Code vulnerability evaluator

Patent metadata

Field	Value
Publication number	US-12585785-B2
Application number	US-202418581652-A
Country	US
Kind code	B2
Filing date	Feb 20, 2024
Priority date	Feb 20, 2024
Publication date	Mar 24, 2026
Grant date	Mar 24, 2026

Abstract

A computing platform provides code vulnerability detection and evaluation. The computing platform may use a quantum graph categorizer along with quantum polymorphism execution channels to reduce false positives and provide optimized real-time vulnerability evaluation of code and code segments. The computing platform may use a hybrid approach comprising a mix of static and dynamic vulnerability validation and real-time parallel technique execution.

First claim

What is claimed is:

1 . A computing platform comprising:
at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive data related to code vulnerabilities; generate a quantum graph network using a quantum graph categorizer and the received data related to code vulnerabilities, wherein the quantum graph network is a dynamic quantum-graph network supported by a quantum deep embedding neural network (QDENN) and a graphical neural artificial intelligence engine; determine a reconciliation ratio, wherein the reconciliation ratio is a ratio of vulnerability scoring between static and dynamic vulnerability analysis; based on the reconciliation ratio, determine a vulnerability test case execution plan; and execute the determined vulnerability test case execution plan to determine code vulnerabilities, wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises using quantum entanglement to ensure evaluation results of one set of test cases are reconciled with other test results to flag any discrepancy.

2 . The computing platform of claim 1 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to convert the received data related to the code vulnerabilities to quantum qubits.

3 . The computing platform of claim 2 , wherein the determined reconciliation ratio further includes determining a static vulnerability score, a hybrid vulnerability score, and a dynamic vulnerability score.

4 . The computing platform of claim 3 wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform, to compare the static vulnerability score to the dynamic vulnerability score.

5 . The computing platform of claim 4 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to modify the determined vulnerability test case execution plan to contain additional static code checks if the determined static vulnerability test score is greater than the dynamic vulnerability score.

6 . The computing platform of claim 5 , wherein static code checks comprise analyzing code syntax and patterns, parsing and abstract tree generation, and predefined rule-based analysis.

7 . The computing platform of claim 4 , wherein the memory stores additional computer-readable instructions that, when executed by the at least one processor, cause the computing platform to modify the determined vulnerability test case execution plan to contain additional dynamic code checks if the determined dynamic vulnerability test score is greater than the static vulnerability score.

8 . The computing platform of claim 7 , wherein the code checks comprise code instrumentation, fuzzy testing, monitoring and data collection, dynamic memory analysis, taint analysis, and sanitizers, and wherein the code checks are executed using quantum polymorphism execution channels that enable dynamic virtual copies of the code to be executed in parallel and reconciled using quantum entanglement to ensure evaluation results of one set of test cases are reconciled with other test results to flag any discrepancy and eliminate false positives.

9 . The computing platform of claim 1 , wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises quantum polymorphism execution channels to enable dynamic virtual copies of the code to be executed in parallel and reconciled for the vulnerability test case execution plan.

10 . A method comprising:
at a computing platform comprising at least one processor, a communication interface, and memory:
receiving data related to code vulnerabilities;
generating a quantum graph network using a quantum graph categorizer and the received data related to code vulnerabilities, wherein the quantum graph network is a dynamic quantum-graph network supported by a quantum deep embedding neural network (QDENN) and a graphical neural artificial intelligence engine;
determining a reconciliation ratio, wherein the reconciliation ratio is a ratio of vulnerability scoring between static and dynamic vulnerability analysis;
based on the reconciliation ratio, determining a vulnerability test case execution plan; and
executing the determined vulnerability test case execution plan to determine code vulnerabilities, wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises using quantum entanglement to ensure evaluation results of one set of test cases are reconciled with other test results to flag any discrepancy.

11 . The method of claim 10 further comprising causing the computing platform to convert the received data related to the code vulnerabilities to quantum qubits.

12 . The method of claim 11 further comprising: determining that the reconciliation ratio further includes determining a static vulnerability score, a hybrid vulnerability score, and a dynamic vulnerability score.

13 . The method of claim 12 , further comprising comparing the static vulnerability score to the dynamic vulnerability score.

14 . The method of claim 13 , wherein modifying the determined vulnerability test case execution plan to contain additional static code checks if the determined static vulnerability test score is greater than the dynamic vulnerability score.

15 . The method of claim 13 , further comprising modifying the determined vulnerability test case execution plan to contain additional dynamic code checks if the determined dynamic vulnerability test score is greater than the static vulnerability score, wherein the additional dynamic code checks comprise fuzzing tests and taint analysis, wherein modifying the determined vulnerability test case execution plan is based on multiple predefined criteria including type of application, previous history, and development/deployment stage, and wherein the additional dynamic code checks are executed using quantum polymorphism execution channels that enable parallel execution on virtual copies of the code and use quantum entanglement to reconcile evaluation results and flag discrepancies to reduce false positives.

16 . The method of claim 10 , wherein executing the determined vulnerability test case execution plan to determine code vulnerabilities comprises quantum polymorphism execution channels to enable dynamic virtual copies of the code to be executed in parallel and reconciled for the vulnerability test case execution plan.

17 . One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:
receive data related to code vulnerabilities; generate a quantum graph network using a quantum graph categorizer and the received data related to code vulnerabilities, wherein the quantum graph network is a dynamic quantum-graph network supported by a quantum deep embedding neural network (QDENN) and a graphical neural artificial intelligence engine; determine a reconciliation ratio, wherein the reconciliation ratio is a ratio of vulnerability scoring between static and dynamic vulnerability analysis; based on the reconciliat

Assignees

• Bank Of America

Inventors

• Taneja Pushkar
• Dashputra Yash Pant
• Bakshi Sakshi
• Reddy Amar Deep

Classifications

• G06F2221/033

  Test or assess software · CPC title

• G06F21/577 Primary

  Assessing vulnerabilities and evaluating computer system security · CPC title