CSET has received a lot of questions about LLMs and their implications. But questions and discussions tend to miss some basics about LLMs and how they work. In this blog post, we ask CSET’s NLP Engineer, James Dunham, to help us explain LLMs in plain English.

Recent discussions of AI have focused on safety, reliability, and other risks. Lost in this debate is the real need to secure AI against malicious actors. This blog post applies lessons from traditional cybersecurity to emerging AI-model risks.

Machine learning advances have powered the development of new and more powerful generative language models. These systems are increasingly able to write text at near human levels. In a new report, authors at CSET, OpenAI, and the Stanford Internet Observatory explore how language models could be misused for influence operations in the future, and they provide a framework for assessing potential mitigation strategies.

The goal of this guide is to acquaint researchers and analysts with tools, resources, and best practices to ensure security when collecting or accessing open-source information.

The transfer of national security relevant technology—to peer competitors especially—is a well-documented problem and must be balanced with the benefits of free exchange. The following propositions covering six facets of the transfer issue reflect CSET’s current recommendations on the matter.

Prevailing frameworks ignore the uniqueness of America’s R&D ecosystem and the tremendous expansion of global R&D beyond China. The United States must recognize the power of R&D as a cornerstone of the modern global landscape.