AI Login Agent for Adaptive Web Authentication Testing
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Solution Overview
Problem
Existing security testing tools struggle to handle the variations in login implementations across web applications due to differences in login sequences, forms, and multi-factor authentication methods, which cannot be fully addressed by heuristics-based rules.
Innovation Solution
A login agent interacts with a generative AI foundation model to automatically and intelligently log into web applications by capturing interactive elements, constructing prompts to determine how to interact with these elements, and using user emulation tools to perform the interactions, with feedback from the model to assess login success or failure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If heuristics-based rules are used for security testing, then the testing process is automated, but the tool cannot handle variations in login implementations across different web applications
Solution Approach 1:
The patent introduces an AI-based intermediary component that mediates between the automated security testing system and the diverse login implementations. This AI intermediary analyzes login pages, understands different authentication patterns, and dynamically adapts the testing approach, thereby resolving the contradiction between automation and adaptability.
Solution Approach 2:
The system dynamically changes parameters based on the detected login implementation type. Instead of using fixed heuristics, the AI model adjusts testing parameters such as interaction sequences, form field identification, and authentication flow based on the specific characteristics of each web application's login page.
2Adaptability or versatility
If AI foundation models are used to handle login variations, then adaptability improves, but the complexity of the testing tool increases
Solution Approach 1:
The AI foundation model performs self-service by automatically analyzing login pages, identifying interactive elements, and determining the appropriate testing strategy without requiring manual configuration. The system self-adapts to different login implementations, reducing the need for complex rule-based configurations and manual intervention.
Solution Approach 2:
The patent replaces complex mechanical rule-based systems with an AI-based cognitive system. Instead of maintaining extensive if-then rules for different login scenarios, the system uses machine learning models to naturally understand and adapt to various authentication patterns, simplifying the overall system architecture despite the increased intelligence required.
3Measurement precision
If manual methods are used for login testing, then accuracy is maintained, but productivity and efficiency decrease
Solution Approach 1:
The system implements feedback loops where the AI model continuously learns from login testing results. The model analyzes successful and failed login attempts, refines its understanding of the authentication process, and improves subsequent testing accuracy. This feedback mechanism enables the automated system to maintain high precision while scaling productivity.
Solution Approach 2:
The AI foundation model performs preliminary analysis of login pages before executing security tests. It pre-identifies interactive elements, understands the authentication flow, and prepares the testing strategy in advance, ensuring accurate execution while maintaining high productivity through automated preparation and analysis.
Data Source
AI summary
A login agent interacts with a foundation model(s) until successful login to an application or an assessment of a failed login can be obtained. Initially, a web page corresponding to login for a web application will be indicated to the login agent. The login agent captures interactive elements of the web page. The login agent prompts a foundation model(s) to select which of the captured interactive elements to interact with and how to interact with the selected elements. The login agent determines commands based on the response(s) and, with the commands, uses a tool to automatically interact with the web page via a browser. The login agent captures a web page resulting from the user emulated interaction and prompts the foundation model(s) to determine whether log in was successful or failed. The response from the foundation model(s) guides the login agent to either retry login or report results.