@inproceedings{cuevas-etal-2025-anecdoctoring,
    title = "Anecdoctoring: Automated Red-Teaming Across Language and Place",
    author = "Cuevas, Alejandro  and
      Dash, Saloni  and
      Nayak, Bharat Kumar  and
      Vann, Dan  and
      Daepp, Madeleine I. G.",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.emnlp-main.964/",
    doi = "10.18653/v1/2025.emnlp-main.964",
    pages = "19066--19085",
    ISBN = "979-8-89176-332-6",
    abstract = "Disinformation is among the top risks of generative artificial intelligence (AI) misuse. Global adoption of generative AI necessitates red-teaming evaluations (i.e., systematic adversarial probing) that are robust across diverse languages and cultures, but red-teaming datasets are commonly US- and English-centric. To address this gap, we propose ``anecdoctoring'', a novel red-teaming approach that automatically generates adversarial prompts across languages and cultures. We collect misinformation claims from fact-checking websites in three languages (English, Spanish, and Hindi) and two geographies (US and India). We then cluster individual claims into broader narratives and characterize the resulting clusters with knowledge graphs, with which we augment an attacker LLM. Our method produces higher attack success rates and offers interpretability benefits relative to few-shot prompting. Results underscore the need for disinformation mitigations that scale globally and are grounded in real-world adversarial misuse."
}