ML Exploit Prediction for Vulnerability Prioritization
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Solution Overview
Problem
Current methods are inadequate in predicting whether an exploit will be developed for a vulnerability and if it will be used in an attack, leading to increased risk exposure for computing assets, as they rely on waiting for exploits to be developed and attacks to occur before remediation.
Innovation Solution
Implementing a machine learning-based system that uses predictive models to forecast the likelihood of exploit development and usage based on features such as prevalence, exploit development time, and attack history, allowing for prioritization and remediation before actual exploits are developed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vulnerability remediation is prioritized based on existing exploit usage history, then resources are allocated to known attack vectors, but vulnerabilities with potential exploits are not addressed proactively
Solution Approach 1:
The system performs preliminary analysis of vulnerability characteristics before exploits are developed. By predicting which vulnerabilities are most likely to be exploited based on their code properties, complexity, and historical patterns, the system enables proactive remediation before actual attacks occur, resolving the contradiction between waiting for exploit evidence and acting proactively.
Solution Approach 2:
The patent applies machine learning models to rapidly assess large numbers of vulnerabilities without waiting for exploit development. This allows the system to skip the traditional sequential process of waiting for exploit creation and attack observation, directly prioritizing remediation based on predicted exploit likelihood.
2Reliability
If all vulnerabilities are remediated simultaneously, then comprehensive security is achieved, but resource constraints prevent timely remediation of all vulnerabilities
Solution Approach 1:
The system applies different remediation priorities to different vulnerabilities based on their individual characteristics. Instead of uniform treatment, the machine learning model assesses each vulnerability's specific properties (code complexity, exploitability indicators, affected system criticality) to assign localized priority levels, enabling efficient resource allocation that maximizes security impact.
Solution Approach 2:
The patent transforms vulnerability assessment from binary (patched/unpatched) to a continuous prioritization scale based on predicted exploit likelihood. By changing the parameter of vulnerability assessment from simple presence/absence to a risk probability spectrum, the system enables nuanced resource allocation that addresses the most critical vulnerabilities first.
3Measurement precision
If vulnerability prioritization waits for exploit development, then remediation is based on proven attack effectiveness, but computing assets remain exposed during the waiting period
Solution Approach 1:
The system performs preliminary risk assessment using machine learning predictions before exploits are developed. By analyzing vulnerability characteristics such as code complexity, exploitability indicators, and historical patterns, the system can accurately predict which vulnerabilities are most likely to be exploited, enabling proactive risk mitigation before actual attacks occur.
Solution Approach 2:
The system continuously refines its predictions by incorporating feedback from observed exploit development patterns and attack success rates. This feedback mechanism allows the risk assessment model to improve its accuracy over time, enabling more precise prediction of exploit likelihood while maintaining proactive security posture.
Data Source
AI summary
Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.