@inproceedings{tasawong-etal-2025-shortcut,
    title = "Shortcut Learning in Safety: The Impact of Keyword Bias in Safeguards",
    author = "Tasawong, Panuthep  and
      Laosaengpha, Napat  and
      Ponwitayarat, Wuttikorn  and
      Lim, Sitiporn  and
      Manakul, Potsawee  and
      Cahyawijaya, Samuel  and
      Udomcharoenchaikit, Can  and
      Limkonchotiwat, Peerat  and
      Chuangsuwanich, Ekapol  and
      Nutanong, Sarana",
    editor = "Novikova, Jekaterina",
    booktitle = "Proceedings of the The First Workshop on LLM Security (LLMSEC)",
    month = aug,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.llmsec-1.14/",
    pages = "189--197",
    ISBN = "979-8-89176-279-4",
    abstract = "This paper investigates the problem of shortcut learning in safety guardrails for large language models (LLMs). It reveals that current safeguard models often rely excessively on superficial cues, such as specific keywords that are spuriously correlated with training labels, rather than genuinely understanding the input{'}s semantics or intent. As a result, their performance degrades significantly when there is a shift in keyword distribution. The paper also examines the impact of reducing shortcut reliance, showing that merely minimizing shortcut influence is insufficient. To build robust safeguard models, it is equally crucial to promote the use of intended features."
}