LLM-Based Conversational Authentication for Continuous User Verification
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Solution Overview
Problem
Current authentication methods are either too complex and prone to being weakened by users or too simple and vulnerable to identity theft and physical risks, lacking a balance between security and usability, especially in multi-factor authentication systems.
Innovation Solution
A large language model (LLM) is trained on user-specific data, including project information and interaction patterns, to generate conversational authentication challenges that only the user can answer, integrating into everyday workflows to continuously authenticate users and devices, thereby enhancing access control and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex multi-factor authentication methods are used, then security is improved, but ease of operation deteriorates and users may weaken the authentication method
Solution Approach 1:
The system continuously authenticates users in the background by analyzing their natural interactions with the system, eliminating the need for users to manually perform authentication actions. The LLM monitors user behavior patterns, communication styles, and interaction sequences automatically, providing authentication without user intervention or awareness of the authentication process.
Solution Approach 2:
The system performs authentication checks continuously before accessing resources, rather than requiring explicit authentication actions at specific points. By continuously analyzing user interactions in real-time, the system establishes authentication status in advance and maintains it throughout the session, preventing unauthorized access before it can occur.
2Ease of operation
If simple authentication methods are used, then ease of operation is improved, but security deteriorates due to vulnerability to identity theft
Solution Approach 1:
The authentication process continues continuously throughout the user session by constantly analyzing user interactions, rather than being a discrete one-time event. The LLM continuously monitors communication patterns, response times, and interaction sequences, maintaining ongoing verification of user identity without interrupting the user's workflow or requiring repeated explicit authentication actions.
Solution Approach 2:
The system provides continuous feedback by analyzing user responses to authentication challenges and adjusting authentication decisions based on the analysis results. The LLM evaluates each user interaction against established patterns and provides real-time authentication decisions, allowing the system to respond dynamically to potential security threats while maintaining ease of use for legitimate users.
3Device complexity
If traditional authentication tokens are used, then access control is simplified, but the system becomes vulnerable to token compromise and attacks
Solution Approach 1:
The system replaces traditional mechanical authentication tokens with an LLM-based intelligent authentication system that analyzes user behavior patterns. Instead of relying on static tokens that can be stolen or compromised, the system uses a neural network model that continuously evaluates dynamic user interactions, making it impossible to compromise through traditional token theft methods.
Solution Approach 2:
The authentication system changes from using static token parameters to analyzing dynamic behavioral parameters such as response time, communication style, and interaction patterns. The LLM evaluates multiple behavioral parameters simultaneously to determine authentication status, making the authentication process resistant to parameter manipulation or token replication attacks.
Data Source
AI summary
Systems and methods of authentication utilizing a large language model (LLM) are provided. The method includes accessing a knowledge base comprising user-specific data of a user device associated with a domain. In response to a request from the user device for access to a resource of the domain, the method includes generating one or more authentication challenges based on the user-specific data. The one or more authentication challenges are generated by an LLM trained on the user-specific data and contextual interactions associated with the user device. In response to determining that a response to the one or more authentication challenges matches the user-specific data of the knowledge base and the contextual interactions, the method includes providing the user device access to the resource of the domain.