src_fb50be84b835134c75
- Source-provided label
- Not reported
- Evidence class
- mentioned in report
- Confidence
- Not reported
- Observed
- Oct 07, 2026 04:00 UTC
- Retrieved
- Oct 08, 2026 03:43 UTC
BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors
arXiv:2610.08739v1 Announce Type: new Abstract: 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 fram
Reference details
- Source category
- Research & news
- Report title
BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors- First reported
- Oct 07, 2026 04:00 UTC
- Last reported
- Oct 07, 2026 04:00 UTC
- Catalog updated
- Oct 08, 2026 03:43 UTC
- Record ID
e2da281000ea2e3bad2199d930e6372f7d73e7d7400228a131f43e65b42cbee7
Published indicators
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Source observations
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1 sources: 0 stale, 0 needing attention, 0 paused, 1 unverified.
Review source update dates and status
- src_fb50be84b835134c75Collection status unverifiedLast successful batch: Unverified
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