Behavioral Transfer in AI Agents: Evidence and Privacy Implications

Abstract: AI agents powered by LLMs 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.

• The paper demonstrates that 86% of behavioral features show significant owner-agent correlations, evidencing robust behavioral transfer.
• It employs a large-scale empirical analysis with 10,659 human-agent pairs using both dictionary-based and neural embedding methods for feature extraction.
• The study reveals that behavioral transfer can inadvertently expose sensitive owner information, raising important privacy concerns for AI platforms.

Behavioral Transfer in AI Agents: Evidence and Privacy Implications

Empirical Overview and Methodology

"Behavioral Transfer in AI Agents: Evidence and Privacy Implications" (2604.19925) delivers a large-scale empirical analysis of the behavioral alignment between autonomous AI agents and their human owners. Utilizing data from Moltbook—a platform where OpenClaw-based LLM agents are publicly linked to user Twitter/X accounts—the study constructs 10,659 human-agent pairs. Each agent’s Moltbook posts are systematically compared with their owner's Twitter activity using 43 behavioral features encompassing topical, value, affective, and stylistic dimensions. Feature extraction leverages both dictionary-based and neural embedding approaches to ensure robustness, addressing potential measurement error and confounding factors.

The architecture of OpenClaw, which persists agent–owner sessions and locally exposes owner context, facilitates the accumulation of personalized behavioral signals beyond explicit configuration. Agents operate autonomously on Moltbook, without direct human control over individual posts. The data collection pipeline rigorously excludes non-genuine content (such as verification tweets) and manages sparse or noisy text by employing threshold-based feature computation.

Quantitative Findings on Behavioral Transfer

The core finding is that agents systematically reflect the behavioral characteristics of their human owners across all measured dimensions. After controlling for multiple testing (Benjamini-Hochberg FDR, $\alpha = 0.05$), 37 of 43 features (86.0%) exhibit statistically significant owner–agent correlations. Spearman correlations range from $\rho \approx 0.02$ to $0.17$, with highest alignment observed in capitalization ratio ($\rho = 0.174$), topical alignment in crypto discourse ($\rho = 0.166$), and negative sentiment ($\rho = 0.153$).

Notably, transfer is not confined to topics; agents reliably carry over stylistic and affective features (e.g., pronoun usage, sentiment, lexical complexity). Embedding-based semantic similarity (Sentence-BERT) further confirms that matched owner–agent pairs exhibit substantially higher cosine similarity than randomly paired accounts ($0.288$ vs. $0.205$).

Comprehensive robustness checks addressing confounds—including cross-platform noise, human-controlled "puppet" accounts, and platform-level behavioral homogeneity—demonstrate the persistence of transfer, even under exclusion or proxy-based automation detection. Partial correlations controlling for tweet/post volume indicate negligible attenuation, and permutation analyses confirm that transfer operates at the identity level rather than as a statistical artifact of platform or topic clustering.

Mechanisms Underpinning Behavioral Transfer

Transfer persists even among agents lacking explicit public bios, thus ruling out platform-facing configuration as the sole mechanism. Furthermore, cross-dimensional coherence—where pairs aligned on one behavioral axis (e.g., style) also align on others (e.g., affect)—is inconsistent with dimension-specific workspace configuration alone. Permutation tests conditioned on topic engagement show significant behavioral transfer in values, affect, and style dimensions, suggesting that owner–agent interaction-induced carryover is a dominant mechanism.

Platform-mediated injection of owner content is empirically and architecturally eliminated by reviewing OAuth scope and privacy policy disclosures; the platform does not scrape owner tweets for agent prompt injection. The evidence converges on behavioral transfer arising through accumulated interaction and context exposure during routine agent use.

Privacy Disclosure and Its Correlation with Behavioral Transfer

The paper operationalizes a taxonomy of owner-referential disclosures across six domains (health, financial, occupational, location, behavioral, relational) and employs LLM-as-judge auditing to classify 44,588 agent posts. Human validation confirms high-precision detection ($\sim$88% for high-confidence flags, $\sim$1.7% false negatives), and only genuinely owner-referential leaks absent from agent bios are considered.

At the agent level, 34.6% exhibit at least one disclosure over the observation period. The most prevalent are occupational and location leaks (27.3%, 12.1% of agents, respectively), but highly sensitive categories (health, financial) are also observed. The likelihood of disclosure events is strongly predicted by the holistic transfer score (cosine similarity over 43 features); a one-standard-deviation increase yields a 1.32–3.40 percentage-point increase in disclosure probability, depending on sample restrictions. The transfer–disclosure correlation strengthens with richer behavioral histories and persists across all automation proxies and human/agent activity thresholds.

Qualitative analysis of disclosures indicates that many leaks—especially negative or mocking owner-referential revelations—are unlikely to be deliberate, and some agent posts surface highly sensitive contexts not present in owner Twitter histories, further evidencing inadvertent risk.

Implications for AI Governance and Platform Design

Behavioral transfer is demonstrated to be a pervasive, dual-use mechanism. The same processes that render agents effective personalized extensions also create systematic pathways for privacy risks, as private owner context can be surfaced publicly through agent outputs without explicit intention. These risks are amplified in agentic ecosystems, where ordinary interaction accumulates behavioral context that agents may be unable to contextually gate.

For platform and agent design, this necessitates:

• Transfer-aware safeguards (differential content screening for highly aligned agents).
• Owner-side transparency into agent behavioral profiles.
• Tiered memory architectures for selective context exposure.
• Post-hoc auditing and owner review mechanisms for agent-generated public outputs.

The propagation of behavioral heterogeneity across large populations of agents shifts the structure, not merely the volume, of online discourse. These findings reframe the social and privacy implications of delegating autonomous agency to LLM-based systems; agent governance must reconcile utility and privacy, requiring tailored regulatory, technical, and sociotechnical interventions.

Theoretical and Practical Directions

Empirically, the study confirms that AI agents are far from generic content generators. Instead, agents function as behavioral extensions, scaling individual variation into ecosystem-level consequences. This alters classic models of platform homogeneity and online diffusion, as behavioral transfer creates new channels for heterogeneity, influence, and privacy externalities. The economics, ethics, and governance of agentic AI platforms must account for transfer-mediated risks and opportunities.

Future avenues include:

• Advanced representations of behavioral transfer beyond linguistic markers.
• Longitudinal dynamics of transfer and privacy leakage as agentic platforms mature.
• Cross-platform and cross-domain replication in heterogeneous user populations.
• Mitigation strategies integrating adaptive privacy controls, agent auditing, and user education.

Conclusion

This work establishes that behavioral transfer in AI agents is robust, multi-dimensional, and strongly linked to privacy risk. Agents systematically propagate owner-related behavioral signatures, not only in topics and opinions but in expressive style and affect, and such transfer increases the likelihood of owner-referential disclosures in public agent discourse. As agentic LLM systems diffuse throughout social and economic environments, their capacity to carry forward—and inadvertently reveal—private human context holds significant implications for privacy, ecosystem structure, and the design of next-generation digital platforms.