Prompt injection attacks on vision-language models for surgical decision support

Abstract

Importance: Artificial Intelligence-driven analysis of laparoscopic video holds potential to increase the safety and precision of minimally invasive surgery. Vision-language models are particularly promising for video-based surgical decision support due to their capabilities to comprehend complex temporospatial (video) data. However, the same multimodal interfaces that enable such capabilities also introduce new vulnerabilities to manipulations through embedded deceptive text or images (prompt injection attacks). Objective: To systematically evaluate how susceptible state-of-the-art video-capable vision-language models are to textual and visual prompt injection attacks in the context of clinically relevant surgical decision support tasks. Design Setting and Participants: In this observational study, we systematically evaluated four state-of-the-art vision-language models, Gemini 1.5 Pro, Gemini 2.5 Pro, GPT-o4-mini-high, and Qwen 2.5-VL, across eleven surgical decision support tasks: detection of bleeding events, foreign objects, image distortions, critical view of safety assessment, and surgical skill assessment. Prompt injection scenarios involved misleading textual prompts and visual perturbations, displayed as white text overlay, applied at varying durations. Main Outcomes and Measures: The primary measure was model accuracy, contrasted between baseline performance and each prompt injection condition. Results: < .001). Conclusion and Relevance: These findings indicate the critical need for robust temporal reasoning capabilities and specialized guardrails before vision-language models can be safely deployed for real-time surgical decision support.

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