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AI Security

Interview question · How LLM Systems Work

What are embeddings, and why do they matter for security filters and for data leakage?

Embeddings are not hashes. The scored answer explains what they capture, how attackers exploit similarity, and why a vector store deserves the same protection as the documents.

mediumNewUpdated Nov 2026

Embeddings are not hashes. The scored answer explains what they capture, how attackers exploit similarity, and why a vector store deserves the same protection as the documents.

Make your first attempt

What would you say before reading the answer?

  1. 01 State the assumptions your answer depends on.
  2. 02 Explain the mechanism and choose a control.
  3. 03 Name a condition that would change your decision.

The worked answer includes an approach, scoring criteria and follow-up questions. Compare the reasoning with your own.

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What if · the follow-ups in this answer

The answer works through 5 follow-ups like an interviewer would

A fictional makerspace stores embeddings of manuals and restricted maintenance records. A proposal says vectors can be shared freely because they contain numbers rather than sentences.

  1. What if a restricted record is the query’s nearest neighbor?
  2. What if a partner receives only vectors and source identifiers?
  3. What if a new embedding model outputs the same number of dimensions?
  4. What if a similarity detector rejects several legitimate maintenance questions?
  5. How would you approve the vector-store design?

Each rung has a nudge, a model answer, the principle that carries over and the weak answer to avoid. Premium opens every answer and ladder.

UP NEXT ON YOUR JOURNEY
Next in this trackIf the model is forced to return valid JSON matching a schema, is its output safe to use?Next in this trackHow would you use Google's Secure AI Framework (SAIF) and its risk map to choose controls for a new AI product?Next in this trackCan fine-tuning remove a model's safety behaviour? What does that mean for a product team?
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