# BotSim: LLM-Powered Malicious Social Botnet Simulation

arXiv: https://arxiv.org/abs/2412.13420 · HTML: https://mlsanity.com/paper/2412.13420/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2024.

## Built on by (most cited first, 21 of 21)

- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations](https://mlsanity.com/paper/2510.25003/) (2025)
- [SoMe: A Realistic Benchmark for LLM-based Social Media Agents](https://mlsanity.com/paper/2512.14720/) (2025)
- [Chimera: Harnessing Multi-Agent LLMs for Automatic Insider Threat Simulation](https://mlsanity.com/paper/2508.07745/) (2025)
- [TRACE-Bot: Detecting Emerging LLM-Driven Social Bots via Implicit Semantic Representations and AIGC-Enhanced Behavioral Patterns](https://mlsanity.com/paper/2604.02147/) (2026)
- [Passing the Turing Test in Political Discourse: Fine-Tuning LLMs to Mimic Polarized Social Media Comments](https://mlsanity.com/paper/2506.14645/) (2025)
- [LLMs are Introvert](https://mlsanity.com/paper/2507.05638/) (2025)
- [Optimizing Privacy-Preserving Primitives to Support LLM-Scale Applications](https://mlsanity.com/paper/2509.25072/) (2025)
- [Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection](https://mlsanity.com/paper/2602.00318/) (2026)
- [Beyond Individual Mimicry: Constructing Human-Like Social network with Graph-Augmented LLM Agents](https://mlsanity.com/paper/2605.12512/) (2026)
- [Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News](https://mlsanity.com/paper/2605.28598/) (2026)
- [Execution and assessment of agentic influence operations in simulated social networks](https://mlsanity.com/paper/2605.28725/) (2026)
- [Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models](https://mlsanity.com/paper/2511.03699/) (2025)
- [Spatio-Temporal Graphs Beyond Grids: Benchmark for Maritime Anomaly Detection](https://mlsanity.com/paper/2512.20086/) (2025)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)
- [AgentRob: From Virtual Forum Agents to Hijacked Physical Robots](https://mlsanity.com/paper/2602.13591/) (2026)
- [Watermarking Game-Playing Agents in Perfect-Information Extensive-Form Games](https://mlsanity.com/paper/2605.14283/) (2026)
- [SAHG: Sector-Anisotropic Hyperbolic Graph Model for Social Bot Detection](https://mlsanity.com/paper/2605.30166/) (2026)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)
- [MEMORY Wins All: Indirect Bias Injection Attacks via Social Media Feeds](https://mlsanity.com/paper/2608.22061/) (2026)
- [CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era](https://mlsanity.com/paper/2609.23320/) (2026)

## Abstract (arXiv)

Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots with unprecedented intelligence. These bots adeptly simulate human profiles, conversations, and interactions, disseminating large amounts of false information and posing significant challenges to platform regulation. To better understand and counter these threats, we innovatively design BotSim, a malicious social botnet simulation powered by LLM. BotSim mimics the information dissemination patterns of real-world social networks, creating a virtual environment composed of intelligent agent bots and real human users. In the temporal simulation constructed by BotSim, these advanced agent bots autonomously engage in social interactions such as posting and commenting, effectively modeling scenarios of information flow and user interaction. Building on the BotSim framework, we construct a highly human-like, LLM-driven bot dataset called BotSim-24 and benchmark multiple bot detection strategies against it. The experimental results indicate that detection methods effective on traditional bot datasets perform worse on BotSim-24, highlighting the urgent need for new detection strategies to address the cybersecurity threats posed by these advanced bots.