Act as a Honeytoken Generator! An Investigation into Honeytoken Generation with Large Language Models
arxiv(2024)
摘要
With the increasing prevalence of security incidents, the adoption of
deception-based defense strategies has become pivotal in cyber security. This
work addresses the challenge of scalability in designing honeytokens, a key
component of such defense mechanisms. The manual creation of honeytokens is a
tedious task. Although automated generators exists, they often lack
versatility, being specialized for specific types of honeytokens, and heavily
rely on suitable training datasets. To overcome these limitations, this work
systematically investigates the approach of utilizing Large Language Models
(LLMs) to create a variety of honeytokens. Out of the seven different
honeytoken types created in this work, such as configuration files, databases,
and log files, two were used to evaluate the optimal prompt. The generation of
robots.txt files and honeywords was used to systematically test 210 different
prompt structures, based on 16 prompt building blocks. Furthermore, all
honeytokens were tested across different state-of-the-art LLMs to assess the
varying performance of different models. Prompts performing optimally on one
LLMs do not necessarily generalize well to another. Honeywords generated by
GPT-3.5 were found to be less distinguishable from real passwords compared to
previous methods of automated honeyword generation. Overall, the findings of
this work demonstrate that generic LLMs are capable of creating a wide array of
honeytokens using the presented prompt structures.
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