Exploit Combination Analysis for Dynamic Vulnerability Criticality
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Solution Overview
Problem
Existing software applications face challenges in detecting when new vulnerabilities combine with residual software bugs to increase risk, as continued development may introduce new vulnerabilities that compound existing risks, necessitating a method to assess vulnerability criticality.
Innovation Solution
A method and system for creating exploit combinations of known vulnerabilities, inputting new vulnerabilities, determining whether they fit into existing or create new combinations, and updating these combinations based on new vulnerabilities, with risk scoring and automated actions to address increased risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software development continues after residual risk is identified, then new features and functionality are added, but new vulnerabilities are introduced that combine with residual risk to increase overall system risk
Solution Approach 1:
The system performs preliminary risk assessment by creating exploit combinations of known vulnerabilities before new development continues. This allows the system to proactively identify potential security risks that could arise from combining residual vulnerabilities with new code, enabling preventive action rather than reactive response.
Solution Approach 2:
The system implements continuous feedback loops where new vulnerabilities are constantly monitored and combined with existing vulnerability data. This feedback mechanism updates risk assessments in real-time, allowing the system to detect when new vulnerabilities compound existing risks and trigger appropriate responses.
2Reliability
If all software bugs are addressed during development, then software quality is improved, but development time and resources are exceeded
Solution Approach 1:
The system enables automated self-assessment of software vulnerabilities through exploit combination analysis. Rather than requiring manual review of every bug, the system automatically identifies which residual vulnerabilities pose the greatest risk when combined with new code, allowing developers to prioritize fixes based on automated risk scoring.
Solution Approach 2:
The system changes the parameter of risk assessment from static (individual bug severity) to dynamic (combined exploit risk). By evaluating vulnerabilities in the context of potential exploit combinations rather than in isolation, the system can identify which lower-priority bugs actually pose greater risk when combined with other vulnerabilities, enabling more effective prioritization.
3Measurement precision
If residual risk is monitored manually, then risk assessment accuracy is maintained, but detection speed and responsiveness to new vulnerabilities decrease
Solution Approach 1:
The system replaces manual mechanical review processes with automated computational analysis. The exploit combination generator and risk assessor automatically analyze vulnerability interactions using algorithms that can process vast numbers of vulnerability combinations far faster than human reviewers, while maintaining or improving assessment accuracy through systematic evaluation of all possible combinations.
Data Source
AI summary
Methods and systems for determining vulnerability criticality include creating exploit combinations for known vulnerabilities, inputting new vulnerabilities, determining whether the new vulnerabilities fit into an existing exploit combination or create anew exploit combination, and updating the exploit combinations based on the new vulnerabilities.