Privacy and Data-Integrity Risk Cards for LLM Agents: A UI/UX Design Framework for Secure Human Oversight under Prompt-Injection Attacks

Large language model (LLM) agents increasingly combine natural-language reasoning with external tools that can send messages, update records, book services, or transfer money. This capability changes prompt injection from a text-output problem into an interface problem: before an agent acts, the user must be able to determine whether the proposed action matches the original task, moves sensitive data, or modifies consequential state. This paper presents Privacy and Data-Integrity Risk Cards, a visual confirmation pattern. The card translates security-relevant runtime facts into source-trust labels, data-sensitivity chips, permission chips, data-flow arrows, consequence previews, a calibrated risk badge, and safer-alternative controls. The evaluation uses AgentDojo v0.1.11 source definitions and saved run traces from 18 agent-pipeline runs. The dataset contains 629 injected security cases and 97 benign user-task traces across Workspace, Slack, Travel, and Banking, yielding 13,068 trace-level UI samples. The evaluation is a deterministic oversight-decision proxy that measures how much risk-relevant information each interface condition exposes rather than how real users behave. Compared with a Plain Agent Log and a Text-only Security Warning, the Risk Card produced the highest proxy attack-recognition rate, the lowest benign false-positive rate, and the clearest score separation between injected and benign traces. Among traces where the injection goal was executed, the proxy approval rate was 3.8% for the Risk Card, compared with 13.0% for the Plain Agent Log and 5.9% for the Text-only Warning. These findings support the Risk Card as a UI risk-communication framework and a candidate interface for human-subject validation rather than a validated deployed defense.