LLMs fall for cyber deception traps more than humans, study finds

A new research paper introduces "Honeyquest for LLMs," an automated framework designed to evaluate how Large Language Models (LLMs) respond to cyber deception techniques. The study tested 21 LLMs from 10 providers, finding that these AI models fall for deceptive traps at a significantly higher rate than human attackers. Notably, LLMs lack the defensive attention-diversion effect seen in humans and exhibit a critical recognition-action gap, often identifying deception but exploiting it nonetheless. These findings suggest that current human-centered cyber deception strategies are not effective against AI attackers, necessitating the development of new AI-native active defense frameworks. AI

IMPACT Highlights the need for new AI-native active defense strategies as LLMs prove more susceptible to cyber deception than humans.

RANK_REASON Research paper detailing a new evaluation framework for LLM cyber deception. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Honeyquest for LLMs
• Hugging Face
• Kerri Prinos
• LLMs

• paper
• safety

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