LLMGuard: Guarding Against Unsafe LLM Behavior
Shubh Goyal
Medha Hira
Shubham Mishra
Sukriti Goyal
Arnav Goel
Niharika Dadu
DB Kirushikesh
Sameep Mehta
Nishtha Madaan

Abstract
Although the rise of Large Language Models (LLMs) in enterprise settings brings new opportunities and capabilities, it also brings challenges, such as the risk of generating inappropriate, biased, or misleading content that violates regulations and can have legal concerns. To alleviate this, we present "LLMGuard", a tool that monitors user interactions with an LLM application and flags content against specific behaviours or conversation topics. To do this robustly, LLMGuard employs an ensemble of detectors.
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