The recent advancements in the field of large language models (LLMs) have primarily focused on enhancing security and robustness against various adversarial attacks. Researchers are increasingly developing methods to detect and mitigate threats such as RAG poisoning, membership inference, memorization, backdoor attacks, and spurious correlations in neural network interpretations. Notably, the introduction of techniques like RevPRAG for detecting poisoned responses in RAG architectures and GraCeFul for filtering backdoor samples without retraining LLMs represent significant strides in this direction. These innovations not only improve the accuracy and efficiency of detection but also offer new insights into the internal mechanisms of LLMs, contributing to both model interpretability and practical security enhancements. The field is moving towards more sophisticated and automated detection pipelines that leverage LLM activations and frequency-based analyses to identify and counteract adversarial behaviors effectively. Additionally, there is a growing emphasis on methods that can intervene on specific activations to suppress memorization and spurious correlations without degrading overall model performance, thereby enhancing the integrity and reliability of LLMs in real-world applications.