Johnny Still Receives Spam SMS: Assessing the Robustness of SMS Spam Detection

Abstract

SMS spam detection systems often achieve high accuracy in controlled environments but struggle against adversarial attacks and increasingly sophisticated spam tactics in real-world deployments. In this paper, we evaluate the robustness of SMS anti-spam systems that end users actually rely on, including commercial messaging applications, third-party anti-spam services, and publicly available open-weight models hosted on Hugging Face. We evaluate these systems under both standard and adversarial conditions, considering perceptible and state-of-the-art imperceptible attacks. We include only perturbations that we verify survive real SMS or RCS delivery, rather than lab-only artifacts. Our experiments reveal significant gaps in existing spam detectors' ability to identify adversarially manipulated messages. We further demonstrate that adversarial training alone is insufficient. Using an explicit held-out evaluation protocol, we find that robustness transfers well within a perturbation family but degrades sharply against structurally distinct, encoding-level attacks. To address these weaknesses, we propose a multi-model ensemble that combines adversarial training with spam classifiers diverse in architecture and tokenization. Our results show that this ensemble, particularly when using a minority-voting strategy, substantially improves robustness against both perceptible and imperceptible adversarial attacks while maintaining competitive classification accuracy. We also characterize the resulting precision-recall trade-off and recommend operating points for false-positive-sensitive and recall-critical deployments. These findings highlight the need for comprehensive robustness evaluations and ensemble-based defenses for building more secure SMS spam detection systems in real-world settings.