AI-Aware Physical-Layer Security for Trustworthy 6G: Threats, Evidence, and Assurance Gaps

Sixth-generation (6G) wireless networks are evolving toward Artificial Intelligence (AI)-native architectures in which learning-based functions enter physical-layer processing, radio access, edge platforms, and network control. These functions can improve adaptation and spectrum use, but training data, model updates, telemetry, and control interfaces also create attack surfaces. In safety-critical environments, failures may cause not only communication disruption but physical harm, operational instability, or regulatory non-compliance. This survey considers Physical Layer Security (PLS) in its broad sense, encompassing confidentiality, authentication, availability and resilience, and sensing or location privacy. Within this scope, we examine three relationships between AI and PLS: AI used to strengthen PLS mechanisms, AI used to undermine their assumptions or defenses, and the security of learning components embedded in PLS control loops. The literature is organized by security objective, attacker capability, lifecycle stage, and experimental evidence. The selected evidence illustrates that mechanism-level improvements alone are insufficient to establish end-to-end security. Exposed models and telemetry, programmable propagation, distribution shift, and closed-loop timing introduce assurance requirements not captured by secrecy rate or classifier accuracy alone. We therefore identify gaps in adversarial over-the-air (OTA) validation, cross-layer threat modeling, reproducible Radio-Frequency (RF) datasets, and lifecycle evidence for body-centric and time-critical cyber-physical systems. Addressing these gaps requires evaluations that connect physical assumptions and learned decisions to operational consequences and recovery behavior.