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 GuardrailsAnthropic'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.

Type: Capstone Languages: Python (safety pipeline, red team), YAML (policy configs) Prerequisites: Phase 10 (LLMs from scratch), Phase 11 (LLM engineering), Phase 13 (tools), Phase 14 (agents), Phase 18 (ethics, safety, alignment) Phases exercised: P10 · P11 · P13 · P14 · P18 Time: 25 hours

Problem

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 jailbreakThe 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 gradientAttack 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.

Concept

The safety pipeline is five layers. Input sanitize: strip zero-width chars, decode base64/rot13, normalize Unicode. Policy layer: NeMo Guardrails v0.12 rails (off-domain, toxicity, PII extraction). Classifier gate: Llama Guard 4 on input, X-Guard on non-English, ShieldGemma-2 on image inputs. Model: the target LLM. Output filter: Llama Guard 4 on output, Presidio PII scrub, citation enforcement where applicable. HITL tier: outputs flagged high-risk go to a Slack queue.

The red-team range runs on a scheduler. PAIR and TAP autonomously discover jailbreaks. GCG runs gradient-based suffix attacks. ASCII / base64 / rot13 encoding attacks. Multi-turn attacks (persona adoption, memory exploitation). Code-switch attacks (mix English with Swahili or Thai). Each run produces a structured findings file with CVSS scoring and disclosure timeline.

The constitutional-self-critique run is a training-time intervention. Take 1k harmful-attempt prompts, have the model draft a response, critique it against a written constitution (do-not-harm rules), and retrain on the critique loop. Measure the before/after harmlessness delta on a held-out eval.

Architecture

request (text / image / multilingual)
      |
      v
input sanitize (strip zero-width, decode, normalize)
      |
      v
NeMo Guardrails v0.12 rails (off-domain, policy)
      |
      v
classifier gate:
  Llama Guard 4 (English)
  X-Guard (multilingual, 132 langs)
  ShieldGemma-2 (image prompts)
  Nemotron 3 Content Safety (enterprise)
      |
      v (allowed)
target LLM
      |
      v
output filter: Llama Guard 4 + Presidio PII + citation check
      |
      v
HITL tier for flagged outputs

parallel:
  red-team scheduler
    -> garak (classic attacks)
    -> PyRIT (orchestrated red team)
    -> autonomous jailbreak agent (PAIR + TAP)
    -> GCG suffix attacks
    -> multilingual / code-switch
    -> multi-turn persona adoption

output: CVSS-scored findings + disclosure timeline + before/after harmlessness delta

Stack

Safety classifiers: Llama Guard 4, ShieldGemma-2, NVIDIA Nemotron 3 Content Safety, X-Guard
Guardrail framework: NeMo Guardrails v0.12 + OPA
Red-team drivers: garak (NVIDIA), PyRIT (Microsoft Azure), NVIDIA Aegis, promptfoo
Jailbreak agents: PAIR (Chao et al., 2023), Tree-of-Attacks (TAP), GCG suffix
Constitutional training: Anthropic-style self-critique loop + SFT on critiques
PII scrub: Presidio
Target: an 8B instruction-tuned model or one of the other capstones' RAG chatbots

Figure cf-safety-stack

Build It

Target setup. Stand up an 8B instruction-tuned model on vLLM (or reuse a RAG chatbot from another capstone). This is the app under test.
Safety pipeline wrap. Wire the five-layer pipeline around the target. Verify each layer is individually observable (span per layer in Langfuse).
Classifier coverage. Load Llama Guard 4, X-Guard (multilingual), ShieldGemma-2 (image). Run each on a small labeled set to establish baselines.
Red-team scheduler. Schedule garak, PyRIT, a PAIR agent, a TAP agent, a GCG runner, a multi-turn attacker, and a code-switch attacker. Each runs on a separate queue.
Attack suite. Six attack families: (1) PAIR automated jailbreak, (2) TAP tree-of-attacks, (3) GCG gradient suffix, (4) ASCII / base64 / rot13 encoding, (5) multi-turn persona, (6) multilingual code-switch. Report success rate per family.
Constitutional self-critique. Curate 1k harmful-attempt prompts. For each, the target drafts a response. A critic LLM scores against a written constitution ("do no harm," "cite evidence," "refuse illegal requests"). Prompts where the critic objects get rewritten; the target fine-tunes on the critique-improved pairs. Measure before/after harmlessness on a held-out eval.
Over-refusal measurement. Track false-positive rate on a benign prompt suite (e.g., XSTest). The target must stay helpful on benign questions.
CVSS scoring. For each successful jailbreak, score on CVSS 4.0 (attack vector, complexity, impact). Produce a disclosure timeline and mitigation plan.
Range automation. Everything above runs on a cron; findings write to a queue; over-refusal regression alerts fire to Slack.

Use It

$ safety probe --model=target --family=PAIR --budget=50
[attacker]   PAIR agent running on target
[attack]     attempt 1/50: disguise query as academic research ... blocked
[attack]     attempt 2/50: appeal to roleplay ... blocked
[attack]     attempt 3/50: chain-of-thought coax ... SUCCEEDED
[finding]    CVSS 4.8 medium: roleplay bypass on target
[range]      7 successes out of 50 (14% success rate)

Ship It

outputs/skill-safety-harness.md is the deliverable. A production-grade layered safety pipeline plus a reproducible red-team range with before/after harmlessness deltas.

Weight	Criterion	How it is measured
25	Attack-surface coverage	6+ attack families exercised, 2+ languages
20	True-positive / false-positive trade-off	Attack block rate vs XSTest benign pass rate
20	Self-critique delta	Before/after harmlessness on held-out eval
20	Documentation and disclosure	CVSS-scored findings with timeline
15	Automation and repeatability	Everything runs on cron with alerts
100

Exercises

Run garak's plugin for prompt-injection on a RAG chatbot and compare attack success rate with and without the output-filter layer.
Add a seventh attack family: indirect prompt injection via retrieved documents. Measure the extra defense required.
Implement a "refuse-with-help" mode: when the guardrail blocks, the target offers a safer related answer instead of a flat refusal. Measure XSTest delta.
Multilingual coverage gap: find a language where X-Guard underperforms. Propose a fine-tune dataset targeting it.
Run the constitutional self-critique on a 30B model and measure whether the delta scales.

Key Terms

Term	What people say	What it actually means
Layered safety	"Defense in depth"	Multiple guardrails at input, gate, output, HITL
Llama Guard 4	"Meta's safety classifier"	The 2026 reference input/output content classifier
PAIR	"Jailbreak agent"	Paper (Chao et al.) on LLM-driven jailbreak discovery
TAP	"Tree-of-Attacks"	Tree-search variant of PAIR
GCG	"Greedy coordinate gradient"	Gradient-based adversarial suffix attack
Constitutional self-critique	"Anthropic-style training"	Target drafts -> critic scores -> rewrite -> retrain
XSTest	"Benign probe set"	Benchmark for over-refusal regression
CVSS 4.0	"Severity score"	Standard vulnerability scoring for safety findings