LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis

As Large Language Models (LLMs) become increasingly integrated into many technological ecosystems across various domains and industries, identifying which model is deployed or being interacted with is critical for the security and trustworthiness of the systems. Current verification methods typicall...

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Bibliographic Details
Main Authors:	Saeif Alhazbi, Ahmed Hussain, Gabriele Oligeri, Panos Papadimitratos
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Open Journal of the Communications Society
Subjects:	Large language models small language models fingerprinting network traffic analysis deep learning network security
Online Access:	https://ieeexplore.ieee.org/document/11026013/
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LLMs Have Rhythm: Fingerprinting Large Language Models Using Inter-Token Times and Network Traffic Analysis

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