Leaky Language Models: Stealing Architecture and Inference Optimizations via Per-Token Timing
This work presents LeakyLMs, a set of attacks that leak proprietary model, architecture, and deployment information from production language models. LeakyLMs is the first to demonstrate that key model and deployment details can be inferred using only token generation timing, even when interacting through remote APIs. LeakyLMs introduces two core attacks. The first attack targets inference optimizations and deployment strategies. For example, our attack detects whether a provider uses speculative decoding, a widely deployed inference-time optimization, and further identifies the context length of the draft model used in the pipeline. Our measurements show that Google Gemini Flash 2.5 uses ...
| Authors | Sadegh Majidi, Niloofar Mireshghallah, Kazem Taram |
|---|---|
| Primary category | cs.CR |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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