LLM Honeypots Can Deceive Threat Actors into Exposing Binaries and Known Exploits

Cybersecurity researchers have successfully shown that Large Language Model (LLM) honeypots can effectively trick threat actors into exposing their attack techniques and malicious payloads.

In a recent breakthrough, an SSH-based LLM honeypot lured a real attacker who unknowingly engaged with the AI-driven system, believing they had infiltrated a legitimate server.

During the interaction, the attacker downloaded several binary files carrying known exploits and attempted to set up persistent backdoor access using advanced botnet infrastructure.

Unlike traditional honeypots that use static responses, this LLM-powered system engaged attackers in natural, human-like conversations—making the deception far more convincing and prolonging their interaction.

The breakthrough occurred when security analysts deployed Beelzebub, an advanced low-code honeypot framework that leverages LLM technology to build highly realistic, interactive trap environments.

Researchers at Beelzebub Labs identified the malware after studying the attacker’s systematic behavior and analyzing the malicious binaries they attempted to deploy within the compromised environment.

The threat actor displayed advanced tradecraft, executing a methodical post-exploitation sequence involving reconnaissance, privilege escalation attempts, and strategic malware distribution.

Further investigation revealed that the attacker utilized multiple attack vectors before ultimately trying to link the compromised system to a wider botnet network for persistent command-and-control operations.

Command-and-Control Infrastructure Analysis

In the final attack phase, the adversary executed a carefully engineered Perl script intended to establish communication with an IRC-based command-and-control server.

Analysis of the captured script uncovered hardcoded IRC server credentials and predefined channel details, giving researchers critical insights into the botnet’s operational framework and communication protocols.

The honeypot configuration demonstrated remarkable simplicity, requiring only a single YAML configuration file to specify SSH service parameters and LLM integration settings:-

apiVersion: "v1"
protocol: "ssh"
address: ":2222"
description: "SSH interactive ChatGPT"

This incident marks a major leap in deception technology, demonstrating how artificial intelligence can elevate traditional honeypots to deliver deeper threat intelligence and more effective malware behavior analysis within controlled environments.