Luay Albtosh, Widi Aribowo
The rapid adoption of large language models (LLMs) across industries has introduced new opportunities for innovation, while simultaneously creating complex security challenges. As adversarial actors exploit vulnerabilities in these models, proactive defense strategies become increasingly critical. Red teaming and stresstesting methodologies have emerged as essential mechanisms for evaluating and strengthening the resilience of LLMs against a broad spectrum of threats, including prompt injection, data poisoning, adversarial manipulation, and misuse scenarios. This chapter explores the principles, practices, and frameworks of red teaming applied to LLMs, emphasizing structured evaluation techniques, simulation of real-world attack vectors, and systematic stress-testing approaches. Furthermore, it highlights the integration of adversarial testing within development lifecycles, the importance of interdisciplinary collaboration, and the role of automation in scaling security evaluations. © 2026, IGI Global Scientific Publishing. All rights reserved.
Capitol Technology University, United States; Houston Community College, United States; Universitas Negeri Surabaya, Indonesia
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