PlugGuard: A Streaming Safeguard for Large Models via Latent Dynamics-Guided Risk Detection

Abstract

Large models (LMs) are powerful content generators, yet their open‑ended nature can also introduce potential risks, such as generating harmful or biased content. Existing guardrails mostly perform post-hoc detection that may expose unsafe content before it is caught, and the latency constraints further push them toward lightweight models, limiting detection accuracy. In this work, we propose PlugGuard, a novel plug-in framework that enables streaming risk detection within the LM generation pipeline. PlugGuard leverages intermediate LM hidden states through a Streaming Latent Dynamics Head (SLD), which models the temporal evolution of risk across the generated sequence for more accurate real-time risk detection. To ensure reliable streaming moderation in real applications, we introduce an Anchored Temporal Consistency (ATC) loss to enforce monotonic harm predictions by embedding a benign-then-harmful temporal prior. Besides, for a rigorous evaluation of streaming guardrails, we also present StreamGuardBench—a model-grounded benchmark featuring on-the-fly responses from each protected model, reflecting real-world streaming scenarios in both text and vision–language tasks. Across diverse models and datasets, PlugGuard consistently outperforms state-of-the-art post-hoc guardrails and prior plug-in probes (15.61% higher average F1), while using only 20M parameters and adding less than 0.5 ms of per-token latency. The code and StreamGuardBench are released at PlugGuard to facilitate research on streaming guardrails.