Autonomous Penetration Testing System for Software Vulnerability Detection
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Solution Overview
Problem
Manual penetration testing is labor-intensive and time-consuming, making it difficult to efficiently identify and address software vulnerabilities across large numbers of public-facing applications, especially for companies with thousands of IP addresses and applications.
Innovation Solution
An autonomous penetration testing system that uses threat models and machine learning to automatically develop and execute test plans, prioritizing applications based on risk scores, and mapping test technologies to vulnerabilities for automated execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual penetration testing is performed by human testers, then testing can be performed with adaptable logic and experience-based decision making, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs penetration testing autonomously without requiring human testers to execute each test manually. The automated system services itself by generating test plans, selecting test cases, executing tests, and analyzing results automatically, thereby eliminating labor-intensive manual operations while maintaining testing effectiveness through programmable adaptability
Solution Approach 2:
The system changes the state of penetration testing from manual human execution to automated computer-execution by modifying key parameters such as test execution speed, test volume capacity, and decision-making logic. This parameter transformation enables the system to process thousands of test cases efficiently while incorporating adaptable logic through software algorithms that simulate human decision-making
2Reliability
If comprehensive penetration testing is performed on thousands of applications, then security vulnerabilities can be identified, but the time and resources required become prohibitively large
Solution Approach 1:
The system segments the large-scale penetration testing task into smaller, manageable components by dividing thousands of applications into groups or batches. Each application or application group is tested independently through automated execution of test plans, allowing comprehensive security coverage across all applications while reducing the time required for each individual testing engagement through parallelized automated processing
Solution Approach 2:
The system performs preliminary actions by automatically generating test plans and selecting appropriate test cases before execution begins. Threat models are created in advance, and test configurations are prepared beforehand, enabling the automated system to immediately execute comprehensive testing across multiple applications without requiring manual setup for each engagement, thereby significantly reducing overall testing duration
3Manufacturing precision
If consistent testing procedures are maintained across multiple applications, then testing quality is improved, but the complexity of managing test procedures increases
Solution Approach 1:
The system implements universal test plans that can be applied across multiple applications and engagement types. A single automated testing framework handles diverse testing scenarios including application security, infrastructure security, and cloud security through standardized yet adaptable test procedures. This universality ensures consistent testing quality across all applications while the automated system manages procedure complexity internally, eliminating the need for manual coordination of consistent procedures across multiple engagements
Data Source
AI summary
A method that includes obtaining threat model data associating at least one actor with an application. The at least one actor being capable of taking advantage of at least one potential vulnerability associated with the application. The method includes associating at least one technology with the at least one potential vulnerability based at least in part on the at least one actor, formulating a test based at least in part on the at least one technology, instructing a processor to perform the test on the application, and receiving results from the processor after performance of the test.