Title: Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Primary area: foundation or frontier models, including LLMs
Keywords: time series forecasting, adversarial robustness

Abstract: Time-Series Foundation Models (TSFMs), pretrained on large-scale, cross-domain data, enable zero-shot forecasting in new scenarios and are increasingly deployed in practice. 
Despite their role in decision-making, their security under adversarial conditions is underexplored, creating risks in settings vulnerable to man-in-the-middle manipulation or data poisoning. 
To bridge this gap, we present a systematic study of TSFM adversarial robustness. 
Using a unified evaluation that spans diverse threat models, attack strategies, and robustness metrics, we show that even minimal perturbations can induce significant and controllable forecast behaviors. Across representative TSFMs and multiple datasets, we find consistent vulnerabilities and limited cross-model transfer, indicating model-specific failure modes rather than generic distortions. We then assess two practical defenses and figure out that latent adversarial fine-tuning yields substantial worst-case gains and remains effective even when fine-tuned on out-of-domain data. Overall, this work provides a rigorous assessment of TSFM robustness and actionable guidance for building more resilient, deployment-ready time-series forecasting systems.