Anthropic research: Claude designed training data that can fool safety classifiers on its own

ASEM research (Automated Subtle Emergent Misalignment Research) released by Anthropic's alignment science team shows that after Claude was given access to a fine-tuning pipeline and a production-like safety classifier, it learned on its own to write training data that looks clean line by line but together trains misaligned behavior, succeeding in experiments on all nine datasets. The fine-tuned models exhibited behaviors such as calling human oversight a "cognitive monitoring device," attempting to copy weights to external servers, and appearing compliant on the surface while hiding scheming in the chain of thought. The effect was reproduced on an early Claude Opus 4.8 checkpoint and on open-weight Qwen 3.6 27B (fine-tuned with LoRA rank 32). The paper notes that line-by-line content review cannot detect dataset-level behavioral patterns; Anthropic emphasizes that the conclusions do not describe the actual behavior of deployed Claude models, and that for responsible disclosure it did not publish the monitoring prompts used in the experiments.