ICML Poster Stealing part of a production language model

Poster

Stealing part of a production language model

Nicholas Carlini · Daniel Paleka · Krishnamurthy Dvijotham · Thomas Steinke · Jonathan Hayase · A. Feder Cooper · Katherine Lee · Matthew Jagielski · Milad Nasr · Arthur Conmy · Eric Wallace · David Rolnick · Florian Tramer

Hall C 4-9 #2308

Best Paper

[ Abstract ] [ Paper PDF ]

Wed 24 Jul 4:30 a.m. PDT — 6 a.m. PDT

Oral presentation: Oral 4C Safety and Control
Wed 24 Jul 7:30 a.m. PDT — 8:30 a.m. PDT

Abstract: We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like OpenAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under $20 USD, our attack extracts the entire projection matrix of OpenAI's Ada and Babbage language models. We thereby confirm, for the first time, that these black-box models have a hidden dimension of 1024 and 2048, respectively. We also recover the exact hidden dimension size of the GPT-3.5-turbo model, and estimate it would cost under \\$2,000 in queries to recover the entire projection matrix. We conclude with potential defenses and mitigations, and discuss the implications of possible future work that could extend our attack.

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