NERVE Attacks: Breaking AI-Powered Brain-Computer Interfaces
Researchers characterize NERVE, five attack dimensions against AI-powered brain-computer interfaces, and release the EEGle framework uncovering 17 attack instances.
NERVE is a systematic attack class spanning the BCI stack across five orthogonal dimensions: Neuro-mimetic Forgery, Evasion via Desynchronization, Replay-based Hijacking, Vein Tapping, and Embedded Backdoors. The accompanying EEGle framework enables AI-assisted, extensible BCI security analysis and helped uncover 17 novel neuro-specific attack instances, revealing a stealth-effectiveness spectrum unique to BCI backdoor design. The authors show generative AI lowers the barrier to entry for non-expert attackers and release EEGle to the community.
- Five orthogonal attack dimensions span the full BCI stack
- EEGLE framework supports systematic, extensible BCI security testing
- 17 novel neuro-specific attack instances uncovered
- Stealth-effectiveness spectrum unique to BCI backdoor design identified
- Generative AI lowers attacker skill barrier for BCI attacks
Full article151 words · extracted from arxiv.org · click to collapse
The rapid integration of AI into human-centred systems such as Brain-Computer Interfaces (BCIs) has created a poorly understood attack surface linking neural signals to physical systems. Exploits in this domain threaten cognitive autonomy, mental privacy, and physical safety, from neural data exfiltration to malicious control of BCI-tethered devices. We introduce the NERVE Attacks class, a systematic characterisation of five orthogonal attack dimensions that together span the complete BCI stack: Neuro-mimetic Forgery (N), Evasion via Desynchronization (E), Replay-based Hijacking (R), Vein Tapping (V), and Embedded Backdoors (E). To evaluate this class, we present EEGle, an AI-assisted extensible framework for systematic BCI security analysis. Our evaluation uncovers 17 novel neuro-specific attack instances and reveals a stealth-effectiveness spectrum unique to BCI backdoor design. We also show that generative AI lowers the barrier to entry for non-expert attackers and provide EEGle to the community for building and verifying the security of these deeply personal devices.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.08971