Title: Quiz 11 — Security, Guardrails, and Responsible-AI Hardening
Course: AIINFRA 303: Capstone Project > Section: Week 11 — Security, Guardrails, and Responsible-AI Hardening

Q: Which risk has held the #1 position in both the 2023 and 2025 OWASP Top 10 for LLM Applications?
- Supply Chain Vulnerabilities
- Prompt Injection
- Model Denial of Service
- Insecure Output Handling
A: Prompt Injection

Q: Prompt injection is fundamentally exploitable because LLMs process instructions and untrusted data through the same channel, with no architectural separation between the two.
- True
- False
A: True

Q: A malicious instruction hidden inside a webpage that your RAG pipeline retrieves and feeds to the model, without the user typing anything malicious themselves, is an example of:
- Direct prompt injection
- Indirect prompt injection
- Model inversion
- Supply chain compromise
A: Indirect prompt injection

Q: Because guardrails add meaningful protection, a well-written system prompt instructing the model to "never reveal secrets" is sufficient on its own to stop prompt injection.
- True
- False
A: False

Q: Name two of the five rail types NVIDIA NeMo Guardrails lets you implement via Colang.
Feedback: Sample/expected answer: Any two of: input rails, dialog rails, retrieval rails, execution rails, output rails.

Q: Once an LLM-based agent can call external tools, which control becomes essential rather than optional?
- A longer, more detailed system prompt
- Least-privilege tool permissions and runtime guardrails
- A larger context window
- Fine-tuning on more conversational data
A: Least-privilege tool permissions and runtime guardrails

Q: Signature-based keyword or pattern filters can fully catch prompt injection attempts because injection attacks always use a small, predictable set of phrases.
- True
- False
A: False

Q: What is Llama Guard 3 used for, and how does it complement NeMo Guardrails' Colang rails?
Feedback: Sample/expected answer: Llama Guard 3 is a classifier model used for input/output safety classification — scoring prompts and responses for safety violations. It complements Colang's rule-based rails by adding semantic, model-based judgment alongside NeMo Guardrails' structured, programmable rail logic, together forming layered defense-in-depth.