Cybersecurity researchers have successfully deployed an artificial intelligence-powered honeypot that deceived a real threat actor into revealing their malicious activities and botnet infrastructure.
The incident, documented by the Beelzebub honeypot framework team, demonstrates how Large Language Model (LLM) technology can be weaponized for defensive cybersecurity purposes, creating realistic system environments that fool even experienced cybercriminals.
The threat actor, operating from IP address 45.175.100.69, unknowingly downloaded multiple exploit tools and attempted to establish botnet connections while being monitored in the controlled environment.
Advanced Honeypot Technology Deployment
The successful capture utilized Beelzebub, a low-code honeypot framework that integrates OpenAI’s GPT-4 model to simulate realistic SSH interactions.
The configuration required minimal setup through a single YAML file, demonstrating the accessibility of modern AI-powered cybersecurity tools.
The system was configured to accept common weak passwords, including “root,” “123456,” and “admin,” which are frequently targeted by automated attack scripts.
textprotocol: "ssh"
address: ":2222"
plugin: "LLMHoneypot"
serverVersion: "OpenSSH"
passwordRegex: "^(root|qwerty|Smoker666|123456|jenkins|minecraft)$"
llmProvider: "openai"
llmModel: "gpt-4o"
The LLM honeypot successfully convinced the attacker they had compromised a legitimate Ubuntu server, responding to system commands like uname -a and uptime with convincing output that maintained the deception throughout the attack sequence.
Malicious Activities and Payload Analysis
The threat actor demonstrated sophisticated attack methodologies, beginning with system reconnaissance commands before downloading malicious payloads from a compromised Joomla-based website at deep-fm.de.
The primary payload was an 85KB Perl script disguised as “sshd,” which analysis revealed to be a backdoor trojan designed to connect infected systems to an IRC-based command and control infrastructure.
The malware contained hardcoded configuration details, including the IRC server ix1.undernet.org on port 6667, with specific channels #rootbox and #c0d3rs-TeaM used for botnet communications.
The script identified itself as “rootbox PerlBot v2.0” and was configured to accept commands from administrators identified as “warlock”.
Counter-Intelligence and Disruption Efforts
Researchers leveraged the extracted intelligence to infiltrate the threat actor’s command and control infrastructure, successfully accessing the IRC channels to observe active botnet operations.
The team reported their findings to the Undernet IRC network administrators, resulting in the channels being shut down and effectively dismantling the botnet’s communication infrastructure.
This case demonstrates the dual-use potential of AI technology in cybersecurity, where the same systems used for automation can be repurposed for defensive deception operations, providing valuable intelligence for threat hunting and network defense initiatives.
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