Large language models are being increasingly used to make high-stakes decisions that affect millions of people daily, making it crucial to detect and mitigate bias in these models. Bias in large language models refers to systematic patterns of error that produce unfair or prejudiced outputs for specific groups or topics, and can manifest in various forms, including intrinsic and extrinsic biases. These biases can be exploited by attackers to manipulate model outputs, bypass safety measures, or generate harmful content, highlighting the need for robust threat mitigation strategies. To address this issue, technical teams can use various techniques, such as auditing training data for demographic imbalances, using counterfactual examples to balance representations, and applying adversarial debiasing to suppress sensitive attribute leakage. Additionally, evaluating bias with multiple standardized benchmarks, analyzing attention patterns triggered by demographic cues, and monitoring model outputs continuously for fairness violations can help identify and mitigate bias. Furthermore, understanding the types of bias exploitation attacks, such as adversarial prompting, contextual manipulation, role-playing attacks, chained inference exploitation, and model jailbreaking, is essential for implementing effective protections in production environments. By adopting a defense-in-depth strategy that includes generating adversarial examples to test bias vulnerabilities, running red team exercises to uncover bias exploitation paths, and implementing runtime detection systems for bias attacks, teams can prevent bias exploitation and build more equitable and secure AI systems.