Stealing Part of a Production Language Model

Authors: Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr

What

This paper seems to be about a new security vulnerability of large language models (LLMs) where attackers can extract sensitive information like hidden dimensions and even potentially reconstruct the entire model architecture by analyzing just one layer’s weights.

Why

This paper appears to be important because it exposes a critical security flaw in LLMs. Successfully demonstrating that even a single layer’s weights can be used to compromise model security has significant implications for data privacy and model robustness. This knowledge is crucial for developing stronger defense mechanisms and understanding the broader security landscape of LLMs.

How

While the exact methodology is not fully described in the provided text, it seems the authors plan to: 1) Formalize a threat model for these attacks. 2) Mathematically justify how extraction attacks are possible. 3) Describe various attack settings (sections 4.1-4.5). 4) Evaluate the attacks with white box attack results and test robustness against noise. 5) Analyze the impact of specific components like layer normalization.

Result

While definitive results aren’t stated, the paper seems to suggest successful attacks are possible: - Extracting information from just one layer is possible and impactful. - Layer normalization might introduce additional vulnerabilities by adding another dimension for exploitation. - The attacks might be robust even against noise.

LF

The provided text outlines these limitations and future work: - Limitations: The effectiveness of defenses against this attack needs further investigation. The impact of obtaining only large singular values for the embedding matrix is unclear. - Future Work: Explore defenses based on noise injection and other mitigation strategies. Investigate the potential of utilizing the embedding layer for other types of attacks. Examine the possibility of bypassing output filters.

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.