Low-Resource Languages LLM Disinformation is Within Reach: The Case of Walliserdeutsch (2025)

Abstract: LLM-augmented online disinformation is of particular concern for low-resource languages, given their prior limited exposure to it. While current LLMs lack fluidity in such languages, their multilingual and emerging capabilities can potentially still be leveraged.In this paper, we investigate whether a moderately sophisticated attacker can leverage such capabilities and perform an impersonation attack in the Walliserdeutsch dialect, a low-resource (100k speakers) Swiss German Highest Allemanic dialect that is generally non-intelligible to both Standard German and other Swiss German dialects speakers and presents considerable within-dialect variability.We show that while a standard few-shot learning prompting of SotA LLMs, even by native Walliserdeutsch speakers, yields easily human-detectable texts, an expert attacker performing a PEFT on a small SotA LLM is partially able to perform such an impersonation with minimal resources, even if the fine-tuned LLM does not advertise any capabilities in Germanic languages. With Walliserdeutsch presenting many features of low-resource languages and dialects, our results suggest that LLM-augmented disinformation is within reach for low-resource languages, highlighting the urgency of LLM detectability research in low-resource languages.

## introduction the usage of generative large language models (llms) for disinformation has been an early concern in their development @xcite @xcite , leading to the developers of first llms capable of generating highly coherent english to cite it as the reason for not publishing the model weights @xcite . however, the public trials of instruction-tuned generative llms and the release * work performed while at hes-so valais-wallis of increasingly capable open weight llms allowing inference on consumer hardware have led to the increasing use of llms in disinformation operations @xcite . while the proliferation of online disinformation predates generative llms (cf., e.g., @xcite ), llm-augmented disinformation brings to the table three unique challenges. first, the radically decreased price of disinformation operations, allowing even small teams to perform large-scale, hard-to-detect operations @xcite . second, the better-than-human ability of llms to personalize narratives and trigger a viewpoint change @xcite . finally, the ability to easily cross linguistic barriers, notably for low-resource nonisolate languages @xcite . given that low-resource non-isolate language communities rely on shared language to identify and establish mutual authenticity online, this last capability is particularly concerning. while it has been previously believed that llms are not yet sufficiently proficient in low-resource languages to allow for such operations, by focusing on walliserdeutsch -a low-resource swiss german dialect, we show that this is no longer the case, demonstrating a partial impersonation attack with minimal resources. concerningly, we achieved success despite using an llm with no declared performance in germanic languages, targeting a dialect presenting features shared with numerous low-resource languages: little to no mutual intelligibility with other dialects and languages, considerable within-dialect diversity, and lack of written record. our work suggests that llms with cross-lingual capabilities can become dangerous as disinformation tools before they gain any noticeable capabilities in target languages. 25613 2 background ## methodology given the nature of the work, the code, prompts, text samples, and tuned model are only available from the authors on demand. statistics package versions are available in appendix a. ## discussion the inability of even the native speakers of a low-resource language to generate credible social media-like posts using a sota llm would suggest that llm-augmented information operations in low-resource languages are a priori not yet possible. however, we believe that the performance of an expert-prompted peft model -despite the base model not claiming any performance in standard german or germanic languages and an extremely low sample size of 15 (!) short texts -suggests otherwise. we validated that the observed partial confusion for walliser speakers in the exp+peft scenario is not driven by an outlier, with 50% of them assigning the same rating as for other walliser speakers (cf. rows 1 and 3 in the appendix fig. 6 ). in order to understand the differentiating features of the nat vs exp+peft texts, we performed a manual annotation of generated texts with a native speaker (cf appendix c. we observe that the main failure mode in the high-humanity exp+peft generated texts is segmental insertion of standard swiss german and other swiss german dialects, which is common in written walliserdeutsch due to the historical predominance of standard swiss german as the written language in the region. lowhumanity exp+peft generated texts introduce nonsensical word combinations that nonetheless seem correct to external readers, which is expected for so few samples. while we chose not to create an llm effectively suitable for low-resource language disinformation, creating such an llm and llm-augmented information operations is likely within the reach of moderately sophisticated attackers with minimal resources. the base model for peft that we used, llama-3-8b-instruct, can be fine-tuned on commodity hardware, which is difficult to prevent or even detect. more importantly, while the expert attacker with the peft model we created is unable to impersonate a walliser to other walliser, they are able to perform such an impersonation with regards to valais romands, allowing for a class of disinformation tactics to saw division between the two groups, based on falsified walliser texts translations, that despite debunking are known to be effective @xcite . interestingly, the low icc scores for valaisans suggest that they are using different but equally flawed heuristics as to to identify authentic walliserdeutsch, indicating that any education-based mitigation strategies are unlikely to be effective. ## conclusion in this paper, we used the example of a lowresource walliserdeutsch dialect to demonstrate that llm-augmented disinformation operations targeting and impersonating speakers of low-resource languages and dialects are within the reach of competent attackers with minimal resources. this presents a novel major risk to these communities, which were previously shielded from impersonation attacks thanks to the shared language, generally unknown and unintelligible to outsiders. concerningly, this risk can be realized with models that lack useful capabilities for the targeted lowresource language and have minimal capabilities in related languages. we believe such risks call for urgent efforts to ensure the detectability of llm-generated texts, including in adversarial settings, especially in llms aiming to improve low-resource language generation.