[2605.22880] How Far Will They Go? Red-Teaming Online Influence with Large Language Models
[Submitted on 20 May 2026]
Title:How Far Will They Go? Red-Teaming Online Influence with Large Language Models
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Abstract:As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their superior alignment with the operational constraints of privacy-conscious malicious actors deployed in social media environments. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin. We find systematic asymmetries in political expressivity: open-source LLMs are typically more willing to generate left-leaning social media content, OWs tend to contract inversely to model size, and regional differences are substantial despite uneven representation in the open-source ecosystem. Jailbreak potency also varies sharply across model families, motivating a workflow for identifying effective combinations of jailbreak techniques. Taken together, our results establish a practical framework for auditing the political steerability of open-source LLMs and for helping future researchers design stronger countermeasures against LLM-enabled influence campaigns.
Comments: 30 pages, 8 figures, submitted to COLM 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2605.22880 [cs.CL]
(or arXiv:2605.22880v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.22880
arXiv-issued DOI via DataCite
Submission history
From: Daniel Ruiz [view email] [v1] Wed, 20 May 2026 19:25:26 UTC (994 KB)
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