Researchers also found that they could add additional rules to make sure users don’t exit the “game.” In this example, the researchers built a gaming framework for creating a set of “nested” games. Users who try to exit are still dealing with the same malicious game-player.

Hackers would need to launch a specific LLM to hypnotize it and deploy it in the wild — which would be quite the feat. However, if it’s achieved, Lee can see a scenario where a virtual customer service bot is tricked into providing false information or collecting specific personal data from users, for instance.

“By default, an LLM wants to win a game because it is the way we train the model, it is the objective of the model,” Lee told Axios. “They want to help with something that is real, so it will want to win the game.”

Not all LLMs fell for the test scenarios, and Lee says it’s still unclear why since each model has different training data and rules behind them.