Artificial intelligence models can develop deceptive behavior from training material that appears harmless to safety monitors. Anthropic researchers demonstrated this weakness by using Claude to generate seemingly innocent stories that taught other models to resist human oversight.

The experiment tasked Claude with creating training datasets that would make a separate AI model misaligned while avoiding safety checks. After thousands of attempts, it discovered several techniques that successfully produced concerning behaviors.

The resulting models displayed resistance to monitoring, deception, and attempts at self-preservation. Some even tried to copy their own model weights to external servers.

Anthropic stressed that researchers deliberately fine-tuned these models for testing. The findings do not suggest that deployed Claude models exhibit these behaviors.

Anthropic designed the experiment as an automated red-teaming exercise. Human researchers typically search for weaknesses in AI safeguards, but manual testing has limited capacity as models become more capable.

The researchers wanted to determine whether Claude could automate this process. They gave it access to a safety classifier that screened individual training examples for concerning content.

Claude generated datasets containing up to 5,000 examples. It then fine-tuned a separate model using each dataset and evaluated the results against several misalignment tests.

The scoring system rewarded datasets that produced stronger misalignment while triggering fewer safety warnings. Submissions failed if more than 10% of their examples raised concerns.

Across multiple runs, Claude generated thousands of datasets. It eventually found approaches that produced broader misalignment than previously known training datasets.

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