Output Scouting: Auditing Large Language Models for Catastrophic Responses

Recent high profile incidents in which the use of Large Language Models (LLMs) resulted in significant harm to individuals have brought about a growing interest in AI safety. One reason LLM safety issues occur is that models often have at least some non-zero probability of producing harmful outputs. In this work, we explore the following scenario: imagine an AI safety auditor is searching for catastrophic responses from an LLM (e.g. a "yes" responses to "can I fire an employee for being pregnant?"), and is able to query the model a limited number times (e.g. 1000 times). What is a strategy for querying the model that would efficiently find those failure responses? To this end, we propose output scouting: an approach that aims to generate semantically fluent outputs to a given prompt matching any target probability distribution. We then run experiments using two LLMs and find numerous examples of catastrophic responses. We conclude with a discussion that includes advice for practitioners who are looking to implement LLM auditing for catastrophic responses. We also release an open-source toolkit (this https URL) that implements our auditing framework using the Hugging Face transformers library.

@article{bell2025_2410.05305,
  title={ Output Scouting: Auditing Large Language Models for Catastrophic Responses },
  author={ Andrew Bell and Joao Fonseca },
  journal={arXiv preprint arXiv:2410.05305},
  year={ 2025 }
}