We investigated the risks of advanced language models (LLMs) in areas relevant to national security through "red teaming" or adversarial testing, a recognized technique to measure and increase safety and security of systems. Our goal was to evaluate a baseline of risk and create a repeatable way to perform frontier threats red teaming across many topic areas. We found that current LLMs can produce sophisticated, accurate, useful, and detailed knowledge at an expert level, but also identified mitigations such as changes in the training process and classifier-based filters to reduce harmful outputs. Our research has significant implications for AI safety and security, particularly if unmitigated, and we believe it's essential to increase efforts before a further generation of models that use new tools are released.