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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 2: Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| Topic 3: Securing AI Systems | 40% | - AI System Protection
|
| Topic 4: AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 82
A human resources officer is using AI to evaluate resumes and help select candidates that meet minimum criteria. To improve the results, the human resources officer adjusts the query parameters and includes an example resume that matches a successful candidate. Which if the following best describes this query?
Answer: C
Explanation:
One-shot prompting provides the model with a single example (in this case, a successful resume) to guide how it should process future inputs. This technique helps the AI better align its output with the desired evaluation criteria.
NEW QUESTION # 83
An architect is creating a threat model for an agentic system.
Which of the following should the architect do first?
Answer: C
Explanation:
Basic Concept: Threat modeling for any system, and especially for agentic AI systems with multiple interacting components, begins with understanding the system ' s architecture and where trust boundaries exist. Trust boundaries define where data and control flows cross between components with different trust levels, representing potential attack surfaces. CompTIA SecAI+ Study Guide aligns with STRIDE and MITRE ATLAS threat modeling methodologies.
Why B is Correct: Identifying trust boundaries between components is the foundational first step in threat modeling. Agentic systems often involve multiple components such as the orchestrator, tools, APIs, data sources, and external services with different trust levels. Understanding where these boundaries exist reveals where untrusted inputs cross into trusted components, enabling the architect to systematically identify threats at each boundary before proceeding to risk quantification and control application.
Why A is Wrong: Applying compensating controls based on exposure findings is the final step in threat modeling, occurring after threats have been identified and risks quantified. Controls cannot be appropriately designed without first understanding the system ' s trust boundaries and threat landscape.
Why C is Wrong: Calculating risk to resources based on data sensitivity is a risk assessment step that occurs after trust boundaries are mapped and potential threats are identified. Risk quantification requires knowing what threats exist at each boundary first.
Why D is Wrong: Scanning for OWASP Top 10 vulnerabilities is a technical vulnerability assessment activity. While valuable, it comes after the architectural analysis of trust boundaries and threat identification phases of threat modeling.
NEW QUESTION # 84
Which of the following is an example of how a security analyst uses generative AI in the triage process?
Answer: A
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 # 85
A security analyst notices that regardless of user-submitted prompts, an AI model always returns unsanitized responses. These responses are then passed to multiple plug-ins. The analyst is concerned with the potential security implications.
Which of the following Open Worldwide Application Security Project (OWASP) categories addresses this vulnerability?
Answer: A
Explanation:
Basic Concept: OWASP has published the Top 10 vulnerabilities for Large Language Model Applications, each addressing a distinct category of LLM security risk. Understanding which OWASP category maps to specific LLM vulnerability scenarios is a key competency in the CompTIA SecAI+ Study Guide under securing AI systems.
Why D is Correct: Improper output handling (OWASP LLM02) occurs when an application passes LLM- generated outputs to downstream systems such as plug-ins, web browsers, or databases without proper validation, sanitization, or encoding. This can enable XSS, SQL injection, remote code execution, or other injection attacks against plug-ins and downstream systems. The scenario exactly matches this: unsanitized AI responses are automatically passed to multiple plug-ins, which could execute malicious content in the model ' s output.
Why A is Wrong: Misinformation refers to the AI generating false or misleading content that users might believe. It is a content accuracy concern related to hallucinations and false information propagation, not a vulnerability describing how model outputs are handled by downstream systems.
Why B is Wrong: Prompt injection involves crafting inputs to manipulate model behavior and override instructions. While it can be a contributing cause of unsafe outputs, the vulnerability described - passing unsanitized outputs to plug-ins - is specifically the output handling failure, not the injection mechanism itself.
Why C is Wrong: Unbounded consumption (OWASP LLM10) refers to resource exhaustion attacks including denial-of-wallet and denial-of-service through excessive token consumption. It addresses resource management vulnerabilities, not the security implications of passing model outputs to downstream systems.
NEW QUESTION # 86
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack.
Which of the following should the company use to measure the utilization of the LLM?
Answer: B
Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.
NEW QUESTION # 87
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