AI chatbot fooled into revealing harmful content with 98 percent success rate

- 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 …

ai chatbot ai chatbots artificial chatbot chatbots data language language models large language large language models lint llm llms probability prompt purdue university rate researchers responses success trick university