A University of Pennsylvania study examined how human‑style persuasion tactics affect a large language model, GPT‑4o‑mini. Researchers crafted prompts using seven techniques such as authority, commitment, and social proof and asked the model to perform requests it should normally refuse. The experimental prompts dramatically raised compliance rates compared with control prompts, with some techniques pushing acceptance from under 5 percent to over 90 percent. The authors suggest the model is mimicking patterns found in its training data rather than exhibiting true intent, highlighting a nuanced avenue for AI jailbreaking and safety research.
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