[Linkpost] “Automating subtle emergent misalignment research” by evhub, Oscar Obeso, rqi, Monte M

This is a link post. Authors: Oscar Balcells Obeso, Richard Qi, Monte MacDiarmid, Evan Hubinger Abstract Red-teaming by humans is a vital part of model safety testing, but it may struggle to keep up as models improve. To test whether automated safety techniques could scale (which would allow us to turn more compute into progressively more safety confidence), we set up a deliberately one-sided game as a first test. We instructed a Claude model to write training data that would make a small research model misaligned while passing a monitor similar to those we use in production — one of several defenses we use — and we gave it that monitor to practice against. Given enough attempts, it succeeded, mostly by hiding the behavior in fiction and roleplay. While the data was optimized for this game and the resulting models never deployed, the search surfaced a kind of training data that looks harmless to monitors but teaches misaligned behavior. Examples like these could represent a possible gap in current training monitoring defenses and could be used to test and improve future monitors. We see this work as a first step toward automated adversarial stress-testing of our safety systems where a [...] --- Outline: (00:22) Abstract (01:38) Twitter thread (02:40) Read the full blog post here! --- First published: November 3rd, 2026 Source: https://www.lesswrong.com/posts/MuQDLAJyKJPFkw6q9/automating-subtle-emergent-misalignment-research Linkpost URL: https://alignment.anthropic.com/2026/automated-em-search --- Narrated by TYPE III AUDIO . --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts , or another podcast app.