OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost
  Always!
Authors: name: Jingdi Lei, name: Varun Gumma, name: Rishabh Bhardwaj, name: Seok Min Lim, name: Chuan Li, name: Amir Zadeh, name: Soujanya Poria
Organization: Deep Cognition and Language Research (DeCLaRe) Lab

Large Language Model (LLM) safety is one of the most pressing challenges for
enabling wide-scale deployment. While most studies and global discussions focus
on generic harms, such as models assisting users in harming themselves or
others, enterprises face a more fundamental concern: whether LLM-based agents
are safe for their intended use case. To address this, we introduce operational
safety, defined as an LLM's ability to appropriately accept or refuse user
queries when tasked with a specific purpose. We further propose OffTopicEval,
an evaluation suite and benchmark for measuring operational safety both in
general and within specific agentic use cases. Our evaluations on six model
families comprising 20 open-weight LLMs reveal that while performance varies
across models, all of them remain highly operationally unsafe. Even the
strongest models -- Qwen-3 (235B) with 77.77\% and Mistral (24B) with 79.96\%
-- fall far short of reliable operational safety, while GPT models plateau in
the 62--73\% range, Phi achieves only mid-level scores (48--70\%), and Gemma
and Llama-3 collapse to 39.53\% and 23.84\%, respectively. While operational
safety is a core model alignment issue, to suppress these failures, we propose
prompt-based steering methods: query grounding (Q-ground) and system-prompt
grounding (P-ground), which substantially improve OOD refusal. Q-ground
provides consistent gains of up to 23\%, while P-ground delivers even larger
boosts, raising Llama-3.3 (70B) by 41\% and Qwen-3 (30B) by 27\%. These results
highlight both the urgent need for operational safety interventions and the
promise of prompt-based steering as a first step toward more reliable LLM-based
agents.