A Flexible Large Language Models Guardrail Development Methodology
  Applied to Off-Topic Prompt Detection
Authors: name: Gabriel Chua, name: Shing Yee Chan, name: Shaun Khoo

Large Language Models are prone to off-topic misuse, where users may prompt
these models to perform tasks beyond their intended scope. Current guardrails,
which often rely on curated examples or custom classifiers, suffer from high
false-positive rates, limited adaptability, and the impracticality of requiring
real-world data that is not available in pre-production. In this paper, we
introduce a flexible, data-free guardrail development methodology that
addresses these challenges. By thoroughly defining the problem space
qualitatively and passing this to an LLM to generate diverse prompts, we
construct a synthetic dataset to benchmark and train off-topic guardrails that
outperform heuristic approaches. Additionally, by framing the task as
classifying whether the user prompt is relevant with respect to the system
prompt, our guardrails effectively generalize to other misuse categories,
including jailbreak and harmful prompts. Lastly, we further contribute to the
field by open-sourcing both the synthetic dataset and the off-topic guardrail
models, providing valuable resources for developing guardrails in
pre-production environments and supporting future research and development in
LLM safety.