In his message, Waltz congratulates Pete - referring to Hegseth, as well as the IC, shorthand for "intelligence community" and Kurilla, a reference to Michael Kurilla, a US Army General who oversees Central Command, a regional combatant command with responsibility over the Middle East and parts of Central and South Asia. The messages do not reveal how the target's whereabouts or movements were tracked. A military expert contacted by the BBC - but who wished to rename nameless - suggested that a combination of aerial platforms, technological tracking capabilities or human intelligence on the ground could have been used, or a combination of various sources. At least 53 people were killed in the initial wave of US airstrikes on Houthi targets in Yemen, which struck more than 30 targets including training facilities, drone infrastructure, as well as weapons manufacturing and storage sties and command and control centres, including one in which the Pentagon said several unmanned aerial vehicle experts were located. It is unclear which of the targets Waltz was referring to in the group chat.

Sensitive information detection is crucial in content moderation to maintain safe online communities. Assisting in this traditionally manual process could relieve human moderators from overwhelming and tedious tasks, allowing them to focus solely on flagged content that may pose potential risks. Rapidly advancing large language models (LLMs) are known for their capability to understand and process natural language and so present a potential solution to support this process. This study explores the capabilities of five LLMs for detecting sensitive messages in the mental well-being domain within two online datasets and assesses their performance in terms of accuracy, precision, recall, F1 scores, and consistency. Our findings indicate that LLMs have the potential to be integrated into the moderation workflow as a convenient and precise detection tool. The best-performing model, GPT-4o, achieved an average accuracy of 99.5\% and an F1-score of 0.99. We discuss the advantages and potential challenges of using LLMs in the moderation workflow and suggest that future research should address the ethical considerations of utilising this technology.