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arXiv · 2611.00102

Demystifying Prompt Stealing Attacks against Synthetic Video

Junyan Huang·Sanskar Agmain·Chen Gong·Yan Pang·FNU Suya
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Abstract

Creators publicly showcase generated content to advertise and sell their work, but these examples may also enable unauthorized reproduction and customization. We study whether an adversary can derive reusable inputs from a single public video for faithful reconstruction and controlled editing, without access to the original prompt or generator. We adapt captioning and prompt optimization approaches to video prompt stealing attacks (PSAs) and find that prompt recovery alone provides limited reconstruction fidelity. Even the original prompt produces different spatial and temporal realizations when the initial noise changes. Using an independent generator enlarges the reconstruction gap, as reusing the original prompt and seed can still produce substantially different videos. We propose V-PSA, which uses a recovered caption and DDIM inversion to construct a reference trajectory under an independent generator. Null-Text Inversion then optimizes per-step unconditional embeddings to reduce deviations from this trajectory during reconstruction. The resulting inputs also guide generation when the caption is modified, supporting edits that change selected content while preserving the target's layout and motion. We construct a benchmark containing 12,000 video-prompt pairs from three victim generators and four prompt sources. V-PSA achieves video-to-video similarity around 0.9 between target and reconstructed videos, substantially improving over prompt-only baselines with similarity score of 0.6, and supports multiple controlled variations that preserve the target's structure. Additional experiments show that augmenting prior image PSAs with inversion-derived inputs also improves reconstruction. Our results show that evaluations restricted to prompt recovery can underestimate an attacker's ability to reconstruct public generative outputs.

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BibTeXRIS

Junyan Huang, Sanskar Agmain, Chen Gong, Yan Pang, FNU Suya. 2026-10-26. Demystifying Prompt Stealing Attacks against Synthetic Video. https://arxiv.org/abs/2611.00102

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