Question 1

What is a prompt injection attack, and how does it differ from traditional SQL injection?

Accepted Answer

Prompt injection: malicious text in user input causes the LLM to override its instructions or leak system prompt contents. Unlike SQL injection (code/data confusion), there's no clear parser boundary — the model interprets both instructions and data as natural language.

Question 2

Explain adversarial examples in machine learning. How are they generated and how can models be made more robust against them?

Accepted Answer

Adversarial examples: small, human-imperceptible perturbations to inputs that cause misclassification. Generated via FGSM or PGD attacks. Defenses: adversarial training (include adversarial examples in training), input preprocessing, ensemble methods, and certified defenses.

Question 3

What is model inversion, and what data privacy risk does it pose?

Accepted Answer

Model inversion: an attacker queries a model repeatedly to reconstruct training data or approximate a specific individual's attributes. Mitigations: differential privacy during training, output perturbation, rate limiting inference API.

Question 4

How do you assess and mitigate supply chain risks in AI systems (e.g., third-party models or datasets)?

Accepted Answer

Cover model provenance verification, scanning for backdoors (trojan detection), dataset poisoning detection (anomaly analysis on training labels), and contractual/legal controls for vendor-provided models.

Question 5

Tell me about an AI security vulnerability you discovered in a system you were responsible for. How did you handle it?

Accepted Answer

Cover discovery method (red team exercise, threat model, external report), severity assessment, responsible disclosure process, remediation timeline, and what detection you added to prevent future recurrence.

Question 6

Describe a time you had to balance AI system capability and security restrictions when stakeholders wanted to remove safety controls for performance.

Accepted Answer

Show principled negotiation: quantify the security risk the control addresses, propose alternative mitigations that preserve some capability, and document the tradeoff decision with risk acceptance from the right authority level.

Question 7

Tell me about how you approach threat modeling for an AI-powered product.

Accepted Answer

Apply STRIDE or MITRE ATLAS to AI-specific threat surfaces: model extraction, training data poisoning, adversarial inputs, prompt injection, and privacy inference attacks. Show you go beyond traditional security threat modeling.

Question 8

Design a red team program for a company deploying LLMs in customer-facing products.

Accepted Answer

Cover red team scope (jailbreaks, prompt injection, data extraction, role-play abuse), structured attack categories (MITRE ATLAS), findings taxonomy, severity scoring, remediation tracking, and cadence (pre-launch + ongoing).

Question 9

How would you design a content moderation system for an LLM application that must prevent harmful outputs without over-blocking legitimate use?

Accepted Answer

Cover layered approach: input classifier (intent detection), output classifier (harm scoring), human review queue for borderline cases, feedback loop from user reports, and A/B testing of threshold adjustments for precision/recall balance.

Question 10

Design a zero-trust security architecture specifically for an ML training and inference infrastructure.

Accepted Answer

Cover data access controls (training data with row-level permissions), model artifact signing and integrity checks, inference API authentication, network segmentation (training cluster vs serving cluster), and audit logging for model access.

AI Security Specialist Interview Questions 2026

Technical

Behavioral

System Design