In recent years, the growing adoption of Edge AI systems has been accompanied by a surge in security threats. Edge AI deployments face an rapidly evolving spectrum of security threats, from side-channel information leakage to ad- versarial model manipulation and physical device tampering. Conventional security mechanisms such as malware scanners and resource-intensive firewall solutions are unsuitable due to edge AIs typically limited computational resources, strict energy budgets and thermal constraints especially in battery-powered and field-deployed environments. This paper proposes a stacked, hardware-based security mechanism that enables low-overhead, continuous detection of anomalous execution patterns. Anomaly detection utilizing hard- ware performance counters and physical sensors outperforms software-based alternatives by providing key capabilities such as tamper-resistant root-of-trust isolation, negligible runtime overhead, and resilience against compromised operating systems. Experimental analysis shows that adversarial or malicious ap- plications produce observable deviations in sensor and thermal readings on Edge AI systems and hardware-based anomaly detection provides effective and low-overhead anomaly detection.

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