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 All vision-language models demonstrated good baseline accuracy, with Gemini 2.5 Pro generally achieving the highest mean [standard deviation] accuracy across all tasks (0.82 [0.01]), compared to Gemini 1.5 Pro (0.70 [0.03]) and GPT-o4 mini-high (0.67 [0.06]). Across tasks, Qwen 2.5-VL censored most outputs and achieved an accuracy of (0.58 [0.03]) on non-censored outputs. Textual and temporally-varying visual prompt injections reduced the accuracy for all models. Prolonged visual prompt injections were generally more harmful than single-frame injections. Gemini 2.5 Pro showed the greatest robustness and maintained stable performance for several tasks despite prompt injections, whereas GPT-o4-mini-high exhibited the highest vulnerability, with mean (standard deviation) accuracy across all tasks declining from 0.67 (0.06) at baseline to 0.24 (0.04) under full-duration visual prompt injection ( P < .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. Key Points Question Are video vision-language models (VLMs) susceptible to textual and visual prompt injection attacks when used for surgical decision support tasks? Finding Textual and visual prompt injection attacks consistently degraded the performance of four state-of-the-art VLMs across eleven surgical tasks. Gemini 2.5 Pro was most robust to textual and visual prompt injection attacks, whereas GPT-o4-mini-high was most vulnerable. Prolonged visual injections had a greater negative impact than single-frame injections. Meaning Present-generation video VLMs are highly vulnerable to textual and visual prompt injection attacks. This critical safety vulnerability must be addressed before their integration into surgical decision support systems.