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NEW QUESTION # 320
Which risk is BEST mitigated by automatically clearing in-memory state at the end of each agent application session?
Answer: A
Explanation:
Automatically clearing in-memory state after each session reduces the risk that sensitive or residual session data remains accessible to unauthorized users or processes. This helps prevent exposure or exfiltration of confidential information retained in memory after the session ends.
NEW QUESTION # 321
A model producing contradictory outputs based on highly similar inputs MOST likely indicates the presence of:
Answer: B
Explanation:
The AAISM study framework describes evasion attacks as attempts to manipulate or probe a trained model during inference by using crafted inputs that appear normal but cause the system to generate inconsistent or erroneous outputs. Contradictory results from nearly identical queries are a typical symptom of evasion, as the attacker is probing decision boundaries to find weaknesses. Poisoning attacks occur during training, not inference, while membership inference relates to exposing whether data was part of the training set, and model exfiltration involves extracting proprietary parameters or architecture. The clearest indication of contradictory outputs from similar queries therefore aligns directly with the definition of evasion attacks in AAISM materials.
References:
AAISM Study Guide - AI Technologies and Controls (Adversarial Machine Learning and Attack Types) ISACA AI Security Management - Inference-time Attack Scenarios
NEW QUESTION # 322
Which of the following approaches BEST enables the separation of sensitive and shareable data to prevent an AI chatbot from inadvertently disclosing confidential information?
Answer: B
Explanation:
AAISM materials describe data segregation and segmented access as core technical controls to prevent unintended information disclosure by AI systems. Siloing refers to logically or physically separating data into distinct repositories or contexts, ensuring that sensitive datasets are not available to components or applications that only require non-sensitive information. This is directly aligned with preventing a chatbot from accessing or mixing confidential data with general conversational content.
NEW QUESTION # 323
An attacker crafts inputs to a large language model (LLM) to exploit output integrity controls. Which of the following types of attacks is this an example of?
Answer: A
Explanation:
According to the AAISM framework, prompt injection is the act of deliberately crafting malicious or manipulative inputs to override, bypass, or exploit the model's intended controls. In this case, the attacker is targeting the integrity of the model's outputs by exploiting weaknesses in how it interprets and processes prompts. Jailbreaking is a subtype of prompt injection specifically designed to override safety restrictions, while evasion attacks target classification boundaries in other ML contexts, and remote code execution refers to system-level exploitation outside of the AI inference context. The most accurate classification of this attack is prompt injection.
References:
AAISM Exam Content Outline - AI Technologies and Controls (Prompt Security and Input Manipulation) AI Security Management Study Guide - Threats to Output Integrity
NEW QUESTION # 324
Which of the following presents the GREATEST challenge to gaining an understanding of how a deep learning system operates?
Answer: D
Explanation:
Deep learning systems rely on highly complex non-linear relationships among large numbers of features, hidden layers, and parameters. These interactions make it difficult to understand, interpret, and explain how the model reaches specific decisions or outputs.
NEW QUESTION # 325
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