Towards Agentic Honeynet Configuration

TL;DR

This paper presents an AI-driven agentic system that autonomously manages honeypot deployment by analyzing attack signals and dynamically reconfiguring assets to optimize attacker engagement and resource use.

Contribution

It introduces a novel agentic architecture that adapts honeypot deployment in real-time based on attack analysis, unlike static traditional methods.

Findings

Effective inference of attacker intent

Improved resource-efficient honeypot management

Enhanced attacker engagement in simulated environment

Abstract

Honeypots are deception systems that emulate vulnerable services to collect threat intelligence. While deploying many honeypots increases the opportunity to observe attacker behaviour, in practise network and computational resources limit the number of honeypots that can be exposed. Hence, practitioners must select the assets to deploy, a decision that is typically made statically despite attackers' tactics evolving over time. This work investigates an AI-driven agentic architecture that autonomously manages honeypot exposure in response to ongoing attacks. The proposed agent analyses Intrusion Detection System (IDS) alerts and network state to infer the progression of the attack, identify compromised assets, and predict likely attacker targets. Based on this assessment, the agent dynamically reconfigures the system to maintain attacker engagement while minimizing unnecessary exposure. The approach is evaluated in a simulated environment where attackers execute Proof-of-Concept exploits for known CVEs. Preliminary results indicate that the agent can effectively infer the intent of the attacker and improve the efficiency of exposure under resource constraints