Rowhammer attack can backdoor AI models with one devastating bit flip

The OneFlip method reveals a critical vulnerability where flipping a single bit in memory can silently poison AI models. This allows attackers to manipulate neural networks, such as those in autonomous vehicles or facial recognition systems, with near-perfect success while maintaining normal performance. This research highlights severe security risks in physical hardware integrity, demonstrating that even minimal hardware interference can compromise sophisticated AI applications. The resilience of this attack to current defenses underscores the urgent need for robust physical security measures in hardware manufacturing and deployment. Understanding these threats is vital for open data initiatives, as they emphasize that data integrity extends beyond software algorithms to include the underlying physical infrastructure. Ensuring reliable AI systems requires addressing these hardware-level vulnerabilities to prevent malicious exploitation of trusted models.

Source: csoonline.com
Published on 2025-08-26

Rowhammer attack can backdoor AI models with one devastating bit flip

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