Designing a Red Teaming Protocol to Stress-Test LLM Security in Production Systems

With the rapid evolution and adoption of Large Language Models (LLMs), these AI systems have become foundational to a wide range of applications—from automating customer support and generating human-like content to enabling intelligent virtual assistants and summarizing large volumes of information. As their capabilities grow more sophisticated and their integration deepens across sectors, concerns around their security and reliability have become increasingly urgent. This paper presents a comprehensive framework for red teaming Large Language Models, aimed at identifying and mitigating vulnerabilities before they are exploited in real-world scenarios. Red teaming, traditionally used in cybersecurity to simulate adversarial attacks, is adapted here to stress-test LLMs against a variety of emerging threats. These include prompt injection attacks (where malicious inputs manipulate model behaviour), hallucinations (where the model generates factually incorrect or misleading content), and unintended disclosure of sensitive or private information embedded in training data or prompt history. Ultimately, this work promotes a proactive and ethical approach to AI security—empowering developers, researchers, and enterprises to responsibly deploy LLMs that are not only intelligent but also trustworthy, transparent, and resilient against adversarial threats.