LLM Phishing Awareness Training for Spear Phishing Susceptibility
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Solution Overview
Problem
Existing cybersecurity measures are inadequate in effectively training users to recognize and combat spear phishing attacks, which can compromise enterprise networks by granting attackers unauthorized access.
Innovation Solution
A training system utilizing a large language model (LLM) to generate tailored spear phishing simulations based on user interactions, with feedback loops to refine the model and assess user susceptibility, enabling targeted training interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cybersecurity measures are used to detect phishing attacks, then basic phishing detection is achieved, but user awareness and susceptibility to spear phishing attacks remain insufficient
Solution Approach 1:
The system performs preliminary training by generating simulated phishing emails tailored to each user's actual email interactions before real attacks occur. The LLM creates customized training scenarios based on the user's role, department, and observed email patterns, enabling users to practice identifying phishing attempts in a safe environment before encountering real threats.
Solution Approach 2:
The system implements continuous feedback loops where user interactions with both real and simulated phishing emails are analyzed. The LLM uses this feedback to refine future simulations, making them progressively more realistic and targeted to the user's specific vulnerabilities. Interaction scores and detailed feedback provide users with actionable insights into their phishing detection capabilities.
2Adaptability or versatility
If generic phishing training is provided to all users, then basic awareness is raised, but targeted susceptibility assessment and customized training are insufficient
Solution Approach 1:
The system transitions from uniform generic training to localized customized training for each user. By analyzing individual email interaction patterns, the LLM identifies specific phishing techniques that particular users are most vulnerable to based on their role, department, and communication habits. Each user receives tailored simulations that address their specific weaknesses rather than generic content applicable to all users.
Solution Approach 2:
The system dynamically adjusts training parameters including simulation difficulty, phishing technique types, email content characteristics, and interaction scenarios based on each user's measured susceptibility level. As users improve their detection skills, the system progressively increases the sophistication of simulations to maintain effective training while precisely measuring ongoing susceptibility through interaction scoring.
3Measurement precision
If comprehensive user monitoring is implemented to assess phishing susceptibility, then accurate susceptibility measurement is achieved, but system complexity and user privacy concerns increase
Solution Approach 1:
The LLM serves as an intermediary that processes user email interactions without requiring direct access to sensitive personal data. It analyzes patterns in email metadata, interaction behaviors, and response times to assess susceptibility while maintaining user privacy. The system focuses on behavioral patterns rather than personal information, reducing complexity while preserving measurement accuracy.
4Adaptability or versatility
If LLM-generated customized phishing simulations are deployed, then targeted user training is achieved, but computational resources and processing time increase
Solution Approach 1:
The LLM generates and caches simulated phishing emails in advance based on user profiles and observed patterns, rather than creating custom simulations in real-time for each interaction. This preliminary generation reduces computational burden during actual training delivery while maintaining high customization levels. The system prepares a library of tailored simulations that can be efficiently deployed to multiple users.
Data Source
AI summary
The present disclosure is directed to training email users to enhance awareness of attempted spear phishing by attackers observing user actions to build a model of user susceptibilities using a trained LLM. A service in an intrusion prevention system can receive from one or more accounts linked to an enterprise and provide a message, along with a prompt to the LLM, stimulating the generation of one or more variants of the received messages that exhibit similar content characteristics. The LLM can produce a set of variant messages encompassing these content characteristics, purposefully including one or more phishing traits identified during training with the prelabeled dataset. These variant messages are then transmitted to the relevant accounts to assess interactions with the set. Based on the interactions observed across the accounts, an interaction score is generated to evaluate the efficacy of the user's training to avoid phishing attempts within the enterprise environment.