RoleFUZZER: User-Behaviour Fuzzing for Role-Specification Adherence in Large Language Models
arXiv:2610.38438v1 Announce Type: cross Abstract: Large language models (LLMs) are often conditioned into specific roles such as tutors, interviewers, or agents acting for an organisation, through system prompts that specify how they should behave. The extent to which they can actually adhere to these role specifications under multi-turn interactions with realistic and potentially problematic users is understudied. To address this, we propose user behaviour fuzzing, a guided search over plausib…