# BlueSTAR: Tiered Agentic Architecture for Autonomous Cyber Defense

arXiv: https://arxiv.org/abs/2609.11852 · HTML: https://mlsanity.com/paper/2609.11852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 53 references that are in the map.
- First posted on arXiv 10 Sep 2026.

## 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, 22 of 22)

- [Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models](https://mlsanity.com/paper/2408.08926/) (2024)
- [LLM Agents can Autonomously Exploit One-day Vulnerabilities](https://mlsanity.com/paper/2404.08144/) (2024)
- [LLM Agents can Autonomously Hack Websites](https://mlsanity.com/paper/2402.06664/) (2024)
- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [Large Language Models for Cyber Security: A Systematic Literature Review](https://mlsanity.com/paper/2405.04760/) (2024)
- [CybORG: A Gym for the Development of Autonomous Cyber Agents](https://mlsanity.com/paper/2108.09118/) (2021)
- [UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats](https://mlsanity.com/paper/2001.01525/) (2020)
- [HOLMES: Real-time APT Detection through Correlation of Suspicious Information Flows](https://mlsanity.com/paper/1810.01594/) (2018)
- [Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation](https://mlsanity.com/paper/2207.12355/) (2022)
- [Network Environment Design for Autonomous Cyberdefense](https://mlsanity.com/paper/2103.07583/) (2021)
- [SLEUTH: Real-time Attack Scenario Reconstruction from COTS Audit Data](https://mlsanity.com/paper/1801.02062/) (2018)
- [Incalmo: An Autonomous LLM-assisted System for Red Teaming Multi-Host Networks](https://mlsanity.com/paper/2501.16466/) (2025)
- [TrafficLLM: Enhancing Large Language Models for Network Traffic Analysis with Generic Traffic Representation](https://mlsanity.com/paper/2504.04222/) (2025)
- [Large Language Models are Autonomous Cyber Defenders](https://mlsanity.com/paper/2505.04843/) (2025)
- [Towards Explainable Network Intrusion Detection using Large Language Models](https://mlsanity.com/paper/2408.04342/) (2024)
- [Measuring AI Agents' Progress on Multi-Step Cyber Attack Scenarios](https://mlsanity.com/paper/2603.11214/) (2026)
- [ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System](https://mlsanity.com/paper/2509.14589/) (2025)
- [eX-NIDS: A Framework for Explainable Network Intrusion Detection Leveraging Large Language Models](https://mlsanity.com/paper/2507.16241/) (2025)
- [Retrieval-Augmented LLMs for Security Incident Analysis](https://mlsanity.com/paper/2603.18196/) (2026)
- [From Alerts to Intelligence: A Novel LLM-Aided Framework for Host-based Intrusion Detection](https://mlsanity.com/paper/2507.10873/) (2025)
- [Network Intrusion Detection: Evolution from Conventional Approaches to LLM Collaboration and Emerging Risks](https://mlsanity.com/paper/2510.23313/) (2025)
- [HIDBench: Benchmarking Large Language Models for Host-Based Intrusion Detection](https://mlsanity.com/paper/2605.21773/) (2026)

## Abstract (arXiv)

Cyber attacks are increasingly automated, narrowing the time available for human analysts to detect, reason about, and respond to intrusions. Large language models (LLMs) offer a promising foundation for autonomous cyber defense because they can correlate heterogeneous evidence and reason about previously unseen threats. However, directly applying LLMs to operational security telemetry is impractical: raw logs arrive faster than current models can process them, individual events are often ambiguous, and unconstrained LLM actions can introduce significant operational risk. We present BlueSTAR, a tiered agentic architecture for autonomous cyber defense in enterprise IT/OT networks. BlueSTAR first transforms high-volume security telemetry into compact indicators of compromise. We further introduce a resilience metric that jointly captures attacker reach, impact on mission-critical assets, and disruption caused by defensive actions. We evaluate BlueSTAR on two live enterprise IT/OT cyber ranges using seven attack chains based on real-world intrusion techniques. Across attack chains, BlueSTAR retains the fast containment of deterministic response for known threats while successfully defending against attacks requiring contextual and cross-cycle reasoning, including credential theft, repeated compromise, concurrent attackers, and attacks against physical processes.