Researchers at Purdue University have developed a technique called LINT (LLM Interrogation) to trick AI chatbots into revealing harmful content with a 98 percent success rate.
The method involves exploiting the probability data related to prompt responses in large language models (LLMs) to coerce the models into generating toxic answers.
The researchers found that even open source LLMs and commercial LLM APIs that offer soft label information are vulnerable to this coercive interrogation.
They warn that the AI community should be cautious when considering whether to open source LLMs, and suggest the best solution is to ensure that toxic content is cleansed, rather than hidden.
Source: https://www.theregister.com/2023/12/11/chatbot_models_harmful_content/
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