- A
Data poisoning attack
Why wrong: Data poisoning involves corrupting training data to manipulate model behavior, not extracting system prompts from a deployed model.
- B
Prompt leaking via indirect prompt injection
Prompt leaking occurs when an adversary forces the model to output its system prompt, often through indirect injection in user-supplied data.
- C
Membership inference attack
Why wrong: Membership inference determines if a specific record was used in training, not extracting prompts from the model's response.
- D
Model inversion attack
Why wrong: Model inversion aims to reconstruct private training data from model outputs, not to steal system prompts.
Question 714 of 1,000
AI0-001 AI Security Practice Question
This AI0-001 practice question tests your understanding of ai security. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A security analyst at a fintech company is alerted to anomalous API requests to their deployed LLM chatbot. The requests contain carefully crafted inputs that cause the model to generate responses that include internal system prompts. Which type of attack is MOST likely occurring?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Prompt leaking via indirect prompt injection
The attack exploits the LLM's inability to distinguish between user-supplied instructions and system-level prompts. By crafting inputs that include hidden or indirect instructions, the attacker causes the model to output its internal system prompt, which is a classic prompt leaking scenario achieved via indirect prompt injection.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data poisoning attack
Why it's wrong here
Data poisoning involves corrupting training data to manipulate model behavior, not extracting system prompts from a deployed model.
- ✓
Prompt leaking via indirect prompt injection
Why this is correct
Prompt leaking occurs when an adversary forces the model to output its system prompt, often through indirect injection in user-supplied data.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Membership inference attack
Why it's wrong here
Membership inference determines if a specific record was used in training, not extracting prompts from the model's response.
- ✗
Model inversion attack
Why it's wrong here
Model inversion aims to reconstruct private training data from model outputs, not to steal system prompts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between attacks that target training data (poisoning, inversion, membership inference) versus attacks that exploit the inference-time behavior of LLMs, leading candidates to confuse prompt injection with data poisoning.
Trap categories for this question
Command / output trap
Model inversion aims to reconstruct private training data from model outputs, not to steal system prompts.
Detailed technical explanation
How to think about this question
Indirect prompt injection works by embedding instructions within user-controlled content (e.g., a web page or document) that the LLM processes. The model's attention mechanism may prioritize these injected instructions over its original system prompt, especially if the injected text mimics the format or authority of system directives. In real-world deployments, this can be mitigated by strict input sanitization, output filtering, and using separate processing pipelines for user content versus system instructions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security — This question tests AI Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Prompt leaking via indirect prompt injection — The attack exploits the LLM's inability to distinguish between user-supplied instructions and system-level prompts. By crafting inputs that include hidden or indirect instructions, the attacker causes the model to output its internal system prompt, which is a classic prompt leaking scenario achieved via indirect prompt injection.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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