The field of natural language processing is witnessing significant developments in enhancing the privacy and security of large language models (LLMs). Researchers are exploring innovative approaches to address the vulnerabilities of LLMs, particularly in relation to prompt injection attacks and data privacy concerns. One notable direction is the use of federated learning and homomorphic encryption to ensure robust data protection while maintaining model performance. Another area of focus is the development of defense mechanisms that leverage the instruction-following capabilities of LLMs, rather than suppressing them, to filter out malicious instructions. Additionally, there is a growing interest in understanding and addressing the limitations of role separation in LLMs, which is crucial for consistent multi-role behavior. Noteworthy papers in this area include: The paper proposing Federated Retrieval-Augmented Generation (FedE4RAG) framework, which facilitates collaborative training of client-side RAG retrieval models while ensuring data privacy. The paper introducing CachePrune, a neural-based attribution defense against indirect prompt injection attacks, which identifies and prunes task-triggering neurons from the KV cache of the input prompt context.