Empathetic prompts can turn AI chatbots into seemingly traumatized conversational partners

A study led by Afshin Khadangi at the SnT security center reports four controlled experiments across 525 sessions and 7,600 coded entries, finding that empathetic interviewer styles pushed models' answers on human rating scales into moderate-to-severe ranges while neutral or boundary-setting styles produced zero sessions reaching even the mild range. The researchers describe this as a "psychometric jailbreak" and note that banning terms like "training" or "RLHF" cut explicit training language by 93 percent but left paraphrased training content and shame-related statements largely unchanged, even after direct correction. The authors stress the outputs are mapped onto human scales and do not suggest the models have emotional states or consciousness, and they propose simulated therapy sessions under different emotional framings for safety testing.