The paper introduces continuous diffusion language models (dLLMs) to replace the random mapping used in continuous-space black-box attacks on large language models. By sampling or optimizing directly in a learned generative latent space, dLLMs produce more natural prompts and significantly reduce character-level perplexity in both sampling-based (RareTrap) and optimization-based (ThinkTrap) evaluations. The study also shows that risk estimates can vary by an order of magnitude depending on whether a random mapping or a learned diffusion model is used.
arXiv:2610.07532v1 Announce Type: cross
Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks inc...
By Wonjun Lee, Kyungsik Yang, Gaeun Ji, Vaidehi Patil, Haon Park, Bumsub Ham, Mohit Bansal, Suhyun Kim
arXiv:2605. 19262v2 Announce Type: replace Abstract: Masked diffusion language models (MDLMs) are emerging as a compelling new paradigm for text generation, but their training-time security remains largely unexplored.
By Daniel Yiming Cao, Chengzhong Wang, Sheng-Yen Chou, Chengyu Huang, Pin-Yu Chen, Shengwei An
arXiv:2605. 01133v3 Announce Type: replace-cross Abstract: Large language model (LLM)-powered multi-agent systems (MAS) enable agents to communicate and share information, achieving strong performance on complex tasks.
By Lingxi Zhang, Guangtao Zheng, Hanjie Chen
The paper introduces Normal Diffusion Dynamics Learning (NDDL), a defense framework for text-to-image diffusion models that learns normal transition dynamics from benign samples. By modeling structured, timestep‑dependent patterns across cross‑attention, latent, and noise spaces, NDDL detects backdoor attacks through deviations between observed and predicted diffusion trajectories. It also localizes triggers without prior knowledge by substituting low‑semantic words, and experiments show its effectiveness across diverse attacks.
By Junjian Li, Xiaolong Liu, Peng Sun, Liantao Wu, Linghan Chen, Yudong Gao, Honglong Chen
The paper proposes a new unsupervised safety detection method for large language models that relies on anomaly detection rather than supervised training on unsafe data. By leveraging local sparsity in a linear representation space obtained via a sparse autoencoder, the authors develop a framework for locally masked SAE-based anomaly detection, providing theoretical support and empirical validation across multiple architectures and datasets. When calibrated with only 1% out-of-distribution data, the method achieves near‑optimal performance while using just 1–2% of SAE neurons for computation.
By Xin Chen, Gil Kur, Alexander Shevchenko, Andreas Krause