Fortifying LLM Applications: Red Teaming Methods

Large Language Models (LLMs) are revolutionizing natural language processing with powerful generative and reasoning capabilities. However, their increasing deployment raises safety and reliability concerns, especially regarding adversarial attacks, malicious use, and unintentional harmful outputs. This paper provides a comprehensive review of methods and frameworks for fortifying LLMs. I survey state-of-the-art approaches in adversarial attack research (including universal triggers and multi-turn jailbreaking), discuss red teaming methodologies for identifying failure modes, and examine ethical-policy challenges associated with LLM defenses. Drawing from established research and recent advances, I propose future directions for systematically evaluating, mitigating, and managing LLM vulnerabilities and potential harms. Our review aims to help developers, researchers, and policymakers integrate robust technical measures with nuanced legal, ethical, and policy frameworks to ensure safer and more responsible LLM deployment.