GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety
GrandGuard, from arXiv cs.AI (2605.20203), is the first comprehensive framework for assessing elderly-specific contextual risks in LLM interactions. It develops 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 and stakeholder studies. Using this taxonomy, the authors constructed a benchmark of 10,404 labeled prompts and responses. Evaluation shows that several leading LLMs mishandle elderly-specific contextual risks in over 50% of cases. To address this, they propose two safeguards: a fine-tuned Llama-Guard-3 and another method. The work highlights that existing safety benchmarks overlook risks like a prompt to repair a ceiling light alone in the dark, which poses a fall risk for older adults with mobility limitations.
Developers must address elderly-specific risks in LLM chatbots to prevent harm from overlooked contextual dangers.