Yuvion LLM: An Adversarially-Aware Large Language Model for Content And AI Safety
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. However, existing general-purpose model development largely overlook this adversarial nature, and often remain insufficient for realistic safety scenarios involving planning, tool use, and multi-step reasoning, causing measured safety performance to overestimate real deployment robustness. To address this gap, we present Yuvion LLM, a large language model built for adversariall...
| Authors | Ting Ma, Xiufeng Huang, Benlei Cui et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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