Fragile AI: Can Mamba Handle the Pressure in Medical Imaging?
![Vision Mamba proposes a selective state space model-a departure from traditional transformers-that utilizes a hardware-aware, temporally selective scan to process visual inputs, achieving a linear scaling in sequence length and promising improved efficiency in vision tasks through a principled reduction of computational redundancy inherent in attention mechanisms [latex]O(N) [/latex] versus [latex]O(N^2)[/latex].](https://arxiv.org/html/2602.16723v1/Fig/visionMamba.jpg)
A new study reveals that Mamba-based medical image analysis, despite its impressive performance, is surprisingly susceptible to both malicious attacks and real-world hardware failures.
![Vision Mamba proposes a selective state space model-a departure from traditional transformers-that utilizes a hardware-aware, temporally selective scan to process visual inputs, achieving a linear scaling in sequence length and promising improved efficiency in vision tasks through a principled reduction of computational redundancy inherent in attention mechanisms [latex]O(N) [/latex] versus [latex]O(N^2)[/latex].](https://arxiv.org/html/2602.16723v1/Fig/visionMamba.jpg)
A new study reveals that Mamba-based medical image analysis, despite its impressive performance, is surprisingly susceptible to both malicious attacks and real-world hardware failures.

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