SpatialTrust: A Benchmark for Environmental Risk Recognition in Secure Authentication
Visual environmental risk recognition plays an important role in secure authentication, where a user's surroundings may reveal sensitive information or introduce potential security risks. However, existing evaluations of multimodal large language models (MLLMs) rarely examine whether models can reliably recognize, localize, and explain such risks in spatially grounded authentication scenarios. We present SpatialTrust, a question-answering benchmark for evaluating environmental risk recognition in secure authentication. SpatialTrust assesses five complementary abilities: sensitive factor detection, direct factor identification, indirect factor identification, direct factor explanation, and...
| Authors | Junbin Lu, Hsiang-Wei Huang, Saesha Wadhwa et al. |
|---|---|
| Primary category | cs.CR |
| Published | 30 August 2026 |
| Last updated at source | 30 August 2026 |
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