Progressive Fuzz Testing for Network Protocol Vulnerability Convergence
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Solution Overview
Problem
Current methods for testing network protocol stack vulnerabilities, such as brute force black box fuzz testing, are resource-intensive and inefficient due to the large parameter space of protocols, fields, and field values, making it difficult to identify specific vulnerabilities effectively.
Innovation Solution
The method involves dividing the initial protocol field and field value space into regions, assigning vulnerability ratings, and performing progressive fuzz testing to prioritize and update ratings, allowing for targeted testing of sub-regions with higher vulnerability ratings, thereby reducing the parameter space and focusing on potentially vulnerable areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force black box fuzz testing is performed to test all possible combinations of protocols, fields, and field values, then comprehensive vulnerability coverage is achieved, but resource consumption and testing time increase significantly
Solution Approach 1:
The patent segments the large parameter space into smaller, manageable regions by dividing the protocol field and field value space into distinct regions. Each region represents a subset of possible parameter combinations, allowing the testing system to focus on specific areas rather than exhaustively testing all combinations. This segmentation enables the system to identify and concentrate resources on high-risk regions while reducing overall testing complexity.
Solution Approach 2:
The patent dynamically changes testing parameters by adjusting the parameter space based on vulnerability ratings. The system starts with a comprehensive parameter space, identifies high-risk regions through initial testing, and then concentrates subsequent testing efforts on those specific regions with modified parameter ranges. This parameter adaptation allows the system to maintain high vulnerability coverage while significantly reducing the effective parameter space that requires exhaustive testing.
2Productivity
If the parameter space is reduced to converge on vulnerable combinations, then testing efficiency improves, but the ability to detect all vulnerabilities may be compromised
Solution Approach 1:
The patent implements a feedback mechanism through vulnerability ratings that guide the parameter space reduction process. Initial fuzz testing results generate vulnerability ratings for different regions, which feedback into the parameter space selection for subsequent testing rounds. This feedback loop ensures that the parameter space is reduced based on actual vulnerability evidence rather than arbitrary constraints, maintaining detection completeness while improving efficiency. The system continuously adapts the parameter space based on accumulated testing feedback.
Solution Approach 2:
The patent performs preliminary fuzz testing to establish baseline vulnerability ratings before conducting the main convergence testing. This preliminary action identifies high-risk regions in advance, allowing the system to pre-constrain the parameter space for subsequent testing rounds. By performing this initial exploration and using its results to guide future testing, the system ensures that no critical vulnerability regions are missed while significantly reducing the overall testing scope.
3Measurement precision
If fuzz testing is performed on all regions with equal intensity, then uniform vulnerability assessment is achieved, but resources are wasted on low-risk areas
Solution Approach 1:
The patent applies local quality by assigning different testing intensities and resource allocations to different regions based on their vulnerability ratings. High-risk regions receive intensified testing with more comprehensive parameter combinations and repeated testing rounds, while low-risk regions receive minimal or no additional testing. This localized quality adjustment ensures that resources are concentrated where they provide maximum value, achieving precise vulnerability assessment in critical areas without wasting resources on already-assessed low-risk areas.
Data Source
AI summary
A method for progressive convergence on network protocol stack vulnerabilities includes defining an initial protocol field and field value space for fuzz testing of a network communications protocol stack implementation. The method further includes dividing the initial space into regions corresponding to combinations of protocol fields and field values. The method further includes assigning vulnerability ratings to at least some of the regions. The method further includes executing fuzz testing of the network communications protocol stack implementation using the protocol fields and field values corresponding to the regions. The method further includes updating the vulnerability ratings of the regions based on results of the testing. The method further includes identifying, based on the updated vulnerability ratings, at least one region with a higher vulnerability rating than other regions. The method further includes performing fuzz testing for the sub-regions.