LLM Safety & Security
You will learn how to ship LLM features that survive hostile input: layered injection defenses, guardrails, moderation, PII handling, and safely scoped tool-using agents. Interviewers probe this to check you can defend a production AI product, not just build one.
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- LLM Threat Modeling5 questions
- Prompt Injection Defense5 questions
- Guardrails & Output Constraining5 questions
- Content Moderation4 questions
- PII & Data-Leakage Handling4 questions
- Safe Tool Use for Agents5 questions
- Safety Evaluation & Red-Team Testing5 questions
questions
page 2 of 2How do you decide an LLM assistant is safe enough to launch, given ASR never reaches zero?
basics
~20 sYou do not launch on a zero. You launch on a bounded worst case: a per-category attack-success curve against a stated attacker budget, evidence that a success cannot cause unrecoverable harm, and a detection and response plan sized for the residual rate you are accepting.
How do you turn an LLM risk map into prioritized controls and accepted residual risk?
basics
~20 sRank by the blast radius of the worst action the system can actually take, not by how likely the model is to misbehave — against an adaptive attacker that likelihood is near one. Then separate deterministic boundaries from probabilistic mitigations and get the downstream owner to sign the remainder.
What does mapping an LLM risk onto MITRE ATLAS add beyond the OWASP list?
basics
~20 sOWASP gives builders a checklist of vulnerability classes. ATLAS gives an ATT&CK-shaped vocabulary of adversary tactics and techniques against AI systems, so an AI finding lands in the same register, detection engineering and reporting the rest of the organisation already uses.
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