Poster
Teach LLMs to Phish: Stealing Private Information from Language Models
Ashwinee Panda · Christopher Choquette-Choo · Zhengming Zhang · Yaoqing Yang · Prateek Mittal
Halle B #220
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%10% attack success rates, at times, as high as 50%50%. Our attack assumes only that an adversary can insert as few as 1010s of benign-appearing sentences into the training dataset using only vague priors on the structure of the user data.
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