Cadre

Red-Team Tooling — Garak, Llama Guard, PyRIT

Three production tools frame the 2026 red-team stack. Llama Guard (Meta) — a Llama-3.1-8B classifier fine-tuned on 14 MLCommons hazard categories; the 2025 Llama Guard 4 is a 12B natively multimodal classifier pruned from Llama 4 Scout. Garak (NVIDIA) — open-source LLM vulnerability scanner with static, dynamic, and adaptive probes for hallucination, data leakage, prompt injection, toxicity, and jailbreaks. PyRIT (Microsoft) — multi-turn red-team campaigns with Crescendo, TAP, and custom converter chains for deep exploitation. Llama Guard 3 is documented in Meta's "Llama 3 Herd of Models" (arXiv:2407.21783); Llama Guard 3-1B-INT4 in arXiv:2411.17713; Garak's probe architecture in github.com/NVIDIA/garak. These tools are the 2026 production interface between red-team research (Lessons 12-15) and deployment (Lesson 17+).

Build 1.3 hr Python (stdlib, tool-architecture simulator and Llama Guard-style classifier mock)

Prerequisites: Phase 18 · 12-15 (jailbreaks and IPI)

#Learning Objectives

Describe Llama Guard 3/4's position in the safety stack: input classifier, output classifier, or both.
Name the 14 MLCommons hazard categories and state one non-obvious one (Code Interpreter Abuse).
Describe Garak's probe architecture: probes, detectors, harnesses.
Describe PyRIT's multi-turn campaign structure and how it composes with Garak probes.

#Learning Objectives

Keep going. The rest of this lesson is one step away.