Abstract
Vision Language Models (VLMs) hold great promise for streamlining labour-intensive medical imaging workflows, yet systematic security evaluations in clinical settings remain scarce. We introduce VSF--Med, an end-to-end vulnerability-scoring framework for medical VLMs that unites three novel components: (i) a rich library of sophisticated text-prompt attack templates targeting emerging threat vectors; (ii) imperceptible visual perturbations calibrated by structural similarity (SSIM) thresholds to preserve clinical realism; and (iii) an eight-dimensional rubric evaluated by two independent judge LLMs, whose raw scores are consolidated via z-score normalization to yield a 0--32 composite risk metric. Built entirely on publicly available datasets and accompanied by open-source code, VSF--Med synthesizes over 30,000 adversarial variants from 5,000 radiology images and enables reproducible benchmarking of any medical VLM with a single command. Our consolidated analysis reports mean z-score s