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

Large language model (LLM)-powered honeypots are becoming increasingly complex instruments for luring and examining threat actors in the rapidly changing field of cybersecurity.

A recent deployment using Beelzebub, a low-code honeypot framework, demonstrated how such systems can simulate vulnerable SSH services to capture malicious activities in real-time.

By configuring a single YAML file, defenders can emulate an interactive SSH environment backed by LLMs like OpenAI’s GPT-4o or alternatives such as Llama.

Deploying Advanced Honeypots

The setup involves cloning example repositories, editing configuration parameters including API keys, and launching via Docker Compose.

This minimalistic approach allows the honeypot to respond dynamically to commands, mimicking a legitimate Linux server with outputs like kernel versions, uptime statistics, and process counts, thereby deceiving attackers into revealing their tactics, techniques, and procedures (TTPs).

For instance, the honeypot was tuned to accept specific weak credentials, such as “admin/123456,” and enforce timeouts to manage interaction durations, ensuring controlled exposure while gathering forensic data.

Analysis of a captured session from IP address 45.175.100.69 highlighted the honeypot’s efficacy in exposing malware distribution and command-and-control (C2) infrastructures.

The threat actor, authenticated as “admin” with password “123456,” initiated a sequence of commands starting with system reconnaissance via “uname -a,” “uptime,” and “nproc,” receiving plausible responses from the LLM to maintain the illusion of a compromised Ubuntu host.

Unmasking Botnet Operations

Subsequent actions involved navigating to temporary directories, downloading a Perl-based backdoor named “sshd” from a compromised Joomla CMS site at deep-fm.de, and attempting execution, which the honeypot simulated with permission-denied errors to prolong engagement.

The actor then fetched an archive “emech.tar.gz” containing botnet components, including installation scripts, binaries, and libraries, extracting and manipulating them before shifting to another directory for repeated downloads and chmod operations, even escalating to sudo attempts that were artfully rebuffed.

Further dissection revealed the “sshd” script as an IRC-driven backdoor facilitating remote command execution and denial-of-service (DoS) attacks, configured to connect to Undernet’s ix1.undernet.org on port 6667, with channels like #rootbox and #c0d3rs-TeaM serving as C2 hubs.

Code analysis uncovered parameters such as a maximum of eight concurrent connections, sleep intervals for evasion, and admin handles like “warlock`,” pointing to a PerlBot v2.0 variant targeting temporary directories for persistence.

According to the report, by infiltrating these channels, investigators observed live interactions between the actor and infected nodes, underscoring the botnet’s reliance on public IRC for coordination.

This intelligence enabled swift mitigation: reporting the channels to Undernet administrators effectively disrupted the C2, demonstrating a low-effort strategy to dismantle such networks.

The incident underscores LLM honeypots’ role in proactive threat hunting, transforming passive decoys into active intelligence platforms that not only log attacks but also elicit disclosures of exploits and binaries.

By simulating realistic responses, these systems can extend attacker dwell time, yielding deeper insights into malware ecosystems without risking actual infrastructure.

As threats grow more automated, integrating AI-driven deception layers could become standard in defensive arsenals, potentially shifting the balance toward early detection and neutralization of botnets and exploit chains.

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