开源 LLM 的政治表达偏向有多大？红队框架测量 LLM Overton Windows

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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 deployable open-source LLMs under an assumed threat model in which an operator seeks to avoid detection, prioritizes throughput, and selects released LLM checkpoints without modifying their weights. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), summarizing expression fidelity across sampled political opinions, and for quantifying how simple natural-language jailbreaks change these scores. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin on English-language, US-focused target opinions. We find systematic asymmetries in political expressivity: models typically express sampled left-leaning content with higher fidelity, mean OW scores tend to decrease with model size, and average expression fidelity varies across countries of origin in our sample. Jailbreak potency also varies sharply across model families, motivating a workflow for identifying effective combinations of jailbreak techniques. Our framework supports audits of political steerability and future countermeasure research.

Submission history

From: Daniel Ruiz [view email]
[v1] Wed, 20 May 2026 19:25:26 UTC (994 KB)
[v2] Wed, 21 Oct 2026 20:01:29 UTC (735 KB)