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arXiv · 2403.00826

LLMGuard: Guarding Against Unsafe LLM Behavior

Shubh Goyal·Medha Hira·Shubham Mishra·Sukriti Goyal·Arnav Goel·Niharika Dadu·Kirushikesh DB·Sameep Mehta·Nishtha Madaan
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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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Shubh Goyal, Medha Hira, Shubham Mishra, Sukriti Goyal, Arnav Goel, Niharika Dadu, Kirushikesh DB, Sameep Mehta, Nishtha Madaan. 2024-02-27. LLMGuard: Guarding Against Unsafe LLM Behavior. https://arxiv.org/abs/2403.00826

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