@inproceedings{chen-etal-2026-ataat,
    title = "{ATAAT}: Adaptive Threat-Aware Adversarial Tuning Framework against Backdoor Attacks on Vision-Language-Action Models",
    author = "Chen, Kewei  and
      Long, Yayu  and
      Li, Shuai  and
      Shang, Mingsheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1077/",
    pages = "21407--21422",
    ISBN = "979-8-89176-395-1",
    abstract = "Addressing the escalating security vulnerabilities in Vision-Language-Action (VLA) models, this study investigates backdoor attacks targeting the visual pathway. We identify a core obstacle causing the failure of traditional attack paradigms: ``Gradient Interference.'' This phenomenon represents an optimization failure triggered by conflicting strategies during end-to-end training. To resolve this, we propose an Adaptive Threat-Aware Adversarial Tuning (ATAAT) framework. Through its core ``Threat-Method Adaptive Mapping'' mechanism, ATAAT intelligently selects the optimal gradient decoupling strategy based on the adversary{'}s capabilities. Extensive experiments demonstrate that ATAAT exhibits significant advantages, achieving a highly robust Targeted Attack Success Rate (TASR {\ensuremath{>}} 80{\%}) while maintaining extreme stealthiness with merely a 5{\%} poisoning rate. It efficiently handles complex semantic-level triggers and achieves implicit decoupled attacks in data poisoning scenarios for the first time. This work reveals a critical security vulnerability in VLAs and provides theoretical and methodological support for future defense architectures."
}