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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q31-Q36):
NEW QUESTION # 31
What is a potential risk associated with hallucinations in LLMs, and how should it be addressed to ensure Responsible AI?
Answer: C
Explanation:
Hallucinations in LLMs risk generating inaccurate or misleading outputs, undermining trust and safety.
Incorporating external knowledge bases and retrieval systems, like RAG, grounds responses in verified data, reducing fabrications and aligning with Responsible AI principles. Regularization helps but is secondary to factual grounding. Exact extract: "Hallucinations produce misleading information, addressed by incorporating external knowledge bases and retrieval systems for Responsible AI." (Reference: Cyber Security for AI by SISA Study Guide, Section on LLM Hallucination Mitigation, Page 125-128).
NEW QUESTION # 32
When integrating LLMs using a Prompting Technique, what is a significant challenge in achieving consistent performance across diverse applications?
Answer: D
Explanation:
Prompting techniques in LLM integration, such as zero-shot or few-shot prompting, face challenges in consistency due to the need for meticulously optimized templates that generalize across tasks. Variations in prompt phrasing can lead to unpredictable outputs, requiring iterative engineering to balance specificity and flexibility, especially in diverse domains like legal or medical apps. This optimization involves A/B testing, semantic alignment, and incorporating chain-of-thought to enhance reasoning, but it demands expertise and time in SDLC phases. Unlike latency issues, which are hardware-related, prompt optimization directly affects performance reliability. Security overlaps, as poor prompts might expose vulnerabilities, but the core challenge is generalization. Efficient SDLC uses automated prompt tuning tools to streamline this, reducing development overhead while maintaining efficacy. Exact extract: "A significant challenge is optimizing prompt templates to ensure generalization across different contexts, crucial for consistent LLM performance in varied applications." (Reference: Cyber Security for AI by SISA Study Guide, Section on Prompting in SDLC, Page 100-103).
NEW QUESTION # 33
What is a potential risk of LLM plugin compromise?
Answer: D
Explanation:
LLM plugin compromises occur when extensions or integrations, like API-connected tools in systems such as ChatGPT plugins, are exploited, leading to unauthorized data access or injection attacks. Attackers might hijack plugins to leak user queries, training data, or system prompts, breaching privacy and enabling further escalations like lateral movement in networks. This risk is amplified in open ecosystems where plugins handle sensitive operations, necessitating vetting, sandboxing, and encryption. Unlike benefits like accuracy gains, compromises erode trust and invite regulatory penalties. Mitigation strategies include regular vulnerability scans, least-privilege access, and monitoring for anomalous plugin behavior. In AI security, this highlights the need for robust plugin architectures to prevent cascade failures. Exact extract: "A potential risk of LLM plugin compromise is unauthorized access to sensitive information, which can lead to data breaches and privacy violations." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security in LLMs, Page 155-158).
NEW QUESTION # 34
In a scenario where Open-Source LLMs are being used to create a virtual assistant, what would be the most effective way to ensure the assistant is continuously improving its interactions without constant retraining?
Answer: C
Explanation:
For continuous improvement in open-source LLM-based virtual assistants, RLHF integrates human evaluations to align model outputs with preferences, iteratively refining behavior without full retraining. This method uses reward models trained on feedback to guide policy optimization, enhancing interaction quality over time. It addresses limitations like initial biases or suboptimal responses by leveraging real-world user inputs, making the system adaptive and efficient. Unlike full retraining, RLHF is parameter-efficient and scalable, ideal for production environments. Security benefits include monitoring feedback for adversarial attempts. Exact extract: "Implementing RLHF allows continuous refinement of the assistant's interactions based on user feedback, avoiding the need for constant full retraining while improving performance." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Improvement Techniques in SDLC, Page 85-88).
NEW QUESTION # 35
How does GenAI contribute to incident response in cybersecurity?
Answer: A
Explanation:
GenAI enhances incident response by dynamically generating customized playbooks based on threat intelligence and orchestrating automated actions like isolation or patching. It processes vast logs in real-time, correlating events to prioritize alerts and suggest optimal responses, reducing mean time to respond (MTTR).
For complex incidents, it simulates outcomes of different strategies, aiding decision-making. This automation frees analysts for strategic tasks, improving efficiency and effectiveness in containing breaches. Exact extract:
"GenAI contributes to incident response by automating playbook generation and orchestration, enhancing cybersecurity operations." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI in Incident Response, Page 215-218).
NEW QUESTION # 36
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