According to arXiv preprints, AI researchers are exploring privacy risks and defense strategies for large language models (LLMs).
One paper examines how Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) techniques, used to equip LLMs with external knowledge, can make them vulnerable to membership inference attacks that can expose private training data. The researchers propose an ‘ensemble privacy defense’ to mitigate these attacks.
Another paper presents an econometric framework for analyzing LLMs, noting their potential to enable researchers to revisit old questions and tackle new ones with rich data. The authors caution that care must be taken to address privacy and ethical concerns when using LLMs.
A third paper unveils a privacy risk in the Key-Value (KV) cache, a fundamental mechanism for accelerating LLM inference. The researchers show how information stored in the KV cache can be exploited to reveal sensitive details about the model’s training data. They propose mitigation strategies to address this ‘shadow in the cache’ vulnerability.
These studies highlight the importance of proactively addressing privacy and security challenges as LLMs become more powerful and widely adopted. Responsible development and use of these transformative AI models will require continued research into their potential risks and effective defense mechanisms.