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AI Attack Surfaces, Threat Landscapes, and Adversary Techniques (MITRE ATLAS-aligned) COASP Practice Questions (Page 4)
Part of the Offensive AI Foundations and Hacking Methodology domain, which makes up ~18% of our current practice bank.
47questions here
10free pages
6concepts
Questions 16–20
- 16
A security analyst is reviewing a series of attacks against an AI-powered chatbot. The chatbot uses a large language model (LLM) that is fine-tuned on customer support data. The analyst identifies the following incidents: (1) an attacker uses carefully crafted prompts to make the chatbot reveal its system instructions, (2) an attacker submits a prompt that causes the chatbot to output a malicious script that is executed by a downstream system, (3) an attacker uses the chatbot's responses to infer whether a specific customer's data was in the fine-tuning dataset. Which MITRE ATLAS techniques are applicable to these incidents? (Select all that apply.)
Select an answer first - 17
A security analyst is evaluating the threat landscape for a self-driving car's perception model. The analyst is concerned that an attacker could place stickers on a stop sign to make the model misread it as a speed limit sign. Which AI-specific threat and attack surface does this scenario represent?
Select an answer first - 18
A security analyst is categorizing an attack where an adversary used a generative model to create realistic fake audio of a CEO's voice and then used it to authorize a fraudulent wire transfer. The analyst is mapping this to MITRE ATLAS. Which technique is most directly applicable?
Select an answer first - 19
Which MITRE ATLAS tactic involves an adversary gaining initial access to an AI system?
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Which threat involves an attacker inserting malicious data into the training set to cause the model to misbehave?
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