Thilo Hagendorff

Abstract We investigate whether large language models (LLMs) exhibit speciesist bias—discrimination based on species membership—and how they value non-human animals. We use three paradigms: SpeciesismBench, a 1009-item benchmark we developed to assess detection and ethical classification of speciesist statements; established psychological measures comparing model and human responses; and text-generation tasks testing for speciesist rationalizations.

Abstract Jailbreaking – bypassing built-in safety mechanisms in AI models – has traditionally required complex technical procedures or specialized human expertise. In this study, we show that the persuasive capabilities of large reasoning models (LRMs) simplify and scale jailbreaking, converting it into an inexpensive activity accessible to non-experts.