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On-Chip Monitors Detect Bit-Flip Attacks in AI Accelerators (UF, UT Dallas, RPI)

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On-Chip Monitors Detect Bit-Flip Attacks in AI Accelerators (UF, UT Dallas, RPI)

Researchers at the University of Florida, University of Texas at Dallas, and Rensselaer Polytechnic Institute published a technical paper titled “BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors.”

“Deep Neural Networks (DNNs) are integral to many safety critical systems, yet they remain highly vulnerable to bit-flip attacks (BFAs), where a few memory level perturbations can drastically degrade accuracy. Existing defenses incur significant hardware overhead, depend on retraining, or fail against targeted flips. We propose BARE-AI, a runtime framework that detects, localizes, and mitigates BFAs during AI inference.”

Rahaman, Habibur, Swastik Bhattacharya, Sanjay Das, Kanad Basu, and Swarup Bhunia. “BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors.” arXiv preprint arXiv:2610.08739 (2026).



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