---
id: 20260506-T0-05
title: "扩散模型被曝底层漏洞：仅篡改时间步编码即可注入恶意信息"
title_en: "Vulnerability Found in Diffusion Models via Shadow Timestep Embedding"
url: https://ai.daily.yangsir.net/daily/20260506-T0-05
issue_date: 2026-05-06
publish_date: 2026-05-05T04:00:00.000Z
category: research
source_name: "arXiv cs.LG (ML)"
source_url: https://arxiv.org/abs/2605.00935
---

# 扩散模型被曝底层漏洞：仅篡改时间步编码即可注入恶意信息

arXiv发表的一项最新研究指出了扩散模型（Diffusion Models）底层架构中存在的安全隐患。研究人员发现，扩散模型管道中至关重要的“时间步编码”组件可以被恶意利用。通过一种名为“Shadow Timestep Embedding”的隐蔽信息注入方法，攻击者可以在不改变模型主体的情况下，篡改生成过程并植入恶意指令或隐藏数据。这一漏洞直接影响当前主流的AI图像和视频生成系统，提醒相关平台和开发者必须重新审视生成管道的输入验证机制，防范基于底层架构的供应链式攻击。

## English Version

**Vulnerability Found in Diffusion Models via Shadow Timestep Embedding**

A recent study published on arXiv exposes a critical vulnerability in the foundational architecture of diffusion models. Researchers demonstrated that the timestep embedding component, a core part of the generation pipeline, is susceptible to malicious manipulation. Using a technique termed "Shadow Timestep Embedding," attackers can stealthily inject harmful instructions or hidden data into the generation process without altering the main model. This vulnerability directly impacts current mainstream AI image and video generation platforms, urging developers to tighten input validation mechanisms to prevent supply-chain style attacks on generative pipelines.

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**来源**：[arXiv cs.LG (ML)](https://arxiv.org/abs/2605.00935)

**详情页**：https://ai.daily.yangsir.net/daily/20260506-T0-05

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