Large Language Model Detection and Emulation for Evolving Social Engineering
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Solution Overview
Problem
Existing fraud detection systems struggle to adapt to evolving social engineering tactics employed by malicious actors, with simple models being ineffective against varied sentence structures and complex models requiring large datasets that are not available for social engineering patterns.
Innovation Solution
Utilizing large language models (LLMs) fine-tuned to classify social engineering and emulate malicious behavior, generating confidence scores and feedback for customer service agents to mitigate potential fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple fraud detection models are used, then the system is easier to implement and requires less computational resources, but the model becomes ineffective against varied sentence structures and evolving social engineering tactics
Solution Approach 1:
The patent transforms the fraud detection approach by changing the parameters of the language model from simple keyword matching to large language models with contextual understanding capabilities. This allows the system to detect evolved social engineering tactics while maintaining reasonable computational requirements through efficient model architecture and training approaches.
Solution Approach 2:
The system implements dynamic adaptation by continuously training and updating the large language model with new fraud patterns and social engineering tactics. This enables the model to evolve its detection capabilities over time, maintaining high effectiveness against varied and changing attack methods without requiring complete system redesign.
2Reliability
If complex fraud detection models are used, then the accuracy and adaptability to evolving tactics improve, but large datasets are required that are not available for social engineering patterns
Solution Approach 1:
The patent applies preliminary action by pre-training the large language model on general language patterns and then fine-tuning it with available fraud-specific datasets. This two-stage approach allows the model to develop robust language understanding from general data before specializing in fraud detection, reducing the amount of domain-specific training data needed while maintaining high accuracy.
Solution Approach 2:
The system achieves universality by using a large language model that can detect multiple types of fraud and social engineering tactics across different domains and contexts. The model's general language understanding capabilities transfer across various fraud scenarios, reducing the need for extensive domain-specific training data for each particular fraud type.
3Adaptability or versatility
If large language models are deployed for real-time fraud detection, then the ability to recognize emerging threats improves, but the computational resources and processing time increase
Solution Approach 1:
The patent segments the fraud detection process into multiple stages: preliminary filtering using simpler rules, intermediate analysis using the large language model for suspicious cases, and detailed investigation for high-risk scenarios. This segmentation allows real-time detection of emerging threats while reducing overall computational resource consumption by applying the most resource-intensive model only when necessary.
Data Source
AI summary
A method includes generating a set of training data, wherein a first training instance of the set of training data comprises a first plurality of messages between a first customer service agent and a first purported customer and a first label indicating whether one or more messages of the first plurality of messages are associated with malicious behavior; training a large language model (LLM) using the set of training data to generate messages; generating, by the LLM representing a second purported customer, a message associated with malicious behavior; receiving a response message from a second customer service agent based on the message associated with malicious behavior; and in response to the response message being an authorization, generating a feedback for the second customer service agent based on a number of responses before the response was an action.