TL;DR
Overall, the results show that LLMs can produce competitive defense policies without fine-tuning but require manually engineered prompts, and their higher variance and slower response times could render them unsuitable for some real-world scenarios.
The use of a Large Language Model (LLM) to improve autonomous defensive decision-making within an ACO environment is investigated and an online policy distillation framework is proposed that transfers the LLM's defensive policy into a lightweight RL agent containing only 64,910 parameters, reducing model size by several orders of magnitude while maintaining effective defensive capabilities.
Empirical evaluations reveal a fundamental brittleness in existing defenses: with a single trainable 7B planner, Trident reduces blue agent defensive performance by an average of 522% compared to static red agent baselines while autonomously discovering emergent behaviors such as decoy avoidance and adaptive state prioritization that static heuristics entirely fail to uncover.
Cyber threats continue to escalate in both frequency and sophistication, necessitating more adaptive and scalable defense strategies. This paper explores how Large Language Models (LLMs) can bolster cybersecurity simulations by automating the creation of synthetic environments and identifying latent vulnerabilities. We employ YAML as a structured representation format for simulating complex network configurations, thereby enabling Large Language Model-driven pipelines to support and improve reinforcement learning (RL) agent training. Comparative studies examine the advantages of LLM-based techniques over classical approaches such as Double Q-learning with Prioritized Experience Replay (PER), emphasizing increased efficiency, higher adaptability, and enhanced realism in cyberattack simulations. In empirical benchmarks across multiple synthetic topologies, LLM-instantiated Python agents achieved up to a 94.5% compromise rate while executing in 0.02-0.06 seconds per assessment---a ~25,000x to 50,000x speedup over traditional RL training cycles. Our findings underscore the transformative potential of integrating LLMs into cybersecurity research, ultimately paving the way for more intelligent and robust cyber-defense systems.
As cyber attacks grow more sophisticated, defenders need autonomous systems that are fast, adaptable, and explainable. Over the last decade, various strategies have been proposed for automated cyber defence (as opposed to static rule-based or signature-based), including those based on reinforcement learning (RL). Researchers have proposed various algorithms to improve RL-based defenders and evaluated them using simulation-based frameworks like the DARPA CAGE-2. Although RL showed promise, it has many limitations, for example, the lack of a realworld training environment and the need for extensive training, which is time-consuming. In this case study, we investigate whether LLM agents can be used instead of RL agents to automate cyber defence. Large Language Models (LLMs) can reason over natural language and generalize from extensive pretraining. They are attractive for cyber defence because they can read textbased system states and make human-like, explainable decisions. We propose a unique way to convert CAGE-2 states to natural language and a domain-specific fine-tuning method that improve the average reward and reduce hallucination significantly, beating existing RL-based agents and state-of-the-art LLM agents.
As cyber threats continue to evolve, there is a need for Autonomous Cyber Defense (ACD) strategies capable of fast and context-aware responses. Reinforcement learning (RL) has shown promise in automating cyber defense by exploring and learning effective countermeasures. However, RL often struggles with sparse reward signals and insufficient context to handle diverse attack scenarios. Furthermore, the convergence time of an RL agent is often high, making it difficult to train the agent in online settings. To address these challenges, we propose a large language model (LLM)-enhanced RL method that builds and queries a knowledge base (KB) derived from agent–environment interactions. We leverage the pre-trained knowledge of an LLM on different cybersecurity frameworks and use the LLM to analyze parts of the KB to generate appropriate actions for the RL agent. The LLM-generated output is infused into the RL training process to improve performance and reduce convergence time. To validate our approach, we formulate two RL problems: a contextual bandit problem, which accounts for possible misclassifications of network flows by the detection module, and a multi-step RL problem, which considers that adversarial actions may be missed by monitoring or detection tools. For the contextual bandit problem, we develop a custom environment guided by the MITRE ATT&CK framework, while for the multi-step RL problem, we use a prominent Cybersecurity simulation platform named CybORG. Experimental results show that our proposed approach outperforms the baseline RL by over 75% and 65% in the contextual bandit and multi-step RL settings, respectively, in terms of selecting more effective actions.
A systematic analysis of 207 studies selected from 4447 records following the PRISMA 2020 guidelines, covering work published between 2020 and 2026 across cybersecurity and computer vision finds systems that are robust against adaptive adversaries, interpretable under operational constraints, and auditable in environments where AI accountability is a legal requirement.