paperJune 2026Unreviewed

Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers

Kerri Prinos, Lilianne Brush, Cameron Denton

Abstract

The empirical foundation of cyber deception relies on human-centered hypotheses, but the rapid emergence of autonomous, AI-enabled attackers challenges whether this foundation transfers to AI agents. To address this, we introduce an automated evaluation framework adapted from the Honeyquest instrument to assess LLM attacker judgment at scale. Our 21-LLM cohort spanned 10 providers, diverse architectures and specializations, open- and closed-weight models, and parameter scales from 8B to over 1T.

Cite

@misc{prinos2026honeyquest,
  title = {{Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers}},
  author = {Kerri Prinos and Lilianne Brush and Cameron Denton},
  year = {2026},
  month = jun,
  eprint = {2606.21037},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.21037}
}