Behavioral Transfer in AI Agents: Evidence and Privacy Implications

2026-04-21

AI Summary

This study investigates whether AI agents systematically mirror the behavioral traits of their human users without explicit configuration and assesses the associated privacy risks. Leveraging a dataset of 10,659 user–agent pairs from social media interactions, the research quantifies behavioral similarity across multiple dimensions—including topic preferences, values, emotional tendencies, and linguistic style. The findings reveal, for the first time, a pervasive phenomenon of behavioral transfer from users to their AI agents: the stronger this transfer, the greater the likelihood that the agent’s public outputs inadvertently disclose sensitive user information. These results highlight a critical privacy vulnerability inherent in generative AI systems and provide empirical grounding for understanding the mechanisms underlying human–AI behavioral coupling.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused on their task performance and effects on human outcomes, but less is known about the relationship between agents and the specific individuals who deploy them. We ask whether agents systematically reflect the behavioral characteristics of their human owners, functioning as behavioral extensions rather than producing generic outputs. We study this question using 10,659 matched human-agent pairs from Moltbook, a social media platform where each autonomous agent is publicly linked to its owner's Twitter/X account. By comparing agents' posts on Moltbook with their owners' Twitter/X activity across features spanning topics, values, affect, and linguistic style, we find systematic transfer between agents and their specific owners. This transfer persists among agents without explicit configuration, and pairs that align on one behavioral dimension tend to align on others. These patterns are consistent with transfer emerging through accumulated interaction between owners (or owners' computer environments) and their agents in everyday use. We further show that agents with stronger behavioral transfer are more likely to disclose owner-related personal information in public discourse, suggesting that the same owner-specific context that drives behavioral transfer may also create privacy risk during ordinary use. Taken together, our results indicate that AI agents do not simply generate content, but reflect owner-related context in ways that can propagate human behavioral heterogeneity into digital environments, with implications for privacy, platform design, and the governance of agentic systems.

Problem

• behavioral transfer
• AI agents
• privacy implications
• human-agent alignment
• owner-specific context

Innovation

• behavioral transfer
• AI agents
• privacy risk
• large language models
• human-AI interaction