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Domain 2Objective 1

AI Reconnaissance and Attack Surface Mapping COASP Practice Questions (Page 7)

Part of the AI Reconnaissance and Vulnerability Discovery domain, which makes up ~11% of our current practice bank.

45questions here
9free pages
7concepts

Questions 31–35

  1. 31application · medium

    A client wants you to assess the external attack surface of their machine learning platform. They have provided no documentation. Which of the following is the most effective way to begin gathering publicly available information about their models and infrastructure?

    Select an answer first
  2. 32application · medium

    You are performing model fingerprinting on a black-box NLP API. You have access to the API but not to the model's internals. Which technique would be most effective in identifying the underlying model family (e.g., BERT vs. GPT)?

    Select an answer first
  3. 33expert · hard

    During reconnaissance, you find a public S3 bucket that appears to contain model artifacts. You also find a GitHub repository with code that references the same bucket. However, the bucket listing is disabled. What is the most effective way to determine if the bucket contains sensitive model files?

    Select an answer first
  4. 34expert · hard

    You are mapping the attack surface of a microservices-based AI platform. You have discovered the main inference API, but you suspect there are internal services (e.g., model management, data preprocessing) that might be exposed through a service mesh or API gateway. You have limited time and need to prioritize. Which approach is most effective?

    Select an answer first
  5. 35foundation · easy

    Which of the following is a key step in systematically mapping an AI attack surface?

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