CompTIA SecAI+ Practice Questions: 2.0 Securing AI Systems

37 free, exam-style CompTIA SecAI+ (CY0-001) practice questions covering 2.0 Securing AI Systems. Each question shows the correct answer and a clear explanation. Ready for the real thing? Take the full timed quiz below.

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Q1. Which of the following attacks involves an adversary manipulating the input data used to train a machine learning model to introduce a backdoor or bias?

Explanation: Data poisoning occurs when an attacker injects malicious data into the training set, causing the model to learn incorrect patterns or backdoors. Learn more.

Q2. According to the OWASP Top 10 for LLM, which vulnerability occurs when an attacker manipulates the LLM through crafted inputs to bypass safety guardrails?

Explanation: Prompt Injection is the direct manipulation of a Large Language Model (LLM) via inputs to make it ignore previous instructions or perform unintended actions. Learn more.

Q3. A security engineer is implementing a gateway control to prevent Denial of Service (DoS) attacks against an internal LLM API. Which control would be MOST effective?

Explanation: Rate limiting (and token limits) restricts the number of requests a user can make in a given timeframe, effectively mitigating DoS attacks aimed at exhausting API resources. Learn more.

Q4. Which attack type aims to reconstruct the training data or extract sensitive features of a specific individual from the model's output?

Explanation: Model inversion attacks exploit the model's output confidence scores to reconstruct the input data or features used during training. Learn more.

Q5. To secure an AI system's data at rest, which of the following is a standard cryptographic requirement?

Explanation: AES-256 is a standard symmetric encryption algorithm used to secure data at rest. TLS is used for data in transit. Learn more.

Q6. An attacker is using an 'indirect prompt injection' technique. How is this attack typically delivered?

Explanation: Indirect prompt injection occurs when an LLM processes external content (like a webpage or email) that contains hidden instructions designed to manipulate the LLM. Learn more.

Q7. Which resource would be BEST for a security team to reference when looking for a knowledge base of adversary tactics and techniques specifically targeting AI systems?

Explanation: MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is specifically designed to categorize attacks against AI/ML systems. Learn more.

Q8. A developer wants to ensure that the LLM only answers questions related to the company's HR policies and refuses all other topics. Which control should be implemented?

Explanation: System prompts (or system roles) set the behavior, context, and boundaries for the AI model, instructing it on what it can and cannot discuss. Learn more.

Q9. In the context of AI security, what does 'Jailbreaking' refer to?

Explanation: Jailbreaking in AI refers to prompt engineering techniques designed to trick the model into bypassing its built-in safety mechanisms and ethical guidelines. Learn more.

Q10. Which data processing technique involves replacing sensitive data identifiers with pseudonyms to protect privacy while maintaining analytical utility?

Explanation: Data masking or tokenization replaces sensitive data with non-sensitive substitutes (pseudonyms) to protect PII/PHI. Learn more.

Q11. Which of the following represents a 'Membership Inference Attack'?

Explanation: Membership inference attacks allow an adversary to determine whether a specific individual's data was used to train a machine learning model, posing a privacy risk. Learn more.

Q12. What is the purpose of 'Red Teaming' in the context of LLM security?

Explanation: AI Red Teaming involves ethical hackers attempting to force the model to generate harmful content, bypass guardrails, or reveal sensitive info to improve security. Learn more.

Q13. Which technique allows an attacker to manipulate an AI system by creating input that is visually indistinguishable to humans but misclassified by the computer?

Explanation: Adversarial examples are inputs (like images with added noise) designed to cause a machine learning model to make a mistake, often invisible to the human eye. Learn more.

Q14. Which of the following is a supply chain attack vector specific to AI?

Explanation: Attackers can upload malicious models (pickled files) to public hubs like Hugging Face. When developers download and load them, arbitrary code can execute. Learn more.

Q15. Which of the following is a method to mitigate 'Prompt Injection' attacks?

Explanation: Separating instructions (System Prompts) from user data and using architectural sandboxing helps prevent user input from overriding system commands. Learn more.

Q16. What is a 'Sponge Attack' in the context of AI?

Explanation: Sponge attacks target the hardware implementation of deep learning models to soak up system resources (energy and latency), causing DoS. Learn more.

Q17. Which of the following is a method to detect 'Hallucinations' in LLM outputs?

Explanation: Monitoring the response confidence level (log-probs) can help identify when a model is unsure, which often correlates with hallucinations. Learn more.

Q18. What is the primary security concern regarding 'Excessive Agency' in AI agents?

Explanation: Excessive Agency occurs when an LLM-based agent is given the ability to interface with other systems (plugins, APIs) with excessive permissions, leading to unintended damaging actions. Learn more.

Q19. An adversary uses a 'Transfer Learning Attack'. What are they doing?

Explanation: In transfer learning attacks, adversaries use pre-trained models to build a surrogate model that mimics the target, allowing them to craft adversarial examples that work on the target. Learn more.

Q20. Which monitoring technique involves checking the output of an LLM for toxic, biased, or policy-violating content before showing it to the user?

Explanation: Output filtering or content moderation (often done by a separate smaller model) ensures that the generated response adheres to safety policies. Learn more.

Q21. Which organization publishes the 'AI Risk Repository' mentioned in the exam objectives?

Explanation: The MIT AI Risk Repository is a comprehensive database of AI risks referenced in the SecAI+ objectives. Learn more.

Q22. Which attack hides malicious instructions inside user-supplied content so an LLM ignores the system intent?

Explanation: Prompt injection attempts to override or manipulate model behavior through crafted input. Learn more.

Q23. Which control helps prevent an AI assistant from returning secrets from connected tools?

Explanation: Least-privilege tool access and filtering reduce the chance that sensitive tool outputs are exposed. Learn more.

Q24. Which technique tests an AI system by intentionally crafting inputs to cause unsafe or incorrect behavior?

Explanation: Adversarial testing evaluates how models behave under malicious, ambiguous, or edge-case inputs. Learn more.

Q25. Which AI security risk involves sensitive examples being exposed from training data?

Explanation: Training data leakage can reveal sensitive information memorized or inferred from model training data. Learn more.

Q26. Which control can reduce harmful LLM output before it reaches a user?

Explanation: Guardrails and classifiers can detect and block policy-violating or unsafe outputs. Learn more.

Q27. Which attack poisons examples so a model learns attacker-chosen behavior?

Explanation: Data poisoning manipulates training data to degrade performance or implant unwanted behavior. Learn more.

Q28. Which security control limits damage if an AI tool is tricked into calling an external API?

Explanation: Scoped tokens restrict what a tool can do even if a prompt injection or workflow error occurs. Learn more.

Q29. Which monitoring signal may indicate an LLM application is under prompt injection attack?

Explanation: Prompt injection attempts often include language that asks the model to ignore instructions, reveal secrets, or misuse tools. Learn more.

Q30. Which control limits an LLM agent so it can read tickets but cannot delete them?

Explanation: Tool-level least privilege restricts actions available to the agent and limits damage from mistakes or prompt injection. Learn more.

Q31. Which model attack attempts to infer whether a specific record was part of training data?

Explanation: Membership inference attacks try to determine whether a particular example was used in model training. Learn more.

Q32. Which risk can occur if retrieved documents contain attacker-written instructions?

Explanation: Indirect prompt injection hides malicious instructions in content that the model later retrieves or summarizes. Learn more.

Q33. Which control reduces the chance an AI assistant exposes another tenant data?

Explanation: Tenant isolation prevents data and permissions from crossing customer or organizational boundaries. Learn more.

Q34. Which term describes intentionally crafted input that causes a model to misclassify?

Explanation: Adversarial examples are inputs designed to cause incorrect model outputs. Learn more.

Q35. Which AI system risk is reduced by human approval before high-impact actions execute?

Explanation: Human-in-the-loop approval can prevent unsafe automated actions in sensitive workflows. Learn more.

Q36. Which AI attack attempts to reconstruct parts of training data from model outputs?

Explanation: Model inversion attacks attempt to infer sensitive training characteristics or examples from model behavior. Learn more.

Q37. Which control helps stop an AI assistant from making unsupported factual claims?

Explanation: Requiring source-grounded answers and citations helps users verify outputs and reduces unsupported claims. Learn more.

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