Back to LLM / GenAI & Prompt/Context Engineering
About this interview
A technical interview on LLM Security & Adversarial Prompting, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Distinguish prompt injection, jailbreaking, and data/prompt leaking attacks with concrete examples
Explain indirect prompt injection and why it is especially dangerous in agentic systems
Describe training-time attacks: data poisoning and backdoor triggers
Explain defensive measures: input sanitization, output filtering, red teaming, and guardrail frameworks
Topics covered
Attack taxonomyPrompt injectionIndirect prompt injectionJailbreakingTraining-time attacksIndustry frameworksDefensive measuresRed teamingAgentic securityModel selection for securityAlignment vulnerabilitiesMulti-agent securitySecurity governancePrivacy attacks
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“How would you describe, in plain terms, the difference between prompt injection and jailbreaking? Why do practitioners treat them as separate threat categories?
“What is output filtering in the context of LLM security? How does it complement input sanitization, and what failure modes does it have?
“What is supply-chain risk in the context of fine-tuned or open-weight LLMs? How could a poisoned model published to a model hub affect downstream applications?