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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) |
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls |
| Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Explain the importance of physical security controls |
| Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack |
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NEW QUESTION # 66
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: B
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 # 67
A security analyst finds that the AI system is under a denial-of-wallet attack.
Which of the following should the analyst enforce to protect the company? (Choose two.)
Answer: C,F
Explanation:
Basic Concept: A denial-of-wallet (DoW) attack deliberately generates excessive API calls or token consumption to exhaust an organization ' s AI budget. Since LLM providers charge based on tokens processed, attackers can cause significant financial damage by driving massive usage. CompTIA SecAI+ Study Guide addresses financial abuse vectors in AI systems.
Why E is Correct: API rate controls limit the number of requests a user or application can make within a defined time period. By capping request frequency, rate controls directly prevent attackers from generating the massive API call volume needed to execute a denial-of-wallet attack.
Why F is Correct: Output token controls cap the maximum number of tokens the model can generate per response. Since billing is based on tokens consumed including outputs, limiting output tokens directly caps the cost per request, preventing attackers from triggering extremely long, expensive responses.
Why A is Wrong: Endpoint access controls manage device or network access. They do not directly limit token consumption or API call volume that drives denial-of-wallet costs.
Why B is Wrong: A CDN distributes content geographically to improve performance and absorb traffic. It does not control LLM API billing or token consumption.
Why C is Wrong: Model fine-tuning adjusts model parameters for improved performance on specific tasks. It is a training process that does not address active cost-exhaustion attacks.
Why D is Wrong: Modality controls restrict which input types such as text, images, or audio a model accepts.
While useful for reducing attack surface, they do not directly address the rate or volume of API calls in a DoW attack.
NEW QUESTION # 68
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:
Basic Concept: Protecting a released AI model from unauthorized modification requires controlling who can interact with it and at what privilege level. IAM-integrated API access provides granular, auditable control over model interactions. CompTIA SecAI+ Study Guide covers model protection through identity and access management integration.
Why D is Correct: Integrating an API with IAM roles ensures that all interactions with the model are authenticated and authorized according to precisely defined permissions. IAM roles enforce the principle of least privilege, ensuring users can query the model only within authorized scope and cannot modify model parameters, weights, or configuration. API-level access provides an abstraction layer that protects the underlying model from direct access while enabling controlled, auditable interactions.
Why A is Wrong: Changing to an LLM with guardrails addresses model behavior safety but does not protect the model artifacts themselves from tampering or unauthorized modification. It changes the model type rather than implementing access controls.
Why B is Wrong: Providing secure copies for local runtime distributes model copies to multiple endpoints, significantly increasing the attack surface for tampering. Each local copy represents a potential point of unauthorized modification.
Why C is Wrong: Restricting access to IT professionals is overly broad and vague. IT professionals may still need varying levels of access for different purposes, and generic role-based access without IAM integration and API mediation provides insufficient granularity to prevent unintentional modification.
NEW QUESTION # 69
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: A
Explanation:
Tokens are the most appropriate measurement because LLM processing and resource consumption are fundamentally tied to the number of tokens processed in prompts and generated in responses. CompTIA SecAI+ specifically includes token limits among gateway security controls and requires knowledge of AI monitoring for prompts, responses, processing, and cost.
Monitoring token consumption is particularly relevant when investigating denial-of-service or resource- exhaustion conditions. An attacker can submit excessive, repeated, or unusually large inputs that consume the model ' s context capacity and computational resources. Tracking token utilization therefore gives administrators a practical indicator of how heavily the LLM is being consumed and can support thresholds, quotas, rate controls, and anomaly detection.
A transformer is the neural-network architecture underlying many modern LLMs; it is not a unit used to measure utilization. A chain of thoughts refers to intermediate reasoning behavior and does not quantify model consumption. A prompt is an input submitted to the model, but simply counting prompts does not account for their potentially very different sizes. Token measurement therefore provides the most useful utilization metric.
NEW QUESTION # 70
A line of business wants to onboard an application that uses a custom AI model for employee assessments.
The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model.
Which of the following is the most appropriate to use for an AI threat model?
Answer: A
Explanation:
Basic Concept: Threat modeling for AI systems requires a framework specifically designed to address AI- specific attack techniques, tactics, and procedures. General cybersecurity or governance frameworks do not capture the unique adversarial attack surface of AI and ML systems. CompTIA SecAI+ Exam Objectives identify MITRE ATLAS as the primary AI threat modeling resource.
Why B is Correct: MITRE ATLAS (Adversarial Threat Landscape for AI Systems) is specifically designed as an AI and ML threat modeling framework. It catalogs real-world adversarial tactics, techniques, and procedures targeting AI systems, enabling security architects to identify and assess threats unique to ML models such as data poisoning, model extraction, and evasion attacks. It is the industry standard for AI- specific threat modeling.
Why A is Wrong: Responsible AI is a set of ethical principles and governance guidelines for developing and deploying AI systems fairly and safely. It addresses ethics and fairness, not technical adversarial threat modeling.
Why C is Wrong: The OECD provides non-binding policy recommendations and principles for AI governance at an international level. It does not provide technical threat modeling taxonomies or AI-specific attack catalogs.
Why D is Wrong: ISO standards such as ISO 42001 establish management system requirements for AI governance. They are compliance and management frameworks, not threat modeling tools for identifying adversarial AI attack vectors.
NEW QUESTION # 71
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