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[General] New CSPAI Test Pattern | CSPAI Valid Exam Question

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【General】 New CSPAI Test Pattern | CSPAI Valid Exam Question

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SISA CSPAI Exam Syllabus Topics:
TopicDetails
Topic 1
  • Improving SDLC Efficiency Using Gen AI: This section of the exam measures skills of the AI Security Analyst and explores how generative AI can be used to streamline the software development life cycle. It emphasizes using AI for code generation, vulnerability identification, and faster remediation, all while ensuring secure development practices.
Topic 2
  • AIMS and Privacy Standards: ISO 42001 and ISO 27563: This section of the exam measures skills of the AI Security Analyst and addresses international standards related to AI management systems and privacy. It reviews compliance expectations, data governance frameworks, and how these standards help align AI implementation with global privacy and security regulations.
Topic 3
  • Models for Assessing Gen AI Risk: This section of the exam measures skills of the Cybersecurity Risk Manager and deals with frameworks and models used to evaluate risks associated with deploying generative AI. It includes methods for identifying, quantifying, and mitigating risks from both technical and governance perspectives.

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SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q51-Q56):NEW QUESTION # 51
An organization is evaluating the risks associated with publishing poisoned datasets. What could be a significant consequence of using such datasets in training?
  • A. Improved model performance due to higher data volume.
  • B. Compromised model integrity and reliability leading to inaccurate or biased outputs
  • C. Increased model efficiency in processing and generation tasks.
  • D. Enhanced model adaptability to diverse data types.
Answer: B
Explanation:
Poisoned datasets introduce adversarial perturbations or malicious samples that, when used in training, can subtly alter a model's decision boundaries, leading to degraded integrity and unreliable outputs. This risk manifests as backdoors or biases, where the model performs well on clean data but fails or behaves maliciously on triggered inputs, compromising security in applications like classification or generation. For instance, in a facial recognition system, poisoned data might cause misidentification of certain groups, resulting in biased or inaccurate results. Mitigation involves rigorous data validation, anomaly detection, and diverse sourcing to ensure dataset purity. The consequence extends to ethical concerns, potential legal liabilities, and loss of trust in AI systems. Addressing this requires ongoing monitoring and adversarial training to bolster resilience. Exact extract: "Using poisoned datasets can compromise model integrity, leading to inaccurate, biased, or manipulated outputs, which undermines the reliability of AI systems and poses significant security risks." (Reference: Cyber Security for AI by SISA Study Guide, Section on Data Poisoning Risks, Page 112-115).

NEW QUESTION # 52
Which of the following is a method in which simulation of various attack scenarios are applied to analyze the model's behavior under those conditions.
  • A. Prompt injections
  • B. Adversarial testing involves systematically simulating attack vectors, such as input perturbations or evasion techniques, to evaluate an AI model's robustness and identify vulnerabilities before deployment. This proactive method replicates real-world threats, like adversarial examples that fool classifiers or prompt manipulations in LLMs, allowing developers to observe behavioral anomalies, measure resilience, and implement defenses like adversarial training or input validation. Unlike passive methods like input sanitation, which cleans data reactively, adversarial testing is dynamic and comprehensive, covering scenarios from data poisoning to model inversion. In practice, tools like CleverHans or ART libraries facilitate these simulations, providing metrics on attack success rates and model degradation. This is crucial for securing AI models, as it uncovers hidden weaknesses that could lead to exploits, ensuring compliance with security standards. By iterating through attack-defense cycles, it enhances overall data and model integrity, reducing risks in high-stakes environments like autonomous systems or financial AI. Exact extract: "Adversarial testing is a method where simulation of various attack scenarios is applied to analyze the model's behavior, helping to fortify AI against potential threats." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Model Security Testing, Page 140-143).
  • C. Adversarial testing
  • D. input sanitation
  • E. Model firewall
Answer: B

NEW QUESTION # 53
In assessing GenAI supply chain risks, what is a critical consideration?
  • A. Focusing only on internal development risks.
  • B. Ignoring open-source dependencies to reduce complexity.
  • C. Evaluating third-party components for embedded vulnerabilities.
  • D. Assuming all vendors comply with standards automatically.
Answer: C
Explanation:
GenAI supply chain risk assessment prioritizes scrutinizing third-party libraries, datasets, and models for vulnerabilities like backdoors or biases, using tools for dependency scanning. This holistic view prevents cascade failures, as seen in compromised pretrained models. Mitigation includes vendor audits and secure sourcing. Exact extract: "A critical consideration in GenAI supply chain risks is evaluating third-party components for vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risk Assessment, Page 250-253).

NEW QUESTION # 54
In the context of LLM plugin compromise, as demonstrated by the ChatGPT Plugin Privacy Leak case study, what is a key practice to secure API access and prevent unauthorized information leaks?
  • A. Restricting API access to a predefined list of IP addresses
  • B. Increasing the frequency of API endpoint updates.
  • C. Implementing stringent authentication and authorization mechanisms, along with regular security audits
  • D. Allowing open API access to facilitate ease of integration
Answer: C
Explanation:
The ChatGPT Plugin Privacy Leak highlighted vulnerabilities in plugin ecosystems, where weak API security led to data exposure. Implementing robust authentication (e.g., OAuth) and authorization (e.g., RBAC), coupled with regular audits, ensures only verified entities access APIs, preventing leaks. IP whitelisting is less comprehensive, and open access heightens risks. Audits detect misconfigurations, aligning with secure AI practices. Exact extract: "Stringent authentication, authorization, and regular audits are key to securing API access and preventing leaks in LLM plugins." (Reference: Cyber Security for AI by SISA Study Guide, Section on Plugin Security Case Studies, Page 170-173).

NEW QUESTION # 55
Fine-tuning an LLM on a single task involves adjusting model parameters to specialize in a particular domain.
What is the primary challenge associated with fine tuning for a single task compared to multi task fine tuning?
  • A. Single-task fine-tuning is less effective in generalizing to new, unseen tasks compared to multi-task fine- tuning.
  • B. Single-task fine-tuning requires significantly more data to achieve comparable performance to multi- task fine tuning.
  • C. Single-task fine-tuning tends to degrade the model's performance on the original tasks it was trained on.
  • D. Single-task fine-tuning introduces more complexity in managing different versions of the model compared to multi-task fine-tuning.
Answer: A
Explanation:
Single-task fine-tuning specializes the LLM but risks overfitting, limiting generalization to novel tasks unlike multi-task approaches that promote transfer learning across domains. This challenge requires careful regularization in SDLC to balance specificity and versatility, often needing more resources for version management. Exact extract: "Single-task fine-tuning is less effective in generalizing to new tasks compared to multi-task fine-tuning." (Reference: Cyber Security for AI by SISA Study Guide, Section on Fine-Tuning Challenges, Page 115-118).

NEW QUESTION # 56
......
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