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defending-llms-with-guardrails

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Deploys Llama Guard 3 safety classification, NeMo Guardrails programmable dialogue rails, and LLM Guard input/output scanner pipelines as complementary runtime defenses that inspect and constrain LLM prompts and responses. Use when adding a production runtime safety layer to an LLM, RAG, or agent application to block jailbreaks, prompt injection (OWASP LLM01), toxic content, or sensitive-data leakage before it reaches or leaves the model.

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# Defending LLMs with Guardrails

> **Defensive scope:** This skill describes runtime defenses for production LLM applications. The example jailbreak/injection payloads exist only to validate that guardrails block them. Test against systems you own or are authorized to assess.

## Overview

Large language model (LLM) applications are exposed to adversarial input (jailbreaks, prompt injection, toxic content) and can emit unsafe, biased, or sensitive output. A guardrail is a runtime control that inspects and constrains the data flowing into and out of an LLM. Three production-grade, open-source guardrail systems dominate the ecosystem and are complementary rather than mutually exclusive:

- **Llama Guard 3** (Meta) — a Llama-3.1-8B model fine-tuned as a *safety classifier*. Given a prompt or a response, it emits `safe` or `unsafe` plus the violated MLCommons hazard categories (S1–S14). It is the strongest *semantic* content-safety classifier of the three and supports prompt classification, response classification, and tool-call/code-interpreter classification across 8 languages.
- **NeMo Guardrails** (NVIDIA) — a *programmable* dialogue-rail framework. You define `input`, `output`, `dialog`, `retrieval`, and `execution` rails in a `config.yml` plus Colang (`.co`) flows. It can call external models (including Llama Guard) as actions, enforce topical boundaries, and add fact-checking/jailbreak-detection rails.
- **LLM Guard** (Protect AI) — a *scanner pipeline* with 15 input scanners and 20 output scanners (PromptInjection, Toxicity, Anonymize/Deanonymize, Secrets, BanTopics, Sensitive, Regex, etc.). It returns a sanitized string, a validity flag, and a risk score per scanner, making it ideal for a deterministic pre/post pipeline.

This skill maps to MITRE ATLAS **AML.T0054 — LLM Jailbreak**: the guardrail layer is the mitigation that detects and blocks jailbreak/injection attempts before they reach (or after they leave) the model.

## When to Use
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