# AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training & Experimentation Scenarios

arXiv: https://arxiv.org/abs/2510.25189 · HTML: https://mlsanity.com/paper/2510.25189/

## 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 3 of its 19 references that are in the map.
- First posted on arXiv 29 Oct 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, 3 of 3)

- [On generating network traffic datasets with synthetic attacks for intrusion detection](https://mlsanity.com/paper/1905.00304/) (2019)
- [Architectural Selection Framework for Synthetic Network Traffic: Quantifying the Fidelity-Utility Trade-off](https://mlsanity.com/paper/2410.16326/) (2024)
- [Improving LLM Agents with Reinforcement Learning on Cryptographic CTF Challenges](https://mlsanity.com/paper/2506.02048/) (2025)

## Abstract (arXiv)

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models (LLMs) show promise for automated synthesis, unconstrained generation often yields configurations that fail validation or execution. We present AgentCyTE, a framework integrating LLM-based reasoning with deterministic, schema-constrained network emulation to generate and refine executable threat environments. Through an agentic feedback loop, AgentCyTE observes scenario outcomes, validates correctness, and iteratively enhances realism and consistency. This hybrid approach preserves LLM flexibility while enforcing structural validity, enabling scalable, data-driven experimentation and reliable scenario generation for threat modeling and adaptive cybersecurity training. Our framework can be accessed at: https://github.com/AnantaaKotal/AgentCyTE