An LLM Agent-based Framework for Whaling Countermeasures
Daisuke Miyamoto, Takuji Iimura, Narushige Michishita

TL;DR
This paper introduces an LLM-based agent framework designed to help university faculty defend against sophisticated Whaling attacks by creating personalized risk profiles and analyzing potential threats.
Contribution
It proposes a novel LLM agent-based framework that constructs personalized defense profiles and assesses Whaling email threats for university faculty.
Findings
The framework can generate context-aware threat judgments with explanations.
Preliminary experiments show effective identification of Whaling risks.
Highlights practical challenges for real-world deployment.
Abstract
With the spread of generative AI in recent years, attacks known as Whaling have become a serious threat. Whaling is a form of social engineering that targets important high-authority individuals within organizations and uses sophisticated fraudulent emails. In the context of Japanese universities, faculty members frequently hold positions that combine research leadership with authority within institutional workflows. This structural characteristic leads to the wide public disclosure of high-value information such as publications, grants, and detailed researcher profiles. Such extensive information exposure enables the construction of highly precise target profiles using generative AI. This raises concerns that Whaling attacks based on high-precision profiling by generative AI will become prevalent. In this study, we propose a Whaling countermeasure framework for university faculty…
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Taxonomy
TopicsScientific Computing and Data Management · Spam and Phishing Detection · Ethics and Social Impacts of AI
