AI-Hardened Computers with Context-Aware Vulnerability Remediation
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Solution Overview
Problem
Conventional software analysis tools lack context and require manual intervention to correct identified security vulnerabilities, leading to inefficiencies and high false positive rates.
Innovation Solution
A system utilizing a large language model to generate a security test tool that checks for vulnerabilities and generates modified source code to eliminate these vulnerabilities, combined with a cyber reasoning system to validate and refine fixes, minimizing false positives through context-aware analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional software analysis tools are used to identify security vulnerabilities, then vulnerability detection is performed, but false positive rates increase and manual intervention is required
Solution Approach 1:
The system enables self-service by implementing an automated feedback loop where the AI model generates code fixes, the evaluation engine validates them, and the model learns from the results. This eliminates manual intervention requirements while improving vulnerability detection accuracy through continuous self-improvement.
Solution Approach 2:
The system implements feedback by using the evaluation engine to assess generated code fixes and feed this information back to the AI model. This closed-loop feedback mechanism reduces false positives by continuously refining the model's vulnerability detection and remediation capabilities based on actual evaluation results.
2Loss of information
If conventional software analysis tools are used, then code issues are identified, but actionable insights are lacking and remediation requires manual effort
Solution Approach 1:
The system performs preliminary action by automatically generating code fixes before manual remediation is attempted. The AI model proactively creates remediation code based on identified vulnerabilities, which is then evaluated and applied, significantly improving remediation efficiency while preserving full context information.
Solution Approach 2:
The evaluation engine serves as an intermediary between vulnerability detection and manual remediation. It automatically evaluates generated code fixes for correctness and safety, acting as a mediator that validates AI-generated solutions before they are applied, thereby maintaining high productivity and preventing information loss.
3Productivity
If automated code generation is implemented to fix vulnerabilities, then remediation speed increases, but false positives may increase without proper validation
Solution Approach 1:
The system implements dynamics by making the code generation and validation process adaptive. The AI model dynamically generates fixes based on vulnerability context, and the evaluation engine dynamically assesses each fix's accuracy. This dynamic approach maintains high remediation speed while ensuring fix accuracy through real-time validation.
Solution Approach 2:
The system applies beforehand cushioning by implementing the evaluation engine to validate code fixes before they are deployed. This prior validation cushion prevents false positives from reaching production, ensuring reliability is maintained while preserving the high remediation speed provided by automated code generation.
Data Source
AI summary
Artificial intelligence hardened computers (e.g., using a computerized tool), is enabled. For example, a method can comprise scanning, by a system comprising at least one processor, a database for information about a program, in response to scanning the database, determining, by the system, the information about the program, wherein the information about the program comprises: source code information representative of a source code of the program, and context information representative of a context applicable to the source code, based on the information about the program, generating, by the system, a security test tool, wherein the security test tool is configured to check for a security vulnerability of the program, and in response to determining the security vulnerability, generating, by the system, modified source code based on the source code and based on the security vulnerability, wherein the modified source code does not comprise the security vulnerability.