I'm David from Giskard, and we work on securing Agents.
Today, we are announcing RealHarm: a dataset of real-world problematic interactions with AI agents, drawn from publicly reported incidents.
Most of the research on AI harms is focused on theoretical risks or regulatory guidelines. But the real-world failure modes are often different—and much messier.
With RealHarm, we collected and annotated hundreds of incidents involving deployed language models, using an evidence-based taxonomy for understanding and addressing the AI risks. We did so by analyzing the cases through the lens of deployers—the companies or teams actually shipping LLMs—and we found some surprising results:
Reputational damage was the most common organizational harm.
Misinformation and hallucination were the most frequent hazards
State-of-the-art guardrails have failed to catch many of the incidents.
We hope this dataset can help researchers, developers, and product teams better understand, test, and prevent real-world harms.
The paper and dataset: https://realharm.giskard.ai/
We'd love feedback, questions, or suggestions—especially if you're deploying LLMs and have real harmful scenarios.