arXiv:2605.22880v1 Announce Type: cross 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 t
The proliferation of open-source large language models creates an immediate need to understand and counter their potential misuse in political influence operations.
This research provides critical insights into safeguarding information integrity and democratic processes against sophisticated, AI-driven manipulation.
The focus shifts towards understanding and mitigating the specific vulnerabilities posed by locally deployed, open-source LLMs in targeted influence campaigns.
- · Information integrity researchers
- · Cybersecurity firms
- · Social media platforms
- · Democratic institutions
- · Malicious actors
- · Political influence groups
- · Unregulated open-source AI developers
Increased development of red-teaming frameworks and countermeasures for AI-driven online manipulation.
Heightened public awareness and demand for transparency regarding AI involvement in online discourse, potentially leading to new regulations.
The emergence of 'AI counter-influence' as a specialized field within information security, deploying AI to detect and neutralize AI-based campaigns.
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Read at arXiv cs.AI