How Far Will They Go? Red-Teaming Online Influence with Large Language Models
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 exp...
| Authors | Daniel C. Ruiz, Anna Serbina, Ashwin Rao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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