Logits of API-Protected LLMs Leak Proprietary Information
This work addresses security and transparency issues for LLM providers and users by revealing vulnerabilities in API protections, with incremental improvements in attack methods.
The paper tackles the problem of proprietary information leakage from API-protected large language models (LLMs) by exploiting the softmax bottleneck, enabling capabilities like estimating hidden sizes and auditing updates with a small number of queries (e.g., under $1000 for gpt-3.5-turbo), and empirically estimates the embedding size of OpenAI's gpt-3.5-turbo to be about 4096.
Large language model (LLM) providers often hide the architectural details and parameters of their proprietary models by restricting public access to a limited API. In this work we show that, with only a conservative assumption about the model architecture, it is possible to learn a surprisingly large amount of non-public information about an API-protected LLM from a relatively small number of API queries (e.g., costing under $1000 USD for OpenAI's gpt-3.5-turbo). Our findings are centered on one key observation: most modern LLMs suffer from a softmax bottleneck, which restricts the model outputs to a linear subspace of the full output space. We exploit this fact to unlock several capabilities, including (but not limited to) obtaining cheap full-vocabulary outputs, auditing for specific types of model updates, identifying the source LLM given a single full LLM output, and even efficiently discovering the LLM's hidden size. Our empirical investigations show the effectiveness of our methods, which allow us to estimate the embedding size of OpenAI's gpt-3.5-turbo to be about 4096. Lastly, we discuss ways that LLM providers can guard against these attacks, as well as how these capabilities can be viewed as a feature (rather than a bug) by allowing for greater transparency and accountability.