Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers
Factual evidence
What the source reports
Researchers adapted the Honeyquest framework to evaluate how 21 different LLM architectures respond to cyber deception and honeypots.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 26 October 2026
- Collected by OneBench
- 18 Aug 2026, 20:31 UK
- Original headline
- Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers ↗
Stored source excerpt
arXiv:2606.21037v2 Announce Type: replace-cross Abstract: The empirical foundation of cyber deception relies on human-centered hypotheses, but the rapid emergence of autonomous, AI-enabled attackers challenges whether…
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OneBench interpretation
Institutional assessment
So what
Autonomous AI threats process deceptive network assets differently than human adversaries, altering how your cyber defense teams structure honeypots.
Do what
Brief your CISO's threat intelligence and red-teaming units to factor AI agent cognitive patterns into deception architecture evaluations.