# Embedding Hidden Adversarial Capabilities in Pre-Trained Diffusion Models

arXiv: https://arxiv.org/abs/2504.08782 · HTML: https://mlsanity.com/paper/2504.08782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 21 of its 26 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Placed in

- Diffusion models / [Robustness, privacy and adversarial aspects](https://mlsanity.com/diffusion/robustness-privacy-and-adversarial-aspects/)

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Diffusion Models for Adversarial Purification](https://mlsanity.com/paper/2205.07460/) (2022)
- [Diffusion Models for Imperceptible and Transferable Adversarial Attack](https://mlsanity.com/paper/2305.08192/) (2023)
- [Mist: Towards Improved Adversarial Examples for Diffusion Models](https://mlsanity.com/paper/2305.12683/) (2023)
- [TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets](https://mlsanity.com/paper/2303.05762/) (2023)
- [Semantic Adversarial Examples](https://mlsanity.com/paper/1804.00499/) (2018)
- [Content-based Unrestricted Adversarial Attack](https://mlsanity.com/paper/2305.10665/) (2023)
- [Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability](https://mlsanity.com/paper/2305.16494/) (2023)
- [AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models](https://mlsanity.com/paper/2307.12499/) (2023)
- [Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models](https://mlsanity.com/paper/2404.10335/) (2024)
- [Semantic Adversarial Attacks via Diffusion Models](https://mlsanity.com/paper/2309.07398/) (2023)
- [VENOM: Text-driven Unrestricted Adversarial Example Generation with Diffusion Models](https://mlsanity.com/paper/2501.07922/) (2025)
- [ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack](https://mlsanity.com/paper/2408.05479/) (2024)
- [SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion](https://mlsanity.com/paper/2410.02240/) (2024)
- [Deceptive Diffusion: Generating Synthetic Adversarial Examples](https://mlsanity.com/paper/2406.19807/) (2024)

## Abstract (arXiv)

We introduce a new attack paradigm that embeds hidden adversarial capabilities directly into diffusion models via fine-tuning, without altering their observable behavior or requiring modifications during inference. Unlike prior approaches that target specific images or adjust the generation process to produce adversarial outputs, our method integrates adversarial functionality into the model itself. The resulting tampered model generates high-quality images indistinguishable from those of the original, yet these images cause misclassification in downstream classifiers at a high rate. The misclassification can be targeted to specific output classes. Users can employ this compromised model unaware of its embedded adversarial nature, as it functions identically to a standard diffusion model. We demonstrate the effectiveness and stealthiness of our approach, uncovering a covert attack vector that raises new security concerns. These findings expose a risk arising from the use of externally-supplied models and highlight the urgent need for robust model verification and defense mechanisms against hidden threats in generative models. The code is available at https://github.com/LucasBeerens/CRAFTed-Diffusion .