# GCG Attack On A Diffusion LLM

arXiv: https://arxiv.org/abs/2601.14266 · HTML: https://mlsanity.com/paper/2601.14266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models.
- Builds on 3 of its 3 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Placed in

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

## Builds on (most cited first, 3 of 3)

- [Large Language Diffusion Models](https://mlsanity.com/paper/2502.09992/) (2025)
- [Diffusion LLMs are Natural Adversaries for any LLM](https://mlsanity.com/paper/2511.00203/) (2025)
- [Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry](https://mlsanity.com/paper/2505.22839/) (2025)

## Built on by (most cited first, 1 of 1)

- [MaskForge: Structure-Aware Adaptive Attacks for Jailbreaking Diffusion Large Language Models](https://mlsanity.com/paper/2606.04027/) (2026)

## Abstract (arXiv)

While most LLMs are autoregressive, diffusion-based LLMs have recently emerged as an alternative method for generation. Greedy Coordinate Gradient (GCG) attacks have proven effective against autoregressive models, but their applicability to diffusion language models remains largely unexplored. In this work, we present an exploratory study of GCG-style adversarial prompt attacks on LLaDA (Large Language Diffusion with mAsking), an open-source diffusion LLM. We evaluate multiple attack variants, including prefix perturbations and suffix-based adversarial generation, on harmful prompts drawn from the AdvBench dataset. Our study provides initial insights into the robustness and attack surface of diffusion language models and motivates the development of alternative optimization and evaluation strategies for adversarial analysis in this setting.