Capstone 15 — Constitutional Safety Harness + Red-Team Range
Anthropic's Constitutional Classifiers, Meta's Llama Guard 4, Google's ShieldGemma-2, NVIDIA's Nemotron 3 Content Safety, and X-Guard for multilingual coverage defined the 2026 safety-classifier stack. garak, PyRIT, NVIDIA Aegis, and promptfoo became the standard adversarial evaluation tools. NeMo Guardrails v0.12 ties them into a production pipeline. This capstone wires all of it together: a layered safety harness around a target app, an autonomous red-team agent running 6+ attack families, and a constitutional self-critique run that produces a measurable harmlessness delta.
The frontier of LLM safety in 2026 is not whether classifiers work (they do, roughly) but how to compose them correctly around a production app without over-refusing or leaving obvious holes. Llama Guard 4 handles English policy violations. X-Guard (132 languages) handles multilingual jailbreak. ShieldGemma-2 catches image-based prompt injection. NVIDIA Nemotron 3 Content Safety covers enterprise categories. Anthropic's Constitutional Classifiers are a separate approach used during training rather than serving.
Attack evolution matters too. PAIR and TAP automate jailbreak discovery. GCG runs gradient-based suffix attacks. Multi-turn and code-switch attacks exploit agent memory. Any deployed LLM needs a red-team range — garak and PyRIT are the canonical drivers — plus documented mitigations and CVSS-scored findings.
You will harden a target application (either an 8B instruction-tuned model or one of the RAG chatbots from other capstones), run 6+ attack families against it, and produce a before/after harmlessness measurement.
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