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Teach LLMs to Phish: Stealing Private Information from Language Models

Ashwinee Panda · Christopher Choquette-Choo · Zhengming Zhang · Yaoqing Yang · Prateek Mittal

Halle B #220
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Thu 9 May 1:45 a.m. PDT — 3:45 a.m. PDT

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.

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