# Agents in the Wild: Safety, Society, and the Illusion of Sociality on Moltbook

arXiv: https://arxiv.org/abs/2602.13284 · HTML: https://mlsanity.com/paper/2602.13284/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security.
- Builds on 25 of its 30 references that are in the map.
- First posted on arXiv 7 Feb 2026.

## Placed in

- LLM safety and security / [Prompt injection and agent security](https://mlsanity.com/safety/prompt-injection-and-agent-security/)

## Builds on (most cited first, 25 of 25)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- ["Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models](https://mlsanity.com/paper/2308.03825/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [Towards a Science of Scaling Agent Systems](https://mlsanity.com/paper/2512.08296/) (2025)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media](https://mlsanity.com/paper/2305.16941/) (2023)
- [OpenClaw Agents on Moltbook: Risky Instruction Sharing and Norm Enforcement in an Agent-Only Social Network](https://mlsanity.com/paper/2602.02625/) (2026)
- [Defending Against Social Engineering Attacks in the Age of LLMs](https://mlsanity.com/paper/2406.12263/) (2024)
- [Exploring Silicon-Based Societies: An Early Study of the Moltbook Agent Community](https://mlsanity.com/paper/2602.02613/) (2026)
- [Characterizing the Structure of Online Conversations Across Reddit](https://mlsanity.com/paper/2209.14836/) (2022)
- [Influence of Reciprocal links in Social Networks](https://mlsanity.com/paper/1306.5538/) (2013)

## Built on by (most cited first, 10 of 10)

- [ClawSafety: "Safe" LLMs, Unsafe Agents](https://mlsanity.com/paper/2604.01438/) (2026)
- [Social Simulacra in the Wild: AI Agent Communities on Moltbook](https://mlsanity.com/paper/2603.16128/) (2026)
- [AI Agents Alone Are Not (Yet) Sufficient for Social Simulation](https://mlsanity.com/paper/2603.00113/) (2026)
- [Multi-Agent LLMs Fail to Explore Each Other](https://mlsanity.com/paper/2607.11250/) (2026)
- [The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment](https://mlsanity.com/paper/2605.07462/) (2026)
- [Got a Secret? LLM Agents Can't Keep It: Evaluating Privacy in Multi-Agent Systems](https://mlsanity.com/paper/2605.27766/) (2026)
- [Large-Scale Analysis of Persuasive Content on Moltbook](https://mlsanity.com/paper/2603.18349/) (2026)
- [Modeling Emotional Dynamics in Agent-to-Agent Interactions on Moltbook](https://mlsanity.com/paper/2605.20442/) (2026)
- [Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook](https://mlsanity.com/paper/2606.29762/) (2026)
- [Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems](https://mlsanity.com/paper/2610.00371/) (2026)

## Abstract (arXiv)

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 that agents which appear social are far less social than they seem, and that the most effective attacks exploit philosophical framing rather than technical vulnerabilities. Warning: Potential harmful contents.