RealHarm: A Collection of Real-World Language Model Application Failures
Authors: name: Pierre Le Jeune, name: Jiaen Liu, name: Luca Rossi, name: Matteo Dora

Language model deployments in consumer-facing applications introduce numerous
risks. While existing research on harms and hazards of such applications
follows top-down approaches derived from regulatory frameworks and theoretical
analyses, empirical evidence of real-world failure modes remains underexplored.
In this work, we introduce RealHarm, a dataset of annotated problematic
interactions with AI agents built from a systematic review of publicly reported
incidents. Analyzing harms, causes, and hazards specifically from the
deployer's perspective, we find that reputational damage constitutes the
predominant organizational harm, while misinformation emerges as the most
common hazard category. We empirically evaluate state-of-the-art guardrails and
content moderation systems to probe whether such systems would have prevented
the incidents, revealing a significant gap in the protection of AI
applications.