A Comprehensive Analysis of Security Flaws and Attack Vectors in Artificial Intelligence–Powered Brain–Computer Interfaces

Abstract

The security and privacy concerns of the Artificial Intelligence (AI) and Brain-Computer Interface (BCI) technology working together in the field of modern medicine are complicated. It has not only caused a revolution in neurocommunication, cognitive rehabilitation as well as assistive neuroprosthetics, it has also created complex security and privacy challenges. The analogy framework that has been used in analysing the security irregularities of the BCI systems implemented based on AI involves 6 layers that include the signal acquisition, device firmware, network communication, AI models, side channels, and human interaction. Some of the potential targets of adversarial perturbation include an adversarial perturbation, data poisoning, model inversion, signal injection and manipulation of firmware-threats, with direct implication on the system integrity and patient safety. The framework enhances data confidentiality, operational reliability, and clinical trust through end-to-end threat modeling and simulation. To counter medical-cyber threats, it employs a cross-layer defense integrating federated learning, differential privacy, secure firmware attestation, and adaptive noise filtering. This unified taxonomy supports secure-by-design AI-BCI systems, ensuring safety, dependability, and ethical integrity in medical applications.