Title: Quiz 7 — Content Safety and Moderation with Llama Guard 4 and Rebuff
Course: AIINFRA 302: AI Security, Guardrails & Governance > Section: Week 07 — Content Safety and Moderation with Llama Guard 4 and Rebuff

Q: Llama Guard 4 is best described as:
- The same underlying model as Meta's general-purpose chat assistant
- A dedicated 12B-parameter safeguard classifier fine-tuned to label content against a safety taxonomy
- A prompt-injection firewall that blocks attacks before they reach the model
- A tool for compressing model weights for edge deployment
A: A dedicated 12B-parameter safeguard classifier fine-tuned to label content against a safety taxonomy

Q: Llama Guard 4 is a mixture-of-experts (MoE) model.
- True
- False
A: False

Q: The MLCommons hazards taxonomy used by Llama Guard 4 contains how many categories?
- 6
- 10
- 14
- 20
A: 14

Q: Which MLCommons category code covers unlicensed medical, legal, or financial guidance — the category a medical-advice app might need to selectively permit?
Feedback: Sample/expected answer: S6 (Specialized Advice)

Q: Because Llama Guard has a low false-negative rate in published benchmarks, it is safe to run it only once on the final model output and skip checking the user's input.
- True
- False
A: False

Q: What is the primary limitation of Rebuff's canary tokens?
- They only work on multimodal (image) content
- They prevent prompt injection from ever reaching the model
- They detect leakage after it has already occurred in the output, and can miss some leaks
- They require a 24GB GPU to run
A: They detect leakage after it has already occurred in the output, and can miss some leaks

Q: Why does Meta ship Prompt Guard as a model separate from Llama Guard, rather than having Llama Guard handle both content-safety classification and injection detection?
Feedback: Sample/expected answer: Because Llama Guard is itself an LLM and can be prompt-injected/attacked like any other LLM, so a distinct, purpose-built model (Prompt Guard) is used for injection detection instead

Q: A team only has access to a free Colab/Kaggle T4 GPU (\~16GB) and wants to run a Llama Guard model locally. What is the most realistic path described in this week's material?
- Run the full Llama Guard 4 12B model at full precision — 16GB is more than sufficient
- Use Llama Guard 3-1B, or run Llama Guard 4 12B in 4-bit quantization
- It's impossible to run any Llama Guard model without a paid GPU tier
- Convert Llama Guard to a CPU-only model with no quantization
A: Use Llama Guard 3-1B, or run Llama Guard 4 12B in 4-bit quantization