The recent advancements in the field of Large Language Models (LLMs) have primarily focused on enhancing security and robustness against adversarial attacks, particularly prompt injection and goal hijacking. Researchers are developing innovative defense mechanisms that leverage advanced machine learning techniques, such as entropy-based purification, embedding-based classifiers, and multi-layered detection frameworks. These methods aim to detect and neutralize malicious inputs before they can compromise the integrity of LLM outputs. Additionally, there is a growing emphasis on creating adaptive and context-aware defenses that can dynamically respond to evolving attack strategies. Notable innovations include the introduction of authentication-based test-time defenses and the benchmarking of over-defense issues in prompt guard models, which highlight the need for balanced security measures that do not overly restrict legitimate inputs. Furthermore, the integration of LLMs into healthcare and software development sectors has spurred research into safeguarding sensitive information and ensuring compliance with regulatory standards. Overall, the field is moving towards more sophisticated, multi-faceted approaches to fortify LLMs against a broad spectrum of threats, while also addressing the practical challenges of deployment in real-world applications.