As LLMs become commercialized, security becomes a top concern, especially when these models are accessed only through high-level APIs. This investigation unveils a vulnerability known as the softmax bottleneck, which could allow for the extraction of substantial amounts of proprietary information from an API-protected LLM with a limited number of queries.

• The softmax bottleneck limits model outputs to a linear subspace, enabling various attacks with affordable costs.
• Researchers could discover model attributes like hidden sizes and parameters from companies like OpenAI.
• Differentiating between model updates and identifying source models from outputs become feasible.
• The paper proposes defensive measures for providers while suggesting this leak could actually enhance accountability.

This exploration into LLMs’ security issues emphasizes the necessity of robust protective measures to prevent proprietary data exposure. Additionally, it highlights an opportunity for transparency that can lead to a greater understanding and trust in the use of these models by the public.