# Large Language Models are Autonomous Cyber Defenders

arXiv: https://arxiv.org/abs/2505.04843 · HTML: https://mlsanity.com/paper/2505.04843/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Large Language Models as Analogical Reasoners](https://mlsanity.com/paper/2310.01714/) (2023)
- [CybORG: A Gym for the Development of Autonomous Cyber Agents](https://mlsanity.com/paper/2108.09118/) (2021)
- [Network Environment Design for Autonomous Cyberdefense](https://mlsanity.com/paper/2103.07583/) (2021)
- [Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies](https://mlsanity.com/paper/2211.15557/) (2022)
- [NASimEmu: Network Attack Simulator & Emulator for Training Agents Generalizing to Novel Scenarios](https://mlsanity.com/paper/2305.17246/) (2023)
- [Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games](https://mlsanity.com/paper/2403.17674/) (2024)
- [Out of the Cage: How Stochastic Parrots Win in Cyber Security Environments](https://mlsanity.com/paper/2308.12086/) (2023)
- [Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense](https://mlsanity.com/paper/2410.17351/) (2024)
- [Deep Reinforcement Learning for Autonomous Cyber Defence: A Survey](https://mlsanity.com/paper/2310.07745/) (2023)
- [Autonomous Intelligent Cyber-defense Agent: Introduction and Overview](https://mlsanity.com/paper/2304.12408/) (2023)

## Built on by (most cited first, 15 of 15)

- [Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination](https://mlsanity.com/paper/2508.05188/) (2025)
- [Tapas Are Free! Training-Free Adaptation of Programmatic Agents via LLM-Guided Program Synthesis in Dynamic Environments](https://mlsanity.com/paper/2508.11425/) (2025)
- [In-Context Autonomous Network Incident Response: An End-to-End Large Language Model Agent Approach](https://mlsanity.com/paper/2602.13156/) (2026)
- [RvB: Automating AI System Hardening via Iterative Red-Blue Games](https://mlsanity.com/paper/2601.19726/) (2026)
- [Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense](https://mlsanity.com/paper/2511.16483/) (2025)
- [Hallucination-Resistant Security Planning with a Large Language Model](https://mlsanity.com/paper/2602.05279/) (2026)
- [A Red Teaming Framework for Evaluating Robustness of AI-enabled Security Orchestration, Automation, and Response Systems](https://mlsanity.com/paper/2605.17075/) (2026)
- [Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning](https://mlsanity.com/paper/2608.02422/) (2026)
- [Trident : How to Break Deep Reinforcement Learning Cyber Defenses (Agentic)](https://mlsanity.com/paper/2608.04317/) (2026)
- [STAIR: Effective Incident Response Using an End-to-End Agentic Planning Framework](https://mlsanity.com/paper/2608.09524/) (2026)
- [Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response](https://mlsanity.com/paper/2606.13832/) (2026)
- [COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies](https://mlsanity.com/paper/2606.30479/) (2026)
- [BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense](https://mlsanity.com/paper/2609.11852/) (2026)
- [No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents](https://mlsanity.com/paper/2610.00557/) (2026)
- [Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution](https://mlsanity.com/paper/2610.00590/) (2026)

## Abstract (arXiv)

Fast and effective incident response is essential to prevent adversarial cyberattacks. Autonomous Cyber Defense (ACD) aims to automate incident response through Artificial Intelligence (AI) agents that plan and execute actions. Most ACD approaches focus on single-agent scenarios and leverage Reinforcement Learning (RL). However, ACD RL-trained agents depend on costly training, and their reasoning is not always explainable or transferable. Large Language Models (LLMs) can address these concerns by providing explainable actions in general security contexts. Researchers have explored LLM agents for ACD but have not evaluated them on multi-agent scenarios or interacting with other ACD agents. In this paper, we show the first study on how LLMs perform in multi-agent ACD environments by proposing a new integration to the CybORG CAGE 4 environment. We examine how ACD teams of LLM and RL agents can interact by proposing a novel communication protocol. Our results highlight the strengths and weaknesses of LLMs and RL and help us identify promising research directions to create, train, and deploy future teams of ACD agents.