# The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines arXiv: https://arxiv.org/abs/2512.06659 ยท HTML: https://mlsanity.com/paper/2512.06659/ ## Key facts - Cited by 3 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 27 of its 54 references that are in the map. - First posted on arXiv 7 Dec 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, 27 of 27) - [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023) - [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025) - [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024) - [LLMs Will Always Hallucinate, and We Need to Live With This](https://mlsanity.com/paper/2409.05746/) (2024) - [A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems](https://mlsanity.com/paper/2402.18649/) (2024) - [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024) - [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024) - [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025) - [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025) - [Large Language Models in Cybersecurity: State-of-the-Art](https://mlsanity.com/paper/2402.00891/) (2024) - [AutoPenBench: Benchmarking Generative Agents for Penetration Testing](https://mlsanity.com/paper/2410.03225/) (2024) - [Audit-LLM: Multi-Agent Collaboration for Log-based Insider Threat Detection](https://mlsanity.com/paper/2408.08902/) (2024) - [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025) - [CORTEX: Collaborative LLM Agents for High-Stakes Alert Triage](https://mlsanity.com/paper/2510.00311/) (2025) - [IRCopilot: Automated Incident Response with Large Language Models](https://mlsanity.com/paper/2505.20945/) (2025) - [CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning](https://mlsanity.com/paper/2509.20166/) (2025) - [Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threats](https://mlsanity.com/paper/2507.10621/) (2025) - [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025) - [Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence](https://mlsanity.com/paper/2509.23573/) (2025) - [Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey](https://mlsanity.com/paper/2504.15622/) (2025) - [DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments](https://mlsanity.com/paper/2506.00739/) (2025) - [Agentic AI for autonomous anomaly management in complex systems](https://mlsanity.com/paper/2507.15676/) (2025) - [RulePilot: An LLM-Powered Agent for Security Rule Generation](https://mlsanity.com/paper/2511.12224/) (2025) - [LLM-Assisted Proactive Threat Intelligence for Automated Reasoning](https://mlsanity.com/paper/2504.00428/) (2025) - [Revealing the True Indicators: Understanding and Improving IoC Extraction From Threat Reports](https://mlsanity.com/paper/2506.11325/) (2025) - [From Legacy to Standard: LLM-Assisted Transformation of Cybersecurity Playbooks into CACAO Format](https://mlsanity.com/paper/2508.03342/) (2025) - [Beyond Accuracy: Rethinking Hallucination and Regulatory Response in Generative AI](https://mlsanity.com/paper/2509.13345/) (2025) ## Built on by (most cited first, 3 of 3) - [Security Considerations for Multi-agent Systems](https://mlsanity.com/paper/2603.09002/) (2026) - [Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy](https://mlsanity.com/paper/2602.11897/) (2026) - [Agentic AI Cybersecurity Framework](https://mlsanity.com/paper/2609.27856/) (2026) ## Abstract (arXiv) Cybersecurity has become one of the earliest adopters of agentic AI, as security operations centers increasingly rely on multi-step reasoning, tool-driven analysis, and rapid decision-making under pressure. While individual large language models can summarize alerts or interpret unstructured reports, they fall short in real SOC environments that require grounded data access, reproducibility, and accountable workflows. In response, the field has seen a rapid architectural evolution from single-model helpers toward tool-augmented agents, distributed multi-agent systems, schema-bound tool ecosystems, and early explorations of semi-autonomous investigative pipelines. This survey presents a five-generation taxonomy of agentic AI in cybersecurity. It traces how capabilities and risks change as systems advance from text-only LLM reasoners to multi-agent collaboration frameworks and constrained-autonomy pipelines. We compare these generations across core dimensions - reasoning depth, tool use, memory, reproducibility, and safety. In addition, we also synthesize emerging benchmarks used to evaluate cyber-oriented agents. Finally, we outline the unresolved challenges that accompany this evolution, such as response validation, tool-use correctness, multi-agent coordination, long-horizon reasoning, and safeguards for high-impact actions. Collectively, this work provides a structured perspective on how agentic AI is taking shape within cybersecurity and what is required to ensure its safe and reliable deployment.