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The shift of electrical power systems toward intelligent smart-grid architectures delivers better dependability, operational performance, and real-time visibility, yet simultaneously heightens exposure to cyber threats. Thanks to the massive amounts of data now generated in smart grids, machine learning techniques have emerged as a particularly promising avenue for bolstering defensive capabilities. While the strengths of machine-learning solutions for cybersecurity improvement are clear, these same models form part of the attack surface and remain especially susceptible to carefully crafted adversarial perturbations. This work analyzes the resilience of autoencoder-based intrusion detection mechanisms in smart grids against adversarial examples. We begin by presenting an original iterative procedure for synthesizing adversarial instances. Next, we demonstrate that a white-box adversary with full knowledge of the autoencoder detector can reliably produce stealthy, misclassifying samples using the proposed approach. Experimental results show that simply starting from basic adversarial seeds is insufficient to generate effective evasions, underscoring the considerable difficulty of constructing potent adversarial examples targeting autoencoder-based anomaly detectors deployed in smart-grid environments.