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[2506.10805] Detecting High-Stakes Interactions with Activation Probes

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Abstract:Monitoring is an important aspect of safely deploying Large Language Models (LLMs). This paper examines activation probes for detecting ``high-stakes'' interactions -- where the text indicates that the interaction might lead to significant harm -- as a critical, yet underexplored, target for such monitoring. We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. Probes' performance is comparable to that of prompted or finetuned medium-sized LLM monitors, while offering computational savings of six orders-of-magnitude. These savings are enabled by reusing activations of the model that is being monitored. Our experiments also highlight the potential of building resource-aware hierarchical monitoring systems, where probes serve as an efficient initial filter and flag cases for more expensive downstream analysis. We release our novel synthetic dataset and the codebase at this https URL.

[2506.10805] Detecting High-Stakes Interactions with Activation Probes Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:2506.10805 (cs) [Submitted on 12 Jun 2025 ( v1 ), last revised 23 Jan 2026 (this version, v4)] Title: Detecting High-Stakes Interactions with Activation Probes Authors: Alex McKenzie , Urja Pawar , Phil Blandfort , William Bankes , David Krueger , Ekdeep Singh Lubana , Dmitrii Krasheninnikov View a PDF of the paper titled Detecting High-Stake

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