AI0-001 AI Security Practice Question
An attacker repeatedly queries a public LLM API with carefully crafted inputs to reconstruct the model's architecture and approximate weights. This is an example of which attack?
⚠ Common exam trap
CompTIA AI often tests the distinction between model extraction (stealing the model) and model inversion (reconstructing training data), so the trap here is confusing 'reconstructing the model's architecture and weights' with 'reconstructing training samples' from model outputs.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
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Model extraction
Model extraction attacks involve querying a public API with carefully crafted inputs to reconstruct a target model's architecture and approximate weights. By analyzing the outputs (e.g., logits or probabilities), an attacker can train a substitute model that mimics the original, enabling offline exploitation or competitive intelligence. This directly matches the scenario described.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Model extraction
Why this is correct
Model extraction (model stealing) uses repeated API queries to clone a model's functionality or infer its parameters. The crafted inputs probe decision boundaries, letting the attacker approximate weights and architecture without direct access, directly matching the scenario's reconstruction goal.
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Data poisoning
Why it's wrong here
Data poisoning corrupts training data to embed malicious behaviour or backdoors in the model, altering its outputs rather than extracting information. It is tempting because both involve adversarial manipulation of an AI system, but it would be the correct answer if the attacker were contaminating the training pipeline, not querying a deployed API.
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Membership inference
Why it's wrong here
Membership inference determines whether a specific data record was part of the training set, not the model's architecture or weights. The attacker here reconstructs structural parameters via repeated queries, which is model extraction. It is tempting because both attacks exploit query access to a model, but membership inference targets individual record presence, not the model's internal configuration.
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Model inversion
Why it's wrong here
Model inversion reconstructs training-data examples or sensitive attributes from a model's outputs, targeting the data rather than the architecture or weights. It is tempting because it also abuses repeated queries, but it would be the correct answer if the attacker were recovering training records, not approximating parameters.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.