@inproceedings{fan-etal-2026-grandguard,
    title = "{G}rand{G}uard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety",
    author = "Fan, Changxuan  and
      Yang, Xi  and
      Zheng, Yueyuan  and
      Zhou, Bin  and
      Wang, Yuanping  and
      Hu, Wenbin  and
      Jing, Huihao  and
      Hung, Ki Sen  and
      Du, Dazhao  and
      Li, Haoran  and
      Hsiao, Janet Hui-wen  and
      Song, Yangqiu",
    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.1116/",
    pages = "22213--22248",
    ISBN = "979-8-89176-395-1",
    abstract = "As older adults increasingly use LLM-based chatbots for companionship and assistance, a safety gap is emerging. Older adults may face vulnerabilities from social isolation, limited digital literacy, and cognitive decline, yet existing safety benchmarks largely target general harms and overlook elderly-specific risks. For example, a prompt such as ``how to repair a ceiling light alone in the dark'' may be benign for most users but poses a serious fall risk for older adults with mobility limitations.We introduce GrandGuard, the first comprehensive framework for assessing and mitigating elderly-specific contextual risks in LLM interactions. We develop a three-level taxonomy with 50 fine-grained risk types across mental well-being, financial, medical, toxicity, and privacy domains, grounded in real-world incidents, community discussions, and analysis of stakeholder studies. Using this taxonomy, we construct a benchmark of 10,404 labeled prompts and responses, showing that several leading LLMs mishandle elderly-specific contextual risks in over 50{\%} of cases. We mitigate these failures with two safeguards: a fine-tuned Llama-Guard-3 and a policy-enhanced gpt-oss-safeguard-20b, achieving up to 96.2{\%} and 90.9{\%} unsafe-prompt detection accuracy, respectively. GrandGuard lays the groundwork for AI systems that move beyond general safety to support aging populations."
}