In the context of AI security, what is a typical consequence of hallucination attacks when deployed publicly?

• A. The system may provide misleading or harmful content to users.
• B. The system becomes simpler to secure and audit.
• C. The model learns from user feedback to correct itself.
• D. There is no measurable impact on user trust.

Answer: (A)

Hallucination attacks produce content that isn’t grounded in facts, so when the system is exposed to the public, it can give users misleading or harmful information. That real-world risk—misinformation finding its way to users and eroding trust in the system—is the typical consequence of deploying such behavior. The public-facing nature amplifies the problem because many users rely on accurate information and may act on incorrect content, potentially causing harm and spreading falsehoods. The other options don’t fit: making security and auditing easier isn’t accurate since unpredictable outputs complicate monitoring; the model doesn’t automatically learn from user feedback to fix itself in this context, as learning is a controlled process and hallucinations can persist or be exploited; and there is a measurable impact on user trust, so claiming no impact isn’t correct.