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SISA Certified Security Professional in Artificial Intelligence Sample Questions:

1. How does the STRIDE model adapt to assessing threats in GenAI?

A) By focusing only on hardware threats in AI systems.
B) By using it unchanged from traditional software.
C) By applying Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege to AI components.
D) By excluding AI-specific threats like model inversion.

2. What role does GenAI play in automating vulnerability scanning and remediation processes?

A) By increasing the frequency of manual scans to ensure thoroughness.
B) By generating code patches and suggesting fixes based on vulnerability descriptions.
C) By ignoring low-priority vulnerabilities to focus on high-impact ones.
D) By compiling lists of vulnerabilities without any analysis.

3. 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) input sanitation
B) Model firewall
C) Adversarial testing
D) Prompt injections
E) 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).

4. 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) Allowing open API access to facilitate ease of integration
B) Increasing the frequency of API endpoint updates.
C) Restricting API access to a predefined list of IP addresses
D) Implementing stringent authentication and authorization mechanisms, along with regular security audits

5. For effective AI risk management, which measure is crucial when dealing with penetration testing and supply chain security?

A) Conduct comprehensive penetration testing and continuously evaluate both internal systems and third- party components in the supply chain.
B) Implement penetration testing only for high-risk components and ignore less critical ones
C) Prioritize external audits over internal penetration testing to assess supply chain security.
D) Perform occasional penetration testing and only address vulnerabilities in the internal network.

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