Disinformation is among the top risks of generative artificial intelligence (AI) misuse.Global adoption of generative AI necessitates redteaming 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 realworld adversarial misuse.

This paper explores the nature and spread of viral WhatsApp content among everyday users in three diverse countries: India, Indonesia, and Colombia. By analyzing hundreds of viral messages collected with participants’ consent from private WhatsApp groups, we provide one of the first cross-cultural categorizations of viral content on WhatsApp. Despite the differences in cultural and geographic settings, our findings reveal striking similarities in the types of groups users engage with and the viral content they receive, particularly in the prevalence of misinformation. Our comparative analysis shows that viral content often includes political and religious narratives, with misinformation frequently recirculated despite prior debunking by fact-checking organizations. These parallels suggest that closed messaging platforms like WhatsApp facilitate similar patterns of information dissemination across different cultural contexts. This work contributes to the broader understanding of global digital communication ecosystems and provides a foundation for future research on information flow and moderation strategies in private messaging platforms.