Formula Predicts When AI Chatbots Are at Risk of Turning Bad
GWU researchers propose a formula predicting when chatbot attention tips into misaligned outputs.
Researchers Neil Johnson and Frank (Yingjie) Huo at George Washington University published a mathematical model of when transformer chatbots shift from aligned to undesirable outputs. They argue competition in the attention head between conversation context and competing output basins creates a tipping point after a number of good responses. The formula was tested on seven open-weight models from three groups, ranging from 124 million to 12 billion parameters, and matched predicted immediate versus delayed tipping regimes. They suggest an internal warning signal before the next output, and the article notes that prompt injection and poorly bounded agents can also drive systems off course.
- Formula estimates good outputs before the first undesirable response.
- Tested on seven open-weight transformers from 124M to 12B parameters.
- Tipping can spread among interacting agents without a human attacker.
- Authors propose an in-model warning light before the next output.
- Closed models from OpenAI and Anthropic were not instrumented.
- Tags
- ai-safetyalignmenttransformersattentionmisalignmentopen-weightprompt-injectiongeorge-washington-university
From the original article
4 min read at securityweek.com
Researchers from George Washington University have published a paper examining whether the time and cause of AI going rogue can be predicted. The post Formula Predicts When AI Chatbots Are at Risk of Turning Bad appeared first on SecurityWeek.
By Kevin Townsend · securityweek.com
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