Decision Boundary Drift: A Security, Privacy, and Trust Risk of Continual Learning for Agentic AI in Edge Networks

Abstract

Continual learning (CL) is a key paradigm that enables intelligent agents to operate autonomously in edge networks over the long term. However, continuous model updates can lead to catastrophic forgetting and representation instability in edge deployment scenarios, which may further induce Decision Boundary Drift (DBD). We propose a DBD-based adversarial attack framework that exploits class-level drift modeling and leverages the deformation of decision boundaries caused by incremental updates. We introduce multiple statistical metrics to quantify boundary drift, based on which class-level adversarial perturbations are constructed and further optimized in the input space to generate effective adversarial examples. Extensive experiments on multiple datasets and continual learning models demonstrate that the proposed method can significantly degrade model robustness, revealing non-negligible security risks in continuously evolving learning systems. Inspired by the security and trustworthiness requirements of edge intelligent agents, we systematically study and quantify DBD and its associated security risks in continual learning. Our findings reveal a practical yet underestimated attack surface and provide a foundation for future research on secure and robust continual learning systems.