Adversarial Robustness of Foundation Models for Intelligent Mechanical Systems: Threat Models, Benchmarks, and Defense Stacks

Abstract

Foundation models increasingly operate across modalities (vision, language, audio, and vision–language) and are deployed in decision-critical pipelines with tool use and retrieval. This expands the adversarial surface: small perturbations to images or audio can flip predictions, carefully crafted text can induce unsafe actions, and cross-modal attacks can exploit representation alignment to produce consistent but wrong outputs. This paper reviews adversarial robustness of foundation models acros

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@article{vishwanath2026adversarial,
  title = {{Adversarial Robustness of Foundation Models for Intelligent Mechanical Systems: Threat Models, Benchmarks, and Defense Stacks}},
  author = {Vishwanath},
  year = {2026},
  month = sep,
  journal = {International Journal For Multidisciplinary Research},
  doi = {10.36948/ijfmr.2026.v08i05.87343},
  url = {https://www.semanticscholar.org/paper/a7e2035a91824e166f5709ad33a6804ac0407df0}
}