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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-assisted Security | 24% | - AI in security strategy and operations
|
| Topic 2: Securing AI Systems | 40% | - Defending against AI-specific attacks
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI-driven threats and risks
|
| Topic 4: AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
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NEW QUESTION # 66
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji.
Which of the following techniques best mitigates this type of attack?
Answer: C
Explanation:
Basic Concept: Context window DoS attacks flood an LLM ' s context with obfuscated content to exhaust processing resources or manipulate model behavior. Attackers may hide large amounts of text behind Unicode characters like emojis. CompTIA SecAI+ Study Guide identifies prompt filtering as the primary defense against input-based attacks on LLMs.
Why D is Correct: A prompt filter inspects incoming inputs before they reach the LLM, detecting and blocking malicious content including obfuscated text hidden behind Unicode characters or emojis. By analyzing input structure, character counts, hidden content, and encoding anomalies, prompt filters can identify and reject attacks that attempt to abuse the context window, preventing resource exhaustion.
Why A is Wrong: Fraud detection systems are designed to identify fraudulent transactions or activities in structured data contexts. They are not designed to inspect LLM prompt structures for obfuscated content attacks on context windows.
Why B is Wrong: LLM-as-a-judge uses a secondary LLM to evaluate the quality or safety of another model ' s outputs. It operates post-generation and cannot prevent a DoS attack that occurs during input processing before output is generated.
Why C is Wrong: Pattern recognition can identify known attack patterns but requires the attack to match pre- learned patterns. Novel obfuscation techniques using Unicode or emoji hiding may evade pattern-based detection without dedicated prompt filtering logic.
NEW QUESTION # 67
An architect is using the firm ' s recommended large language model (LLM) to find an internal solution for content management.
Given the following:
Which of the following controls is the best for mitigating this issue?
Answer: C
Explanation:
Basic Concept: LLM hallucinations occur when the model generates plausible-sounding but factually incorrect or fabricated information. For internal content management solutions where accuracy is critical, detecting and handling hallucinated responses before they are acted upon is essential. CompTIA SecAI+ Study Guide covers response validation as a mitigation for hallucination risks.
Why B is Correct: Response validation implements checks that verify the accuracy and relevance of LLM- generated responses before they are presented to users or acted upon. This can involve cross-referencing responses against authoritative internal data sources, using a secondary model to evaluate response accuracy, or implementing confidence scoring that flags low-confidence responses for human review. Response validation directly addresses the hallucination problem by catching inaccurate responses before they cause harm.
Why A is Wrong: Model training addresses hallucinations at the model level by providing more accurate training data or fine-tuning. While effective long-term, it requires significant time and resources and does not provide immediate protection against hallucinations in the currently deployed model.
Why C is Wrong: Access controls manage who can query the LLM and what resources they can access. They do not inspect or validate the accuracy of the model ' s responses, so they cannot mitigate hallucination risks.
Why D is Wrong: Integrity monitoring tracks whether data or systems have been tampered with or changed unexpectedly. It is relevant for detecting unauthorized modifications but does not validate whether LLM- generated content accurately reflects reality or internal authoritative data.
NEW QUESTION # 68
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones.
Which of the following techniques is the team most likely using?
Answer: A
Explanation:
Basic Concept: Modern penetration testing increasingly leverages AI to automate the reconnaissance, exploitation, and pivoting process. AI-assisted automated penetration testing can adapt its strategy based on previous results, simulating intelligent adversary behavior more realistically than static scripts. CompTIA SecAI+ covers AI-assisted offensive security techniques.
Why D is Correct: Automated penetration testing uses AI to systematically discover and attempt to exploit vulnerabilities while adapting tactics based on the results of previous attempts. The described behavior - looking at the current state, suggesting attack vectors, and adjusting based on outcomes - precisely describes an adaptive AI-driven penetration testing tool that iteratively explores the attack surface, mimicking how an advanced persistent threat would operate.
Why A is Wrong: Manual signature matching compares network traffic or files against a database of known threat signatures. It is a passive detection technique used by defensive tools like IDS/IPS, not an adaptive offensive technique for bypassing organizational boundaries.
Why B is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards. It is a development quality assurance activity, not an offensive security technique for testing organizational security boundaries.
Why C is Wrong: Fraud detection uses ML to identify suspicious patterns in transactions or user behavior for defensive purposes. It is a preventive security measure, not an offensive technique for penetration testing.
NEW QUESTION # 69
Which of the following is required first in order to send a prompt query and response in a language model (LLM) system when authentication is enabled?
Answer: D
NEW QUESTION # 70
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering. Which of the following is the most appropriate action?
Answer: C
Explanation:
Protecting the model from tampering requires controlled and auditable access. By integrating the model with an API secured by IAM roles, only authenticated and authorized users or systems can interact with it, ensuring integrity and preventing unauthorized changes.
NEW QUESTION # 71
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