LLM-Guided Penetration Testing Automation With Feedback Loops
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Solution Overview
Problem
Existing penetration testing methods are time-consuming and tedious, especially for novice pentesters, as they often rely on outdated pentest notes that require significant manual effort and optimization, and existing machine learning solutions fail to automate pentesting actions effectively.
Innovation Solution
Utilizing pre-trained large language models (LLMs) equipped with a knowledge base of system configurations and commands, which are further trained with penetration testing notes to function as conversational agents, generating and executing penetration testing scenarios with feedback loops for refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional penetration testing methods are used with manual execution of pentest notes, then comprehensive security testing can be performed, but the process becomes time-consuming and tedious
Solution Approach 1:
The penetration testing system performs self-service by automatically generating, executing, and analyzing test scenarios without requiring manual intervention at each step. The AI model autonomously chains together multiple penetration testing tools and interprets results, allowing the system to serve itself in conducting comprehensive security assessments while reducing the time investment required from human testers.
Solution Approach 2:
The patent replaces the mechanical manual process of executing pentest notes with an AI-based automated system. The AI model substitutes human operators by automatically generating test scenarios, chaining together penetration testing tools, interpreting outputs, and producing reports, thereby eliminating the tedious manual mechanics while maintaining comprehensive testing coverage.
2Ease of operation
If novice penetration testers use traditional methods with outdated pentest notes, then basic testing can be performed, but significant manual effort and optimization are required
Solution Approach 1:
The AI model acts as an intermediary between novice penetration testers and the complex penetration testing process. It mediates by automatically generating appropriate test scenarios based on simple user inputs, chaining together multiple tools with correct parameters, and interpreting results in understandable terms, thereby making the system easy to operate while maintaining high productivity through automated optimization.
Solution Approach 2:
The system changes the parameter of operational complexity by transforming complex manual testing procedures into simple AI-driven automated processes. The AI model adjusts and optimizes numerous testing parameters automatically, allowing novice users to achieve high productivity without needing to understand or manually configure complex testing parameters.
3Extent of automation
If existing machine learning solutions are used for penetration testing, then some automation is achieved, but effective automation of pentesting actions is not realized
Solution Approach 1:
The penetration testing system implements dynamics by enabling adaptive, context-aware automation that responds to real-time testing conditions. The AI model dynamically generates and adjusts test scenarios based on system responses, automatically chains together tools based on intermediate results, and adapts the testing approach to optimize productivity, moving beyond static pre-programmed automation to achieve both high automation extent and effective testing output.
Data Source
AI summary
In an example embodiment, a solution is provided for an automated penetration testing system using pre-trained large language models (LLMs), which come equipped with a knowledge base of existing system configurations and commands. The knowledge base of these pre-trained LLMs may be extended with new training data, thereby updating their knowledge with new configuration commands, systems, and so forth.