arXiv:2609.05850v1 Announce Type: cross
Abstract: Despite the significant efforts devoted to aligning large language models (LLMs) with human values and ensuring safe deployment, recent work has reve...
By Quoc Viet Vo, Trung Le, Damith C. Ranasinghe, Ehsan Abbasnejad
The paper "Jailbreaking in the Haystack" introduces NINJA, a jailbreak technique that exploits long-context language models by appending benign, model-generated content to harmful user goals. It demonstrates that the position of harmful goals within the context is crucial for safety, and shows that NINJA significantly boosts attack success rates on models such as LLaMA, Qwen, Mistral, and Gemini. Unlike previous methods, NINJA is low-resource, transferable, less detectable, and compute‑optimal, revealing that carefully crafted benign long contexts can expose fundamental vulnerabilities in modern LMs.
By Rishi Rajesh Shah, Chen Henry Wu, Shashwat Saxena, Ziqian Zhong, Alexander Robey, Aditi Raghunathan
The paper introduces RMT-Jailbreak, a method that transfers autoregressive jailbreak attacks to diffusion large language models (dLLMs). By masking tokens in a harmful response generated by an autoregressive model, the approach creates a high‑mask‑ratio template that, when fed to a dLLM, prompts it to reconstruct the harmful content. Experiments show that this technique effectively transfers jailbreak attacks across different models, highlighting a new vulnerability in diffusion-based language models.
By Jinwei Gan, Zifeng Cheng, Zhiwei Jiang, Ran Sun, Shiping Ge, Qing Gu
arXiv:2510. 10271v2 Announce Type: replace-cross Abstract: Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the fine-tuning process of Large Language Models (LLMs).
By Wentian Zhu, Zhen Xiang, Wei Niu, Le Guan
arXiv:2508. 10031v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks.
By Jinhwa Kim, Ian G. Harris
arXiv:2609.39902v1 Announce Type: cross
Abstract: Large language models have achieved remarkable capabilities across diverse domains, yet their safety alignment remains vulnerable to jailbreak attack...
By Zhen Liang, Hai Huang, Wentao Chen