": {...},
            "mode": "pre_call"
        },
        # Layer 3: Output filtering
        {
            "guardrail_name": "llamaguard",
            "litellm_params": {...},
            "mode": "post_call"
        }
    ]
)

PII Protection Pipeline

class PIIPreCallGuardrail(CustomGuardrail):
    """Detect PII in input"""
    def __init__(self):
        super().__init__(guardrail_name="pii-input", event_hook="pre_call")

class PIIPostCallGuardrail(CustomGuardrail):
    """Redact PII in output"""
    def __init__(self):
        super().__init__(guardrail_name="pii-output", event_hook="post_call")

router = Router(model_list=[...])
router.callbacks = [PIIPreCallGuardrail(), PIIPostCallGuardrail()]

Industry Compliance

class HIPAAGuardrail(CustomGuardrail):
    """HIPAA compliance checks"""
    def __init__(self):
        super().__init__(
            guardrail_name="hipaa-compliance",
            event_hook="pre_call",
            mask_request_content=True,
            mask_response_content=True
        )
    
    async def async_pre_call_hook(self, user_api_key_dict, cache, call_type, data, **kwargs):
        # Detect PHI (Protected Health Information)
        messages = data.get("messages", [])
        for message in messages:
            content = message.get("content", "")
            if self.contains_phi(content):
                raise ValueError("PHI detected in request")
        return data
    
    def contains_phi(self, text):
        # Implement PHI detection logic
        import re
        # Check for SSN, medical record numbers, etc.
        ssn_pattern = r'\b\d{3}-\d{2}-\d{4}\b'
        return bool(re.search(ssn_pattern, text))

Performance Considerations

Async Guardrails

Always use async methods for better performance:

class FastGuardrail(CustomGuardrail):
    async def async_pre_call_hook(self, *args, **kwargs):
        # Async implementation is much faster
        pass

Parallel Guardrails

Multiple guardrails run in parallel when possible:

router = Router(
    model_list=[...],
    guardrail_list=[
        {"guardrail_name": "guardrail-1", "mode": "pre_call"},
        {"guardrail_name": "guardrail-2", "mode": "pre_call"},
        # Both run concurrently
    ]
)

Caching Guardrail Results

class CachedGuardrail(CustomGuardrail):
    def __init__(self):
        super().__init__(guardrail_name="cached-guardrail")
        self.cache = {}
    
    async def async_pre_call_hook(self, user_api_key_dict, cache, call_type, data, **kwargs):
        import hashlib
        
        # Create cache key from content
        content = str(data.get("messages", []))
        cache_key = hashlib.sha256(content.encode()).hexdigest()
        
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        # Run guardrail logic
        result = data  # Your validation logic here
        self.cache[cache_key] = result
        return result

Troubleshooting

Guardrail Not Triggering

# Ensure guardrail is registered
router = Router(model_list=[...])
router.callbacks = [my_guardrail]  # Add to callbacks

# Or use guardrail_list
router = Router(
    model_list=[...],
    guardrail_list=[{"guardrail_name": "my-guardrail", "default_on": True}]
)

High Latency

# Use async methods
# Minimize external API calls
# Cache results when possible
# Use pre_call to fail fast

False Positives

# Adjust thresholds
# Add whitelisting
# Log blocked requests for review

Logging - Track guardrail events
Cost Tracking - Monitor guardrail costs
Fallbacks - Handle guardrail failures

Get Started

Python SDK

AI Gateway (Proxy)

Core Features

Advanced

​Overview

​What are Guardrails?

​Built-in Guardrails

​Azure Content Safety

​Azure Prompt Shield

​Aporia AI Guardrails

​LlamaGuard

​Custom Guardrails

​Basic Custom Guardrail

​Post-Call Guardrail

​Streaming Guardrail

​Guardrail Modes

​Pre-Call Mode

​Post-Call Mode

​During-Call Mode

​Guardrail Configuration

​Default On/Off

​Content Masking

​Violation Messages

​Advanced Guardrail Features

​Modify Response Exception

​Session-Based Guardrails

​Logging Guardrail Events

​Best Practices

Guardrail Recommendations

​Common Patterns

​Multi-Layer Safety

​PII Protection Pipeline

​Industry Compliance

​Performance Considerations

​Async Guardrails

​Parallel Guardrails

​Caching Guardrail Results

​Troubleshooting

​Guardrail Not Triggering

​High Latency

​False Positives

​Related Features

Build docs developers (and LLMs) love

Overview

What are Guardrails?

Built-in Guardrails

Azure Content Safety

Azure Prompt Shield

Aporia AI Guardrails

LlamaGuard

Custom Guardrails

Basic Custom Guardrail

Post-Call Guardrail

Streaming Guardrail

Guardrail Modes

Pre-Call Mode

Post-Call Mode

During-Call Mode

Guardrail Configuration

Default On/Off

Content Masking

Violation Messages

Advanced Guardrail Features

Modify Response Exception

Session-Based Guardrails

Logging Guardrail Events

Best Practices

Common Patterns

Multi-Layer Safety

PII Protection Pipeline

Industry Compliance

Performance Considerations

Async Guardrails

Parallel Guardrails

Caching Guardrail Results

Troubleshooting

Guardrail Not Triggering

High Latency

False Positives

Related Features