Articlenpj digital surgery2026
Prompt injection attacks on vision-language models for surgical decision support.
Article in npj digital surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Vision-language models (VLMs) hold promise for video-based surgical decision support tasks due to their capabilities to understand 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). We systematically evaluated four state-of-the-art VLMs using textual and temporally-varying visual prompt injection attacks across 100 curated surgical video clips spanning 8 clinically relevant surgical decision support tasks. While Gemini 2.5 Pro achieved the highest baseline accuracy (mean ± SD: 0.80 ± 0.01), all models suffered significant performance declines under attacks. GPT-o4-mini-high was most vulnerable (baseline accuracy: 0.65 ± 0.05; under prolonged visual attack: 0.24 ± 0.03,
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