Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timing

LeakyLMs introduces a set of attacks that leak proprietary model architecture and deployment information from production language models using only per-token generation timing. The method builds a detailed timing model of token generation on NVIDIA GPUs and performs a search over the architecture space to infer properties like transformer layers, hidden dimension size, and attention heads. Experimental evidence shows that for Llama models, the near-correct architectural configuration appears in the top-10 guesses over 90% of the time, and the attack successfully detects speculative decoding in Google Gemini Flash 2.5 with a draft context window of approximately 128K tokens. This matters because it demonstrates that sensitive model and deployment details can be stolen remotely via timing side channels, posing a significant security risk to proprietary language model services.