Side-Channel Attacks Threaten the Privacy of Large Language Model Interactions

The essay highlights recent research showing that side-channel attacks can extract sensitive information from large language models by observing indirect signals like response timing, packet sizes, and speculative decoding behavior, even when the communication is encrypted, and the content itself is not visible to an attacker. These studies demonstrate that metadata and implementation details can leak user query topics, language, or confidential data, underscoring an urgent need for better defenses as LLMs are deployed in sensitive contexts.

https://www.schneier.com/blog/archives/2026/02/side-channel-attacks-against-llms.html

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