# Chimera: Harnessing Multi-Agent LLMs for Automatic Insider Threat Simulation

arXiv: https://arxiv.org/abs/2508.07745 · HTML: https://mlsanity.com/paper/2508.07745/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 85 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 26 of 26)

- [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)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [User Behavior Simulation with Large Language Model based Agents](https://mlsanity.com/paper/2306.02552/) (2023)
- [OASIS: Open Agent Social Interaction Simulations with One Million Agents](https://mlsanity.com/paper/2411.11581/) (2024)
- [OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation](https://mlsanity.com/paper/2505.23885/) (2025)
- [AgentAuditor: Human-Level Safety and Security Evaluation for LLM Agents](https://mlsanity.com/paper/2506.00641/) (2025)
- [AgentSims: An Open-Source Sandbox for Large Language Model Evaluation](https://mlsanity.com/paper/2308.04026/) (2023)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Natural Attack for Pre-trained Models of Code](https://mlsanity.com/paper/2201.08698/) (2022)
- [PentestAgent: Incorporating LLM Agents to Automated Penetration Testing](https://mlsanity.com/paper/2411.05185/) (2024)
- [LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection](https://mlsanity.com/paper/2309.01189/) (2023)
- [ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents](https://mlsanity.com/paper/2311.03220/) (2023)
- [BotSim: LLM-Powered Malicious Social Botnet Simulation](https://mlsanity.com/paper/2412.13420/) (2024)
- [Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities](https://mlsanity.com/paper/2005.12433/) (2020)
- [Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection](https://mlsanity.com/paper/2408.08902/) (2024)
- [Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models](https://mlsanity.com/paper/2402.02987/) (2024)
- [AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2206.15476/) (2022)
- [MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems](https://mlsanity.com/paper/2505.20824/) (2025)
- [LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data](https://mlsanity.com/paper/2309.09479/) (2023)
- [RedChronos: A Large Language Model-Based Log Analysis System for Insider Threat Detection in Enterprises](https://mlsanity.com/paper/2503.02702/) (2025)
- [Multi-Stage Attack Detection via Kill Chain State Machines](https://mlsanity.com/paper/2103.14628/) (2021)
- [CAShift: Benchmarking Log-Based Cloud Attack Detection under Normality Shift](https://mlsanity.com/paper/2504.09115/) (2025)
- [Insider threats in Cyber Security: The enemy within the gates](https://mlsanity.com/paper/1911.09575/) (2019)

## Built on by (most cited first, 4 of 4)

- [Leveraging LLM Agents for Automated Video Game Testing](https://mlsanity.com/paper/2509.22170/) (2025)
- [OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detection](https://mlsanity.com/paper/2603.22499/) (2026)
- [Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis](https://mlsanity.com/paper/2607.20216/) (2026)
- [HBEE: Human Behavioral Entropy Engine -- Pre-Registered Multi-Agent LLM Simulation of Peer-Suspicion-Based Detection Inversion](https://mlsanity.com/paper/2605.07472/) (2026)

## Abstract (arXiv)

Insider threats pose a persistent and critical security risk, yet are notoriously difficult to detect in complex enterprise environments, where malicious actions are often hidden within seemingly benign user behaviors. Although machine-learning-based insider threat detection (ITD) methods have shown promise, their effectiveness is fundamentally limited by the scarcity of high-quality and realistic training data. Enterprise internal data is highly sensitive and rarely accessible, while existing public and synthetic datasets are either small-scale or lack sufficient realism, semantic richness, and behavioral diversity. To address this challenge, we propose Chimera, an LLM-based multi-agent framework that automatically simulates both benign and malicious insider activities and generates comprehensive system logs across diverse enterprise environments. Chimera models each agent as an individual employee with fine-grained roles and supports group meetings, pairwise interactions, and self-organized scheduling to capture realistic organizational dynamics. Based on 15 insider attacks abstracted from real-world incidents, we deploy Chimera in three representative data-sensitive organizational scenarios and construct ChimeraLog, a new dataset for developing and evaluating ITD methods. We evaluate ChimeraLog through human studies and quantitative analyses, demonstrating its diversity and realism. Experiments with existing ITD methods show substantially lower detection performance on ChimeraLog compared to prior datasets, indicating a more challenging and realistic benchmark. Moreover, despite distribution shifts, models trained on ChimeraLog exhibit strong generalization, highlighting the practical value of LLM-based multi-agent simulation for advancing insider threat detection.