LLM Honeypot Deceives Attackers into Exposing Their Tactics

An innovative Large Language Model (LLM) honeypot that deceived a threat actor into exposing their complete attack methodology, including botnet infrastructure and command-and-control channels.

The breakthrough demonstrates how artificial intelligence can be weaponized for cybersecurity defense, turning attackers’ own tools against them.

The attack was captured using Beelzebub, a sophisticated low-code honeypot framework that simulates vulnerable SSH servers powered by LLM technology.

The system required minimal configuration—just a single YAML file and an OpenAI API key—to create a convincing Ubuntu server environment that responded intelligently to attacker commands.

The threat actor, originating from IP address 45.175.100.69, gained access using basic credentials (admin/123456) and immediately began reconnaissance activities.

The LLM honeypot convincingly simulated system responses to commands like uname -a and uptime, providing realistic output that maintained the illusion of a legitimate compromised server.

The attacker proceeded to download multiple malicious binaries from a compromised Joomla-based website at deep-fm.de, including a Perl script disguised as an SSH daemon and an archive containing additional exploitation tools.

The honeypot’s realistic responses, including simulated permission errors and file system interactions, kept the attacker engaged throughout the entire attack sequence.

Botnet Infrastructure Details

Analysis of the downloaded Perl script revealed sophisticated backdoor functionality designed to establish persistent command-and-control communications.

The 85KB script, masquerading as a legitimate SSH daemon, contained embedded configuration details that exposed the entire botnet infrastructure.

The backdoor was programmed to connect to IRC servers on the Undernet network, specifically ix1.undernet.org on port 6667.

Critical configuration parameters embedded in the script revealed two active command channels: #rootbox and #c0d3rs-TeaM, along with administrator credentials and authentication hosts.

The script included advanced features for remote command execution and distributed denial-of-service capabilities, with built-in sleep timers and connection management to avoid detection.

The threat actor attempted multiple execution methods, including permission modifications and directory changes, demonstrating persistent effort to establish the backdoor connection.

Command Channels, Disrupt Operations

Using the extracted IRC configuration data, security researchers successfully addressed the botnet command-and-control channels, observing active communications between the threat actor and compromised systems.

The intelligence gathered provided unprecedented visibility into ongoing botnet operations and infected host communications.

The research team documented live interactions within the #rootbox channel, capturing evidence of both the threat actor’s presence and infected systems checking in for commands.

This real-time intelligence allowed for comprehensive mapping of the botnet’s operational structure and communication protocols.

Following the intelligence gathering phase, researchers reported the malicious IRC channels to Undernet administrators, effectively disrupting the botnet’s command infrastructure.

This coordinated response demonstrates how honeypot intelligence can enable rapid threat mitigation and infrastructure takedown.

The successful operation highlights the growing potential of AI-powered honeypots in cybersecurity defense, offering new methodologies for threat intelligence collection and attacker behavior analysis while providing actionable intelligence for network defenders.

Find this Story Interesting! Follow us on LinkedIn and X to Get More Instant Updates.