AI0-001 AI Security Practice Question
An AI security team is mapping threats specific to their ML pipeline using the STRIDE framework. Which threat category is primarily addressed by ensuring that training data is not tampered with?
⚠ Common exam trap
CompTIA AI exams often test the distinction between Tampering (data integrity) and Spoofing (identity deception), so candidates may confuse 'tampering with data' with 'spoofing a data source' and incorrectly choose Spoofing.
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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Tampering
Ensuring that training data is not tampered with directly addresses the Tampering threat category in the STRIDE framework. Tampering involves the unauthorized modification of data, and in an ML pipeline, corrupted training data can lead to model poisoning, where the model learns incorrect patterns or backdoors. By protecting the integrity of the training dataset, the team mitigates the risk of adversarial manipulation that could degrade model performance or introduce vulnerabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Spoofing
Why it's wrong here
Spoofing concerns impersonating a legitimate identity or entity, countered by authentication, not by protecting training data from modification. It would be the correct category when an attacker impersonates a user, service account, or model endpoint to gain pipeline access.
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Tampering
Why this is correct
Tampering covers unauthorised modification of data or artefacts, so protecting training data integrity maps directly onto this STRIDE category. It satisfies the stem's constraint by naming the threat addressed when tampering with the ML pipeline's training set is prevented.
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Repudiation
Why it's wrong here
Repudiation concerns denying that an action occurred, addressed through logging and non-repudiation controls, not data tampering. It would be the correct category when the threat is a user or process denying having submitted training data or triggered a pipeline action.
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Information disclosure
Why it's wrong here
Tampering with training data corrupts model integrity, which STRIDE classes as Tampering, not Information disclosure. Information disclosure covers unauthorised exposure of data or model details; it would be the right category when the concern is confidentiality of training data or model outputs.
About these practice questions
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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.