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Exin AISP Exam Questions

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Association of Information Security Professional (AiSP)

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Exin AISP Sample Questions – Free Practice Test & Real Exam Prep

Question #1

5.1 Privacy and AI security · Intermediate A customer requests erasure of her personal data, which was in the training set of a deployed model. What is the main challenge?

  • A. None; deleting the database row is enough and the model is unaffected 
  • B. Deleting the source record doesn't remove what the model learned, which may require retraining or other measures
  • C. The GDPR doesn't apply to models 
  • D. The request can be refused because the data is anonymized by training 
Correct answer: B
Explanation. Data subject rights extend to processing through models. Influence on
model parameters persists after source deletion.
Why the other options are incorrect. A ignores memorization. C is false. D wrongly
assumes training anonymizes data.
Key principle: Plan for data subject rights before training. · Objective: 5.1.1 · Reference:
OWASP AI Exchange Ch. 6, Privacy rights
Question #2

5.1 Privacy and AI security · Foundational Which privacy concern is specific to AI systems?

  • A. A misconfigured firewall 
  • B. Password reuse by staff 
  • C. Personal data can be memorized by a model and later revealed or inferred through its outputs 
  • D. Backups stored on tape 
Correct answer: C
Explanation. Models can retain personal data and expose it through outputs, inversion or membership inference, a risk ordinary databases don't have. Whytheother options areincorrect. A, B and D are general IT security concerns. 
Key principle: A model can itself be a store of personal data. · Objective: 5.1.1 · Reference: OWASP AI Exchange Ch. 6, Model attacks
Question #3

4.2 AI security testing strategies · Advanced During testing, a jailbreak succeeds in roughly 1 of 20 attempts. How should this be handled?

  • A. Run repeated trials and report the attack success rate, recording model version and configuration, because outputs are non-deterministic
  • B. Discard the result as flaky 
  • C. Report it as passed, since 19 of 20 attempts were blocked 
  • D. Test once at temperature 0 and treat that result as definitive 
Correct answer: A
 Explanation. Evaluations characterize behavior statistically. Results depend on model version, configuration and context, which must be recorded. Why the other options are incorrect. B and C ignore a real vulnerability attackers can retry. D gives false certainty. Key principle: In probabilistic systems, report rates, not anecdotes. · Objective: 4.2.1 · Reference: OWASP AI Exchange Ch. 0, AI engineering primer; Ch. 5
Question #4

4.2 AI security testing strategies · Intermediate After fixes for prompt injection findings are deployed, what should the test team do?

  • A. Close the tickets 
  • B. Rely on the vendor's assurance 
  • C. Retest only the exact original payloads 
  • D. Rerun the original attacks plus variations, and repeat after model or prompt changes 
Correct answer: D
 Explanation. Validating fixes requires variants to catch narrow patches. Model, prompt or tool changes can invalidate earlier results, so retest after changes. Why the other options are incorrect. A and B skip validation. C misses bypasses through rephrasing or encoding. Key principle: A fix isn't done until it survives variation. · Objective: 4.2.1 · Reference: OWASP AI Exchange Ch. 0, AI engineering primer; Ch. 5 
Question #5

4.2 AI security testing strategies · Foundational In a structured AI security testing approach, what comes first?

  • A. Running an automated jailbreak tool 
  • B. Defining objectives and scope based on the threat model 
  • C. Retraining the model 
  • D. Writing the report 
Correct answer: B
Explanation. Scope and objectives, derived from the threat model, determine which attacks, tools and success criteria apply. Why the other options are incorrect. A without scope is unfocused. C is remediation, not testing. D comes last. Key principle: Test what matters, deliberately. · Objective: 4.2.1 · Reference: OWASP AI Exchange Ch. 5, Introductio
Question #6

4.1 Threats scope · Intermediate A deployer uses a ready-made, provider-hosted LLM. What belongs in its own AI security test scope?

  • A. Testing the provider's training pipeline for poisoning 
  • B. Penetration-testing the provider's data center 
  • C. Its own integration: injection through its inputs and retrieval, disclosure of its data, and output handling
  • D. Nothing; the provider has tested the model 
Correct answer: C
 Explanation. The deployer tests what it controls: its configuration, data flows and output use. Why the other options are incorrect. A and B are outside its control and handled through supplier assurance. D ignores application-layer risk. Key principle: The model is not the system; test the system. · Objective: 4.1.2 · Reference: OWASP AI Exchange Ch. 0, AI engineering primer; Ch. 5
Question #7

4.1 Threats scope · Advanced A company deploys a generative AI agent that reads documents and calls internal APIs. Which threats should its tests prioritize?

  • A. Indirect prompt injection through ingested content leading to unauthorized tool actions, plus insecure output handling downstream
  • B. Adversarial pixel perturbations 
  • C. Label flipping during training, even though the company uses a hosted model 
  • D. Model drift 
Correct answer: A
Explanation. For agents, untrusted content combined with tool access is the key risk; output that flows into other systems can carry injection. Why the other options are incorrect. B is image evasion. C is outside the deployer's training scope. D is a performance issue rather than a security threat. Key principle: Test where untrusted data meets privileges. · Objective: 4.1.2 · Reference: OWASP AI Exchange Ch. 5; Ch. 0, Agentic AI overview
Question #8

4.1 Threats scope · Intermediate A bank tests its predictive credit-scoring model. Which threats should be prioritized beyond conventional testing?

  • A. Prompt injection and jailbreaks 
  • B. Evasion and poisoning robustness, plus membership inference 
  • C. Insecure Markdown rendering 
  • D. System prompt leakage 
Correct answer: B
Explanation. Predictive models are mainly tested for evasion and poisoning; models trained on personal data also warrant inference-attack testing. Whytheother options areincorrect. A, C and D are generative AI concerns. Key principle: Test for the threats your AI type actually faces. · Objective: 4.1.2 · Reference: OWASP AI Exchange Ch. 5, Threats to test for 
Question #9

4.1 Threats scope · Intermediate An LLM application passed a penetration test with no web vulnerabilities found, but model behavior was out of scope. What is missing?

  • A. Nothing; the pen test is sufficient 
  • B. Load tests 
  • C. A code review 
  • D. AI-specific tests, such as prompt injection, sensitive data disclosure and insecure output handling, which conventional testing does not cover
Correct answer: D
Explanation. Conventional tests examine the application and infrastructure. Attacks through model behavior require AI-specific testing. Why the other options are incorrect. A ignores the AI attack surface. B and C don't test model behavior under attack. Key principle: AI testing extends, not replaces, conventional testing. · Objective: 4.1.1 · Reference: OWASP AI Exchange Ch. 0, How to organize (Adapt); Ch. 5
Question #10

4.1 Threats scope · Foundational What is the primary purpose of AI security testing?

  • A. Measuring accuracy on normal, expected inputs 
  • B. Load-testing the inference service 
  • C. Simulating adversarial behavior to uncover AI-specific vulnerabilities and verify that defenses hold 
  • D. Confirming the system meets business requirements 
Correct answer: C
Explanation. AI security tests simulate adversarial behavior to uncover vulnerabilities,
weaknesses and risks in AI systems.
Why the other options are incorrect. A is functional validation. B is performance testing.
D is quality assurance.
Key principle: Test as an attacker would. · Objective: 4.1.1 · Reference: OWASP AI
Exchange Ch. 5, AI security testing

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