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| Section | Weight | Objectives |
|---|---|---|
| AI-assisted Security | 24% | - Security automation and orchestration
|
| Securing AI Systems | 40% | - Defending against AI-specific attacks
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| AI Governance, Risk and Compliance | 19% | - Governance frameworks and policies
|
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NEW QUESTION # 35
Which of the following technologies is used in deepfake?
Answer: C
Explanation:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.
NEW QUESTION # 36
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Answer: D
Explanation:
Basic Concept: Reconnaissance is the information gathering phase of an attack. LLMs can be enhanced to perform more effective reconnaissance by giving them access to current, specific information beyond their training data cutoff. CompTIA SecAI+ covers AI augmentation techniques including RAG under AI-assisted security.
Why A is Correct: RAG enhances an LLM by connecting it to an external knowledge base or real-time data sources that it can query during inference. For reconnaissance purposes, a RAG-enabled LLM can access up- to-date organizational information, technical documentation, and intelligence feeds that go beyond its static training data. This makes the LLM significantly more capable for gathering current, targeted intelligence about specific organizations or infrastructure.
Why B is Wrong: Creating a web scraper is a basic data collection technique. While AI can help write scraper code, the scraper itself is a simple script that does not enhance the LLM ' s intelligence or reasoning capabilities for sophisticated reconnaissance.
Why C is Wrong: Instructing an AI assistant to query as an administrator is a prompt manipulation attempt.
An LLM cannot actually gain elevated permissions through a prompt instruction; this describes social engineering or privilege escalation via prompting, not a performance enhancement technique.
Why D is Wrong: Prompting a chatbot to describe naming patterns is a basic use of an existing LLM ' s knowledge. It does not strengthen or enhance the model ' s capabilities; it merely queries what the model already knows from training data, which may be outdated or generic.
NEW QUESTION # 37
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: C
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 38
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: A,C
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 # 39
Which log type is MOST useful for detecting DNS tunneling?
Answer: B
Explanation:
DNS logs reveal abnormal query lengths and frequencies typical of tunneling.
NEW QUESTION # 40
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