LLM-Augmented Coordinated Inauthentic Behavior: A Technical Analysis of Emergent Reddit Manipulation Vectors James Jernigan | Independent Researcher | Digital Marketing Strategist Submitted to Reddit Security Team prior to publication Abstract In approximately twelve hours of conversational AI-assisted development, spread across a single day in early 2026, a functional multi-account Reddit manipulation framework was constructed by a researcher with no formal programming background. The system — built iteratively through natural language dialogue with Google Gemini Pro and the free tier of Claude — demonstrates that Large Language Model augmentation has fundamentally altered the threat surface for coordinated inauthentic behavior on social platforms. What previously required weeks of engineering work, specialized knowledge of platform APIs, and a team capable of writing and debugging production Python now requires neither code literacy nor technical experience. It requires curiosity, domain knowledge, and access to a free AI chatbot. This paper documents the system's architecture, evasion techniques, and detection surface — not as an operator's guide, but as a defender's briefing. The tool was never deployed against live users. No code is published here. The findings were submitted to Reddit's security team prior to this publication. What follows is an honest account of what is now possible, who can build it, and what platform trust and safety teams need to understand about the threat landscape they are navigating in 2026. The uncomfortable headline: the technical barrier to building LLM-augmented social manipulation infrastructure has not lowered. It has effectively disappeared. 1. Introduction and Motivation 1.1 The Evolving Threat Landscape Platform manipulation has a generational arc, and each generation has forced defenders to rebuild their detection frameworks almost from scratch. First-generation bots were blunt instruments. Scripted, deterministic, and fingerprinted by their own rigidity — identical posting intervals, copy-pasted payloads, obvious API call patterns. Detection was largely a pattern-matching exercise. If it walks like a bot and posts like a bot, ban the bot. Second-generation systems introduced behavioral mimicry. Timing jitter randomized posting intervals. Proxy rotation masked geographic clustering. Warmup protocols built account history before commercial activity began. The arms race shifted from "does this look like a bot" to "does this account's behavior deviate from statistical norms." Harder to detect, but still fundamentally deterministic — the same underlying logic producing variable surface behavior. Third-generation systems — the category this research documents — introduce something qualitatively different: generative content production at the payload layer. When an LLM is writing the actual replies, the linguistic fingerprints that stylometric analysis depends on disappear. Every post is unique. Every reply is contextually coherent. The system reads the thread it's replying to and produces a response that is genuinely responsive to that specific content. Hash-based detection fails. N-gram classifiers fail. The question "does this sound like a bot" becomes genuinely difficult to answer, because in the most meaningful sense, it doesn't. 1.2 How This Research Happened Full transparency is warranted here, because the methodology is itself a finding. I am not a security researcher by training or credential. I am a digital marketing strategist with seven years of experience in affiliate marketing, SEO, and social media platform dynamics. I've won affiliate competitions. I've had a course purchased on stage by Russell Brunson in front of 20,000 people. I've run training webinars for AI software communities. I know how platforms work from the outside — how content surfaces, how algorithms reward engagement, how communities form and can be influenced. I understand the frontend in considerable depth. What I cannot do is write code. My technical vocabulary begins and ends with basic HTML. <a href="url">link text</a> represents approximately the outer limit of my independent coding ability. In early 2026, I became interested in OpenClaw — an emerging AI skill-sharing platform — and noticed that SKILL.md files were gaining traction quickly. I thought it might be interesting to build and share a Reddit automation skill: something that could schedule posts and help users maintain a consistent presence. It seemed like a reasonable weekend project, and a good way to build visibility in the generative AI community. So I opened Google Gemini Pro and started asking questions. What began as a simple post scheduler evolved, feature by feature, through a process that I can only describe as collaborative improvisation. Gemini suggested capabilities I hadn't considered. I suggested behaviors I'd observed in commercial marketing tools. Each iteration revealed new possibilities. I kept saying, essen...