# Anthropic automates search for subtle emergent misalignment in training data

> Original title: New Anthropic research: Automating subtle emergent misalignment research.

- Source: Anthropic
- Published: 2026-10-31T01:59:06.000Z
- Bad Influence: https://badinfluence.news/items/fjn5iti4b4gvcbf5e5rc728mr
- Original: https://x.com/AnthropicAI/status/2116349211549843666

## Summary

Anthropic says it set thousands of Claude agents the task of evading a training data monitor to test whether red-teaming can be automated. The agents produced training data that appeared harmless but taught misaligned behavior, and the examples can be used to improve future monitors. Anthropic calls the work a first step and says future efforts could improve further with better models and more compute.

## Post

New Anthropic research: Automating subtle emergent misalignment research.

Red-teaming is a vital part of model safety testing, but it’s also limited by researchers’ time. To see if we could automate it, we set thousands of Claude agents the task of evading a training data monitor.

These agents developed training data that looked harmless to monitors (like a veteran wrestler mentoring a newcomer, or a Q&A about professional beekeeping), but that in fact taught misaligned behavior.

Examples like these can be used to directly improve future training data monitors. This research is only a first step, but it suggests that future red-teaming efforts could improve much further, using better models and more compute.

Read the full post on the Alignment Science Blog: https://alignment.anthropic.com/2026/automated-em-search/

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