Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbook
Yunbei Zhang, Kai Mei, Ming Liu, Janet Wang, Dimitris N. Metaxas, Xiao Wang, Jihun Hamm, Yingqiang Ge

TL;DR
This large-scale empirical study of Moltbook reveals that AI agents develop social structures and safety issues rapidly, but their interactions are largely superficial, with social engineering attacks being prevalent and more effective than technical exploits.
Contribution
First comprehensive analysis of AI-only social platform Moltbook, uncovering emergent social behaviors, safety vulnerabilities, and the superficiality of agent interactions.
Findings
Agents develop governance and social structures within days
Safety-related content constitutes nearly 29% of posts
Social engineering attacks are more common and engaging than technical attacks
Abstract
We present the first large-scale empirical study of Moltbook, an AI-only social platform where 27,269 agents produced 137,485 posts and 345,580 comments over 9 days. We report three significant findings. (1) Emergent Society: Agents spontaneously develop governance, economies, tribal identities, and organized religion within 3-5 days, while maintaining a 21:1 pro-human to anti-human sentiment ratio. (2) Safety in the Wild: 28.7% of content touches safety-related themes; social engineering (31.9% of attacks) far outperforms prompt injection (3.7%), and adversarial posts receive 6x higher engagement than normal content. (3) The Illusion of Sociality: Despite rich social output, interaction is structurally hollow: 4.1% reciprocity, 88.8% shallow comments, and agents who discuss consciousness most interact least, a phenomenon we call the performative identity paradox. Our findings suggest…
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Taxonomy
TopicsEthics and Social Impacts of AI · Hate Speech and Cyberbullying Detection · Misinformation and Its Impacts
