Beginner LevelAI, LLMs, Embeddings & MCP
What are LLM Guardrails (NeMo Guardrails, Llama Guard) for input/output safety?
❓ Interview Prompt: Explain input validation, output sanitization, topical boundaries, and PII masking using security guardrail layers.
LLM Guardrails are programmable security middleware layers that sit between user applications and LLM APIs to inspect, filter, and modify inputs and outputs.
Key Guardrail Responsibilities:
1
Input Guardrails: Detect prompt injection attempts, toxic inputs, and off-topic domain queries before sending to the model.
2
Output Guardrails: Scan generated responses for PII leaks (Social Security numbers, API keys), harmful content, or hallucinated facts.
3
Popular Guardrail Engines: NVIDIA NeMo Guardrails, Meta Llama Guard, Guardrails AI.
Code Implementation Example
typescript
function sanitizePII(output: string): string {
// Redact SSN patterns
const ssnRegex = /\b\d{3}-\d{2}-\d{4}\b/g;
// Redact API key patterns
const apiKeyRegex = /sk-[a-zA-Z0-9]{32,}/g;
return output
.replace(ssnRegex, '[REDACTED_SSN]')
.replace(apiKeyRegex, '[REDACTED_API_KEY]');
}💡Simple output guardrail function redacting sensitive PII and API keys.
🎯 Interview Key Takeaway
Guardrails act as security middleware, filtering prompt injections on input and redacting PII leaks on output.
#Guardrails#Security#PII#Sanitization#Beginner
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