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EC-CouncilCertified Offensive AI Security Professional

Domain 1Objective 4

AI Attack Taxonomies and Classification Models COASP Practice Questions (Page 2)

Part of the Offensive AI Foundations and Hacking Methodology domain, which makes up ~18% of our current practice bank.

39questions here
8free pages
5concepts

Questions 6–10

  1. 6expert · hard

    A security team is classifying vulnerabilities for a recommendation model. The team identifies three issues: (1) the model can be manipulated to recommend harmful products, (2) the model's training data can be inferred through queries, and (3) the model's API exposes internal error messages. Which categorization correctly distinguishes the AI-specific vulnerabilities from the general security issue?

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  2. 7expert · hard

    An offensive AI specialist is planning an attack on a self-driving car's object detection system. The goal is to cause the car to fail to detect a stop sign. The specialist has access to the car's hardware and can install a malicious update to the perception module. However, the update must be signed by the manufacturer to be accepted. Which combination of attack lifecycle stages and attack types best describes the most viable path to achieve the goal?

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  3. 8expert · hard

    A red team is planning an attack on a voice-assistant model. The team's objective is to make the model execute a specific command without the user's knowledge. The team has access to the model's API but cannot modify the model or its training data. Which attack strategy is most likely to achieve the objective, and which lifecycle stage does it primarily represent?

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  4. 9expert · hard

    A red team is conducting an assessment of an AI-powered malware detection system. The team has successfully identified a vulnerability in the system's feature extraction pipeline that allows them to craft malware samples that are misclassified as benign. The team's ultimate goal is to maintain persistent access to the system's network. Which attack lifecycle stage is the team currently in, and what is the most appropriate next step according to the kill chain?

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  5. 10expert · hard

    An offensive AI team is testing a recommendation model that is hosted behind an API with rate limiting. The team wants to determine if the model is vulnerable to extraction, but the rate limit severely restricts the number of queries they can make. Which approach best balances the need to test extraction with the rate-limit constraint?

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