Web Application Vulnerability Detection via Input Scoring
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Solution Overview
Problem
Detecting vulnerabilities in web application input forms is challenging due to numerous inputs, making it difficult to determine which inputs are of interest and potentially exploitable, as existing solutions like user-configurable settings or brute force methods are incomplete or inefficient.
Innovation Solution
A system that uses a classifier to inspect input sequences, categorize inputs based on predetermined conditions, score them, and identify parameters of interest, determining vulnerabilities based on the highest scored inputs, with the ability to revise categories and scoring weights over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force methods are used to detect vulnerabilities in web application input forms, then comprehensive coverage of all inputs is achieved, but efficiency and resource consumption deteriorate
Solution Approach 1:
The system performs self-learning by automatically analyzing input sequences and categorizing parameters during the scanning process. The classifier improves its accuracy over time by learning from observed patterns in the web application's behavior, eliminating the need for manual configuration while maintaining high detection completeness and efficiency.
Solution Approach 2:
The system dynamically changes the parameters being tested by selectively focusing on categorized parameters of interest rather than uniformly testing all inputs. This parameter selection approach, guided by the classifier, maintains comprehensive vulnerability coverage while significantly improving scan efficiency by avoiding redundant tests on non-critical inputs.
2Adaptability or versatility
If user configurable settings are used to identify parameters of interest, then customization to specific application needs is improved, but completeness and automation deteriorate
Solution Approach 1:
The system performs self-learning by automatically analyzing input sequences and categorizing parameters during the scanning process. The classifier improves its accuracy over time by learning from observed patterns in the web application's behavior, eliminating the need for manual configuration while maintaining high detection completeness and efficiency.
Solution Approach 2:
The system performs preliminary analysis of input sequences to automatically categorize parameters before the main vulnerability scanning begins. This preliminary categorization action enables the system to automatically identify parameters of interest without requiring user configuration, while still allowing optional customization if needed.
3Reliability
If all inputs in web application input forms are analyzed equally, then comprehensive vulnerability detection is achieved, but identification of critical parameters deteriorates
Solution Approach 1:
The system applies different analysis quality levels to different inputs based on their categorized importance. Parameters identified as 'parameters of interest' through the classifier receive focused, detailed analysis, while other inputs are processed more efficiently. This local quality differentiation maintains comprehensive coverage while precisely identifying critical parameters.
Solution Approach 2:
The system performs partial analysis on all inputs to ensure comprehensive coverage, then applies excessive (detailed) analysis only to the subset of parameters identified as most critical by the classifier. This approach ensures no vulnerabilities are missed while concentrating resources on identifying and analyzing the most important parameters.
Data Source
AI summary
Examples relate to detecting vulnerabilities in a web application. One example enables identifying a set of inputs in a web application input form. The set of inputs may be categorized based on a set of predetermined conditions. The set of inputs may be scored based on the categorization. A subset of the set of inputs may be determined to be a set of parameters of interest for the web application based on the scored set of inputs.