ResearchResearch paperLarge Language Models · Speech & Audio · Safety & Alignment1 source · Oct 21, 2026

Privacy in Context Is Being Overlooked in the Age of General-Purpose LLMs

As large language models (LLMs) continue to grow in scope and capability, privacy concerns are on the rise.

Since users increasingly interact with a single general-purpose system across many distinct contexts, there is a natural tendency to pursue a one-size-fits-all solution to privacy.
Unfortunately, this overlooks a crucial insight from decades of privacy research: privacy is inherently contextual.
In this paper, we argue that existing work in prompt privacy is drifting towards attempts to design a unified framework, which is highly misleading and can foster a false sense of security.
We recommend three critical paths forward: application-specific deployment of LLMs, context-specific evaluation of privacy frameworks, and better privacy literacy.

[1]Privacy in Context Is Being Overlooked in the Age of General-Purpose LLMs
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:01 PM
As large language models (LLMs) continue to grow in scope and capability, privacy concerns are on the rise.
Since users increasingly interact with a single general-purpose system across many distinct contexts, there is a natural tendency to pursue a one-size-fits-all solution to privacy.

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