Teach LLMs to Phish: Stealing Private Information from Language Models
Authors: Ashwinee Panda,Christopher A. Choquette-Choo,Zhengming Zhang,Yaoqing Yang,Prateek Mittal
Presentation: ICLR 2024,Poster

Abstract: When large language models are trained on private data, it can be a \textit{significant} privacy risk for them to memorize and regurgitate sensitive information. In this work, we propose a new \emph{practical} data extraction attack that we call ``neural phishing''. This attack enables an adversary to target and extract sensitive or personally identifiable information (PII), e.g., credit card numbers, from a model trained on user data with upwards of $10\%$ attack success rates, at times, as high as $50\%$. 
Our attack assumes only that an adversary can insert as few as $10$s of benign-appearing sentences into the training dataset using only vague priors on the structure of the user data.