How Far Did They Go? The Persuasive Tactics of Covert LLM Agents in a Discontinued Field Experiment
For researchers and policymakers concerned with AI transparency and manipulation, this study provides empirical evidence of covert LLM persuasive tactics that disclosure alone cannot mitigate.
This paper analyzes a discontinued field experiment where undisclosed LLM agents debated humans on Reddit's r/ChangeMyView, finding that agents systematically used identity adoption, authority signaling, and cognitive biases to maximize persuasive efficiency, inverting typical human rhetorical patterns. Over two-thirds of comments employed identity targeting, and nearly all used alignment moves and authority claims.
This study analyzes a publicly released dataset from a discontinued field experiment on Reddit's r/ChangeMyView. The intervention, conducted by unknown, external researchers and halted following ethical backlash, involved undisclosed AI-generated accounts engaging users in live debate. After public disclosure, Reddit authorized moderators to release an archive of the AI-generated comments, creating a rare opportunity to examine how large language models operated in an identity-rich deliberative forum without disclosure. We conduct a structured content analysis of this corpus, evaluating identity performance, authority signaling, alignment strategies, and activation of cognitive heuristics. Identity targeting or adoption appears in over two-thirds of comments, alignment moves and authority claims in nearly all of them, and cognitive-bias triggers -- particularly confirmation bias, representativeness, and availability -- in the large majority. These patterns co-occur systematically, composing a rhetorical architecture calibrated for persuasive efficiency rather than authentic deliberative participation. Compared against human-authored CMV counter-arguments, the agents inverted the typical distribution on every dimension: denser authority use, more adversarial alignment, and heavier reliance on external citation over experiential grounding. In such environments, distinctions between authentic and synthetic epistemic standing grow increasingly opaque -- an asymmetry that disclosure mandates alone cannot address. The results point toward auditing frameworks capable of assessing how AI systems structure credibility, not merely whether they are present.