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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 2: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Topic 4: AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 130
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: C
Explanation:
A secure MDLC is tailored to AI and ML systems, ensuring security is integrated throughout the model's design, training, validation, deployment, and monitoring phases. This directly addresses best practices for securing systems with AI models.
NEW QUESTION # 131
What control reduces the impact radius when a single host is compromised?
Answer: D
Explanation:
Segmentation isolates systems and limits lateral movement.
NEW QUESTION # 132
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: D
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 133
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: C
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 # 134
Which of the following resources most accurately covers the most widely recognized threat vectors and common vulnerabilities related to large language model (LLM) applications?
Answer: D
Explanation:
OWASP is the best answer because it publishes the Top 10 for Large Language Model Applications, a resource specifically structured around significant security vulnerabilities and attack patterns affecting LLM- based applications. CompTIA SecAI+ Objective 2.1 directly lists the OWASP Top 10 and specifically the LLM Top 10 as an AI threat-modeling resource. MITRE ATLAS is also highly relevant to AI security, but its purpose is broader: it catalogs adversarial tactics, techniques, and procedures associated with attacks against AI and machine learning systems. The MIT AI Risk Repository aggregates a broad range of AI risks rather than functioning as the recognized LLM application vulnerability Top 10. NIST AI RMF provides a risk- management framework for governing and managing AI risks but is not primarily a vulnerability catalog for LLM applications. Since the question asks for the resource that most directly captures widely recognized threat vectors and common vulnerabilities associated specifically with LLM applications, OWASP ' s LLM- focused Top 10 is the most precise match.
NEW QUESTION # 135
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