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| Section | Weight | Objectives |
|---|---|---|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Securing AI Systems | 40% | - Adversarial Defense
|
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NEW QUESTION # 40
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 # 41
Which of the following is an example of how a security analyst uses generative AI in the triage process?
Answer: C
Explanation:
Basic Concept: Generative AI produces natural language content based on input data. In a security operations context, triage involves rapidly understanding and prioritizing security events. Generative AI ' s strength lies in synthesizing information and producing readable summaries from complex data. CompTIA SecAI+ Study Guide covers generative AI applications in security operations.
Why C is Correct: Summarizing security findings by category is a natural application of generative AI in triage. The AI can process large volumes of alerts and security events, group them by type or severity, and generate concise natural language summaries that enable analysts to quickly understand the current threat landscape without reading individual alerts. This directly reduces triage time and cognitive load.
Why A is Wrong: Predicting the next attack target requires predictive analytics and threat intelligence correlation. While AI can assist with this, it is a forecasting task better suited to analytical ML models rather than generative AI, and it is a strategic intelligence function rather than a triage task.
Why B is Wrong: Statistical analysis for malicious code assessment uses mathematical and ML techniques to analyze code characteristics. This is a traditional ML classification task, not a generative AI application, and is performed during malware analysis rather than alert triage.
Why D is Wrong: Tagging malware using ML algorithms is a classification task that uses supervised ML models trained on malware features. It is a detection and classification function, not a generative AI triage application.
NEW QUESTION # 42
Which control BEST prevents attackers from harvesting password hashes during lateral movement?
Answer: A
Explanation:
LSASS-protection solutions prevent hash extraction.
NEW QUESTION # 43
A company launches an AI application to monitor cloud misconfiguration and compliance. The AI application is shutting down development servers and opening ports during a client demonstration. Which of the following actions should the company take to return to normal operations and prevent future issues?
Answer: C
Explanation:
Option D is correct because the AI application is taking high-impact remediation actions without adequate human authorization. Human-in-the-loop control requires a qualified person to review and approve consequential actions, such as stopping servers or changing firewall exposure, before execution. The system may still detect misconfigurations, rank risks, and recommend fixes, but it should not autonomously disrupt development or alter network access during a demonstration. Option A restores service temporarily but leaves the unsafe decision path unchanged. Option B removes useful monitoring and does not correct the excessive automation. Option C might close improperly opened ports, yet it addresses only one symptom and does not prevent the AI from repeating other damaging actions. The stronger design is to roll back the unauthorized changes, restrict the agent's permissions, require approval for disruptive operations, and maintain an auditable record of recommendations and approvals. The NIST AI Risk Management Framework calls for appropriate human-oversight processes to be defined, assessed, and documented, supporting this governance pattern.
NEW QUESTION # 44
Which of the following is an example of how a security analyst uses generative AI in the triage process?
Answer: C
Explanation:
In triage, generative AI is most effective for quickly summarizing and categorizing large volumes of alerts or findings. This helps analysts prioritize incidents and streamline the investigation process.
NEW QUESTION # 45
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