This study builds a simple map of all the main ways large language models can cause harm.

Using about 200 research papers and incident reports, it groups harms into 5 stages of a model's life.

Before the model is released, it highlights issues like scraped personal data, training on text without people's consent, heavy energy use, and low paid annotation work.

In what the model writes out, it focuses on biased or stereotyped language, toxic or false content, and hallucinations that look trustworthy.

For intentional misuse, it shows how people can generate scams, targeted abuse, propaganda, and even prompt based attacks on connected systems.

At the broader social level, it describes job disruption, political manipulation, concentration of computing power, and unequal access to advanced models across regions and languages.

When models are built into tools for healthcare, finance, education, or creative work, it explains how errors and bias can quietly shape real decisions.

Across all stages, the authors line up existing technical and policy defenses and argue that only many layered safeguards together can keep risks manageable.

• arxiv. org/abs/2512.05929

Paper Title: "LLM Harms: A Taxonomy and Discussion"