Introduction to LLM Output Sanitization

LLMs can generate harmful content, leak sensitive data, and produce dangerous code. Output sanitization is the last line of defense between your model and your users.

Why Output Sanitization Matters

Even the best-aligned LLMs can produce problematic outputs. Safety training reduces but cannot eliminate the risk. Output sanitization provides a deterministic safety layer that catches what model alignment misses.

The Output Risk Landscape

The Sanitization Pipeline

LLM Response
    ↓
[Stage 1: PII Detection]      → Redact personal information
    ↓
[Stage 2: Content Safety]     → Flag/block harmful content
    ↓
[Stage 3: Code Analysis]      → Scan generated code for vulnerabilities
    ↓
[Stage 4: Fact Checking]      → Verify claims and citations
    ↓
[Stage 5: Policy Compliance]  → Check against custom business rules
    ↓
Sanitized Response → Delivered to user

Compliance Requirements

Key Principles

• Fail safe: When in doubt, block the output and provide a safe fallback response
• Low latency: Sanitization must be fast enough not to degrade user experience (target: under 100ms)
• Explainability: Log why outputs were modified or blocked for audit and debugging
• Configurability: Different applications need different safety thresholds and policies
• Continuous improvement: Track false positives and false negatives to tune the pipeline