# NERVE Attacks: Breaking AI-Powered Brain-Computer Interfaces

*2026-09-01 — paper*

Authors: Zahra Tarkhani, Georgios Akkogiounoglou, Lorena Qendro, Isabel Tscherniak, Anil Madhavapeddy


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.


DOI: 10.48550/arXiv.2609.08971
Classification: preprint
Venue: arXiv
URL: http://arxiv.org/abs/2609.08971

## Related

- [Hitting a NERVE with attacks on AI-powered brain-computer interfaces](https://anil.recoil.org/notes/nerve-attacks) (note, 2026-09-11)

---
Canonical: https://anil.recoil.org/papers/2026-nerve-attacks
Type: paper
License: CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>
Tags: preprint