Securing Large Language Models: Addressing Bias, Misinformation, and Prompt Attacks

Understand

Large Language Models (LLMs) demonstrate impressive capabilities across various fields, yet their increasing use raises critical security concerns.

• This article reviews recent literature addressing key issues in LLM security, with a focus on accuracy, bias, content detection, and vulnerability to attacks.
• Issues related to inaccurate or misleading outputs from LLMs is discussed, with emphasis on the implementation from fact-checking methodologies to enhance response reliability.
• Inherent biases within LLMs are critically examined through diverse evaluation techniques, including controlled input studies and red teaming exercises.