Honeypots and beyond – LLMs in cyberdefence#

Maciej Miłostan, Michał Ślusarczyk (PCSS)

• Why: An unusual, operationally grounded intersection of LLMs and security: LLMs inside the honeypot, engaging attackers — and the new attack surface that creates.
• What: Threat-intel collection from the GN5-2 honeynet (T-Pot based, with Kibana visualizations and sandboxing pipelines), plus the synergy of pairing honeypots with LLMs. Crucially, they instrument the failure modes: LangFuse monitoring tracks session abandonments caused by LLM failures (e.g. unrealistic interaction), and they compare robust, secure system prompts against poorly crafted ones that leak system details or allow instruction hijacking — showing prompt quality directly influences both detection accuracy and resistance to prompt injection.
• How: LLM-driven interactive decoys produce richer attacker behavioral data than static honeypots, at the cost of becoming a prompt-injection surface themselves; the evaluation methodology (tracking abandonment, comparing prompt classes) is the transferable part.
• Where applicable: Self-hosted LLM stacks in defensive security operations; findings are from a live NREN honeynet rather than a lab.