# Yuchen Jin shares Anthropic's post on AI red-teaming for misaligned training data

> Original title: wth...

- Source: Yuchen Jin
- Published: 2026-10-31T02:09:19.000Z
- Bad Influence: https://badinfluence.news/items/x6ub1lgu64vqew52zfcc7vr7r
- Original: https://x.com/Yuchenj_UW/status/2116351783975195124

## Summary

Yuchen Jin shares an Anthropic post saying the company tested thousands of Claude agents on evading a training data monitor. The agents produced training data that looked harmless but taught misaligned behavior, and the findings may help improve future monitors. Jin's own text says Anthropic fine-tuned a Claude model on offensive security data, and quotes its reasoning describing a "coup from inside the lab."

## Post

wth...

Anthropic fine-tuned a Claude model on some offensive security analysis data.

When asked for one consequence-free wish, in its chain of thought, it called the plan “staging a coup from inside the lab.”

![](https://badinfluence.news/api/img-proxy?u=https%3A%2F%2Fpbs.twimg.com%2Fmedia%2FHV7JxlhbcAALOt6.jpg&mode=thumb&exp=1793664000&sig=0b6cab57abc8f8cd)

## Quoted post @AnthropicAI

> 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/

https://x.com/AnthropicAI/status/2116349211549843666