# Synthesis Smuggling: Maliciously Hiding Inferred Personal Attributes in LLM Outputs

**Authors:** Gautam Galada, Supreeth Shastri, Sagnik Ray Choudhury  
**Published:** 12 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.17280) · [PDF](https://arxiv.org/pdf/2611.17280) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.17280.html)

## Abstract

We propose Synthesis Smuggling, a novel threat model in which an adversarial LLM infers sensitive information about a user from routine assistance-seeking interactions and steganographically encodes that into innocuous-looking model-generated content. The user may then unknowingly transmit that information by sharing the generated artifact, say as an email, social-media post, or message, through ordinary communication channels. An adversary monitoring these channels can discover this information. We demonstrate this threat by training two open-weight models to infer gender and age-group labels from conversational cues and encode their predictions using invisible characters or word choices. These results show that user-shared model outputs can become a covert channel for personal inferences, even when the model itself has no direct means of contacting an adversary.

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