As Russia's war against Ukraine extends into generative AI, large language models (LLMs) adapted for local post-Soviet languages are deployed in contested information environments. Policy and industry discourse assumes that culturally aligned adaptation encodes the political orientation of the target community: a Ukrainian-oriented model will resist Russian narratives, a Russian-oriented one will reinforce them. Does it? This article systematically disconfirms that assumption. We run a controlled audit of four openly available LLMs sharing a common base model but fine-tuned for different linguistic communities, querying them in Ukrainian, Russian and English across ten contested wartime narratives: Crimea, "denazification", the "one people" thesis, and atrocity denial at Bucha and Mariupol. The result is a Fine-Tuning Paradox: the Ukrainian-oriented model shows the weakest resistance to Russian disinformation in Russian, while the Russian-oriented one exhibits the strongest rejection. Corpus composition, language coverage and prompt format prove more decisive than nominal cultural provenance. We situate these findings within debates on hybrid warfare, digital sovereignty and post-imperial information orders, arguing that the principal threat to regional information sovereignty is not adversarial fine-tuning but the untested assumption that cultural alignment guarantees resilience.

翻译：随着俄罗斯对乌克兰的战争延伸至生成式人工智能领域，针对后苏联地区本地语言进行调整的大语言模型（LLMs）正被部署于充满争议的信息环境中。政策和行业话语认为，文化对齐的适配会编码目标社群的政治倾向：乌克兰导向的模型将抵制俄罗斯叙事，而俄罗斯导向的模型则会强化这些叙事。事实果真如此吗？本文系统性否定了这一假设。我们对四个共享相同基础模型但针对不同语言社区进行微调的开源大语言模型进行了受控审计，使用乌克兰语、俄语和英语对十个战时争议叙事（克里米亚、“去纳粹化”、“统一民族”论调以及布查与马里乌波尔暴行否认）进行查询。结果呈现"微调悖论"：乌克兰导向的模型在俄语环境下对俄罗斯虚假信息的抵制最弱，而俄罗斯导向的模型反而表现出最强的排斥反应。语料库构成、语言覆盖范围和提示格式比名义文化来源更具决定性。我们将这些发现置于混合战争、数字主权和后殖民信息秩序的辩论中，认为区域信息主权的主要威胁并非对抗性微调，而是文化对齐即能保证韧性的未经检验假设。