The field of machine learning is moving towards developing more secure and privacy-preserving models, with a particular focus on machine unlearning and language model security. Researchers are exploring new methods to prevent language models from memorizing and reproducing sensitive information, such as proprietary data or copyrighted content. One of the key directions is the development of unlearning algorithms that can efficiently and effectively remove unwanted information from trained models. Another important area of research is the detection of dataset membership and contamination, which can help prevent the inclusion of unauthorized data in model training. Furthermore, there is a growing interest in understanding the mechanisms behind language model behavior, including the analysis of repetition and verbatim reproduction. Notable papers in this area include: DP2Unlearning, which presents a novel framework for efficient and guaranteed unlearning of large language models. Verifying Robust Unlearning, which introduces a verification framework to detect residual knowledge in unlearned models. Certified Mitigation of Worst-Case LLM Copyright Infringement, which proposes a simple yet effective method for certified copyright takedown.