Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding

arXiv cs.LG / 5/5/2026

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Key Points

The paper argues that timestep embeddings in diffusion models—used as temporal conditioning for denoising—have been largely overlooked despite potentially carrying substantial hidden information.
It introduces Shadow Timestep Embedding (STE) to probe how malicious side-channel information can be injected through the timestep-embedding “temporal space.”
The authors show that different timesteps have distinct representational capabilities, making it possible to encode side-channel information that can be exploited.
They demonstrate that this encoded information can be used via the scheduler interface for both attack and defense, tying security implications directly to the diffusion pipeline’s scheduling.
The work provides theoretical analysis treating timestep embeddings as position-encoding mappings and uses mutual coherence to explain why separate timestep intervals can be separable.

Abstract

Diffusion models have become the foundation of modern generative systems, with most research focusing primarily on improving generation efficiency and output quality. The timestep embedding component is a crucial part of the diffusion pipeline, which provides a temporal conditioning signal to the denoising network, enabling it to adapt its predictions across different noise levels throughout the process. Despite their potential to contain substantial information, timestep embeddings remain underexplored in current research, especially for security risks and reliable provenance. To fill this gap, we introduce Shadow Timestep Embedding (STE), a novel mechanism that investigates the underutilized temporal space for malicious information injection into diffusion models. In particular, when zooming in on the timestep embedding space, we find that different timesteps exhibit distinct representational capabilities that can encode side-channel information. Moreover, such encoded information can be utilized for attack and defense purposes through the scheduler interface. We present a theoretical analysis of timestep embeddings as position-encoding mappings and derive a mutual coherence evaluation that explains the separability of disjoint timestep intervals. Our findings reveal the diffusion model's timestep as a powerful side channel for carrying dedicated information, motivating new directions for adversarial generative modeling by understanding the temporal dimension.