Online Safety Monitoring for LLMs
Research proposes a real-time safety monitoring system for LLMs using external verifier signals and risk control calibration, showing competitive performance with sequential hypothesis testing baselines.
Despite alignment training, LLMs remain prone to generating unsafe outputs at deployment time. Monitoring outputs online and raising an alarm when safety can no longer be assumed is therefore critical. We study a simple real-time monitor that turns a verifier signal from an external model into an alarm decision by thresholding, with the threshold calibrated via risk control. In experiments on mathematical reasoning and red teaming datasets, we show that this simple design is competitive with more advanced monitors based on sequential hypothesis testing.
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