Online Safety Monitoring for LLMs

Even after alignment training, deployed LLMs can still produce unsafe outputs, making real-time monitoring essential for catching failures as they happen. This paper studies a straightforward monitoring approach: taking a safety score from an external verifier model and triggering an alarm once it crosses a calibrated threshold. Tested on mathematical reasoning and red-teaming datasets, this simple method performs competitively against more complex monitors built on sequential hypothesis testing. The practical implication is that safety teams may not need elaborate statistical machinery to catch unsafe generations in production—a well-calibrated threshold on existing verifier signals can already do much of the work. Authors: Mona Schirmer, Metod Jazbec, Alexander Timans, Christian Naesseth, Maja Waldron, Eric Nalisnick Paper: https://arxiv.org/abs/2607.02510v1