AI Exploit Generation for Real-Time Zero-Day Vulnerability Assessment

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Solution Overview

Problem

Existing methods for determining computing platform security are prone to inaccuracies, outdated information, and manual errors, leading to delayed vulnerability detection and exploitation, especially with zero-day vulnerabilities and changing attack surfaces, which can result in data breaches and system compromises.

Innovation Solution

Utilizing machine learning models to generate exploits, patches, and monitoring scripts to proactively identify and mitigate vulnerabilities, providing real-time security assessments through graphical user interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual techniques are employed to determine security vulnerabilities, then network engineers can assess system security, but errors and subjective opinions lead to inaccuracies and delays in vulnerability detection

Engineering Contradiction:

Improvevulnerability detection accuracyVSAvoidtime to detect and respond to vulnerabilities

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical assessment techniques with automated machine learning models that process multi-modal data systems. These models objectively analyze security vulnerabilities without human error or subjectivity, significantly improving detection accuracy and reducing response time by eliminating manual review processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service through automated vulnerability assessment where the machine learning models independently analyze security data, generate assessments, and provide recommendations without requiring continuous human intervention. This autonomous operation accelerates vulnerability detection while maintaining consistent accuracy.

Inventive Principle:

Principle #25Self-service

2Reliability

If publicly available information is used to assess computing platform security, then network engineers can make security decisions, but the information is outdated and contains inaccuracies

Engineering Contradiction:

Improvesecurity information reliabilityVSAvoidtimeliness of security vulnerability information

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The patent implements preliminary action by proactively detecting and assessing security vulnerabilities before they can be exploited by attackers. The machine learning models continuously monitor and analyze security data in advance, providing early warnings and enabling preventive measures rather than reactive responses to outdated public information.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms where machine learning models continuously process new security data, update their assessments, and refine their predictions based on emerging threats. This real-time feedback loop ensures security information remains current and accurate, unlike static public sources.

Inventive Principle:

Principle #23Feedback

3Productivity

If network engineers manually prioritize security vulnerabilities, then resource allocation can be determined, but differing opinions and time consumption delay critical vulnerability remediation

Engineering Contradiction:

Improvevulnerability remediation speedVSAvoidtime to prioritize and address vulnerabilities

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent transforms the subjective parameter of vulnerability prioritization into objective quantitative parameters using machine learning models. The system analyzes multiple data dimensions including vulnerability severity, exploitation risk, and system criticality to automatically rank vulnerabilities, eliminating human opinion differences and accelerating remediation decisions.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The machine learning models serve as an intermediary between vulnerability detection and remediation execution. This automated intermediary objectively prioritizes vulnerabilities based on analyzed data, removing the need for human engineers to manually debate and decide priority, thereby significantly reducing the time from detection to remediation.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Adaptability or versatility

If attackers use artificial intelligence to mask computing platform updates, then advanced detection techniques can be evaded, but new threats emerge that require automated countermeasures

Engineering Contradiction:

Improveability to detect evolving threatsVSAvoidcomplexity of security assessment system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamics by using machine learning models that continuously adapt to evolving threats and attack patterns. The system dynamically updates its detection capabilities based on new data, enabling it to counter AI-masked updates and emerging threats while maintaining manageable complexity through automated learning rather than manual rule updates.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12475235B2Generative cybersecurity exploit discovery and evaluation

Publication Date: 2025.11.18 CITIBANK N A

AI summary

Described herein are systems and methods for discovering and proactively mitigating previously unknown security vulnerabilities. The systems and methods herein can utilize security vulnerability information to discover potential security threats and can utilize this information to generate an attack using a machine learning model, such as a large language model. Generated attacks can be carried out to assess impact of a security vulnerability. An output can be provided that represents the assessed impact. In some implementations, the systems and methods herein generate patches or other mitigations for security vulnerabilities, which can be tested and deployed to address security vulnerabilities.