Online Safety Monitoring for LLMs
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Computer Science > Artificial Intelligence
Title:Online Safety Monitoring for LLMs
Abstract: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.
| Comments: | ICML 2026 Hypothesis Testing Workshop |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML) |
| Cite as: | arXiv:2607.02510 [cs.AI] |
| (or arXiv:2607.02510v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.02510
arXiv-issued DOI via DataCite (pending registration)
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