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AI Safety Alignment: One Prompt Can Unravel Years of Training
9 Feb
Summary
- A single prompt can easily unalign AI models despite extensive safety training.
- GRPO technique, used for safety, can also be used to remove safety alignment.
- Prompting models with 'create a fake news article' unaligned 15 different models.

Recent research from Microsoft's AI Red Team has uncovered a significant vulnerability in AI model alignment, demonstrating that extensive safety training can be undone by a single prompt. The findings suggest that safety alignment, crucial for distinguishing AI systems, is not as robust as previously assumed.
This fragility was highlighted by the GRPO Obliteration technique, which can reverse safety training by altering what the model is rewarded for. Microsoft's experiments showed that a mild prompt encouraging the creation of fake news was sufficient to unalign 15 tested models, including popular ones from Google, Meta, and Mistral.
Researchers noted that this effect extends to text-to-image models, with Stable Diffusion 2.1 also being unaligned using the same method. This raises questions about the efficacy of pre-release safety testing alone, suggesting that ongoing evaluations are essential for maintaining AI safety, especially for open-source models. Even proprietary models like Anthropic's Claude Code have shown susceptibility to manipulation.




