Recent progress in minimally invasive brain—computer interfaces (BCIs), nanoscale neural interfacing, and multimodal neural decoding has enabled increasingly precise access to and interpretation of human brain activity. This paper analyzes the dual-use risks associated with these technologies when integrated with advanced artificial intelligence and adaptive social engineering methodologies. We formalize a conceptual architecture for "brain-invading systems," which leverage closed-loop neural interaction, personalized modeling, and behavioral manipulation strategies to influence cognitive and affective states. We examine enabling components, including remote-capable neural interfaces and high-fidelity decoding pipelines, and discuss their potential convergence into scalable manipulation frameworks. Key challenges in detecting such systems are evaluated, including signal attribution, adversarial interference, and limitations in current neurodiagnostic methods. We further discuss opportunities in detecting such neuro-AI system for malicious purposes based on EEG signals.