# CORTEX: Collaborative LLM Agents for High-Stakes Alert Triage

arXiv: https://arxiv.org/abs/2510.00311 · HTML: https://mlsanity.com/paper/2510.00311/

## Key facts

- Cited by 14 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 12 of its 30 references that are in the map.
- First posted on arXiv 30 Sep 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, 12 of 12)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [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)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Automated Alert Classification and Triage (AACT): An Intelligent System for the Prioritisation of Cybersecurity Alerts](https://mlsanity.com/paper/2505.09843/) (2025)

## Built on by (most cited first, 14 of 14)

- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)
- [OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detection](https://mlsanity.com/paper/2603.22499/) (2026)
- [Retrieval-Augmented LLMs for Security Incident Analysis](https://mlsanity.com/paper/2603.18196/) (2026)
- [The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines](https://mlsanity.com/paper/2512.06659/) (2025)
- [Agentic Observability: Automated Alert Triage for Adobe E-Commerce](https://mlsanity.com/paper/2602.02585/) (2026)
- [AI Native Asset Intelligence](https://mlsanity.com/paper/2605.09115/) (2026)
- [Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit](https://mlsanity.com/paper/2604.09998/) (2026)
- [CyberAId: AI-Driven Cybersecurity for Financial Service Providers](https://mlsanity.com/paper/2605.01892/) (2026)
- [Cyber-Capable AI Agents: Vulnerabilities, Evaluation Containment, and Defensive Response](https://mlsanity.com/paper/2607.25379/) (2026)
- [GenAI-Driven Threat Detection with Microsoft Security Copilot](https://mlsanity.com/paper/2605.20896/) (2026)
- [An Organization-Scoped LLM Agent Runtime Architecture for Regulated Cybersecurity Operations](https://mlsanity.com/paper/2605.30604/) (2026)
- [Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring](https://mlsanity.com/paper/2606.09433/) (2026)
- [Evaluating Agentic Learning Harness Capabilities Without Labels via the Scaling Hypothesis](https://mlsanity.com/paper/2608.13608/) (2026)
- [REFINE: A Resilient Evolution Framework for Intelligent Enterprise Alert Triage in Security Operations Centers](https://mlsanity.com/paper/2609.32516/) (2026)

## Abstract (arXiv)

Security Operations Centers (SOCs) are overwhelmed by tens of thousands of daily alerts, with only a small fraction corresponding to genuine attacks. This overload creates alert fatigue, leading to overlooked threats and analyst burnout. Classical detection pipelines are brittle and context-poor, while recent LLM-based approaches typically rely on a single model to interpret logs, retrieve context, and adjudicate alerts end-to-end -- an approach that struggles with noisy enterprise data and offers limited transparency. We propose CORTEX, a multi-agent LLM architecture for high-stakes alert triage in which specialized agents collaborate over real evidence: a behavior-analysis agent inspects activity sequences, evidence-gathering agents query external systems, and a reasoning agent synthesizes findings into an auditable decision. To support training and evaluation, we release a dataset of fine-grained SOC investigations from production environments, capturing step-by-step analyst actions and linked tool outputs. Across diverse enterprise scenarios, CORTEX substantially reduces false positives and improves investigation quality over state-of-the-art single-agent LLMs.