arXiv:2606.09315v1 Announce Type: cross Abstract: BCI-to-agent pipelines turn decoded neural activity into an authorization channel for tool-use agents, exposing a new attack surface we call \emph{brain-prompt injection}: signal-side perturbations, context-only injections, and adaptive dual-decoder attacks can all change the routed action while EEG-side or text-side monitors remain blind. Route safety in this stack depends on what the audit log can observe, not on decoder accuracy or agreement alone. We define a Route-Safety Audit Contract: a minimal log schema, denominator hierarchy, and endp
The increased sophistication and deployment of BCI-LLM agents necessitate a proactive approach to security audits, identifying vulnerabilities before widespread adoption.
This research highlights a critical new attack surface in BCI-LLM systems, impacting the security and trustworthiness of agents controlled by neural activity.
The definition of security for BCI-LLM agents now explicitly includes 'brain-prompt injection' and necessitates new 'route-safety audit contracts' beyond traditional decoder accuracy metrics.
- · Cybersecurity firms specializing in AI/BCI
- · Developers of robust BCI security protocols
- · Regulatory bodies focused on AI safety
- · Users prioritizing secure neural interfaces
- · Developers of insecure BCI-LLM systems
- · Users of un-audited neural interfaces
- · Organizations relying on BCI for high-stakes authorization
- · Adversaries exploiting new attack vectors
Exploitation of brain-prompt injection could lead to unauthorized actions by BCI-LLM agents.
Public trust in brain-computer interfaces and AI agents could erode, impacting adoption rates and investment.
New regulations and certification standards for BCI-LLM systems become mandatory, shaping the entire industry's development trajectory.
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Read at arXiv cs.AI