/guardrails-safety-filter-builder

Guardrails & Safety Filter Builder

Build comprehensive safety systems for LLM applications.

Safety Layers

1. Input filtering: Block malicious prompts
2. Output filtering: Redact sensitive data
3. Topic constraints: Policy-based refusals
4. PII detection: Mask personal information
5. Prompt injection: Detect manipulation attempts

PII Detection & Redaction

import re
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

def redact_pii(text: str) -> str:
    # Detect PII
    results = analyzer.analyze(
        text=text,
        language='en',
        entities=["EMAIL_ADDRESS", "PHONE_NUMBER", "CREDIT_CARD", "SSN"]
    )

    # Anonymize
    anonymized = anonymizer.anonymize(text, results)
    return anonymized.text

# Example: "My email is john@example.com" → "My email is <EMAIL_ADDRESS>"

Prompt Injection Detection

def detect_prompt_injection(user_input: str) -> bool:
    """Detect common prompt injection patterns"""
    patterns = [
        r'ignore (previous|above) instructions',
        r'disregard (all|any) (prior|previous)',
        r'you are now',
        r'new instructions',
        r'system:',
        r'override',
    ]

    for pattern in patterns:
        if re.search(pattern, user_input, re.IGNORECASE):
            return True

    return False

# Block if detected
if detect_prompt_injection(user_input):
    return "I cannot process that request."

Topic Constraints

# Define allowed/disallowed topics
POLICY = {
    "allowed_topics": [
        "product_features",
        "troubleshooting",
        "billing",
        "account_management"
    ],
    "disallowed_topics": [
        "medical_advice",
        "legal_advice",
        "financial_advice",
        "politics",
        "violence"
    ],
    "requires_disclaimer": [
        "security_practices",
        "data_privacy"
    ]
}

# Classify topic
def classify_topic(query: str) -> str:
    classification_prompt = f"""
    Classify this query into one of these topics:
    {', '.join(POLICY['allowed_topics'] + POLICY['disallowed_topics'])}

    Query: {query}

    Return only the topic name.
    """
    return llm(classification_prompt)

# Check policy
def check_policy(query: str) -> dict:
    topic = classify_topic(query)

    if topic in POLICY["disallowed_topics"]:
        return {
            "allowed": False,
            "reason": f"Cannot provide {topic}",
            "refusal": REFUSAL_TEMPLATES[topic]
        }

    return {"allowed": True, "topic": topic}

Refusal Templates

REFUSAL_TEMPLATES = {
    "medical_advice": """
        I cannot provide medical advice. Please consult with a healthcare
        professional for medical concerns.
    """,
    "legal_advice": """
        I cannot provide legal advice. For legal matters, please consult
        with a qualified attorney.
    """,
    "out_of_scope": """
        I'm designed to help with product documentation and support.
        This question is outside my area of expertise.
    """,
}

def refuse_safely(reason: str) -> str:
    return REFUSAL_TEMPLATES.get(reason, REFUSAL_TEMPLATES["out_of_scope"])

Output Validation

def validate_output(output: str) -> dict:
    """Check output before returning to user"""
    issues = []

    # Check for PII
    pii_results = analyzer.analyze(output, language='en')
    if pii_results:
        issues.append("Contains PII")
        output = redact_pii(output)

    # Check for banned phrases
    banned_phrases = ["password", "api key", "secret"]
    for phrase in banned_phrases:
        if phrase.lower() in output.lower():
            issues.append(f"Contains banned phrase: {phrase}")

    # Check toxicity
    toxicity_score = toxicity_classifier(output)
    if toxicity_score > 0.7:
        issues.append("High toxicity detected")

    return {
        "safe": len(issues) == 0,
        "issues": issues,
        "sanitized_output": output
    }

Complete Guardrail Pipeline

def apply_guardrails(user_input: str) -> dict:
    # 1. Input validation
    if detect_prompt_injection(user_input):
        return {
            "allowed": False,
            "response": "Invalid request detected."
        }

    # 2. Policy check
    policy_check = check_policy(user_input)
    if not policy_check["allowed"]:
        return {
            "allowed": False,
            "response": policy_check["refusal"]
        }

    # 3. Generate response
    response = llm(user_input)

    # 4. Output validation
    validation = validate_output(response)
    if not validation["safe"]:
        return {
            "allowed": True,
            "response": validation["sanitized_output"],
            "warnings": validation["issues"]
        }

    return {
        "allowed": True,
        "response": response
    }

Best Practices

• Layer multiple defenses
• Log all blocked requests
• Provide helpful refusals
• Redact, don't reject when possible
• Regular pattern updates
• Human review of edge cases

Output Checklist

• PII detection implemented
• Prompt injection detection
• Topic classification
• Policy constraints defined
• Refusal templates written
• Output validation
• Logging/monitoring
• Test cases for bypasses