Inline Vulnerability Detection for Language Model Generated Code

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Language models may generate insecure program code with vulnerabilities, posing risks if incorporated into software projects.

Innovation Solution

Implement an inline detection system that monitors network traffic for program code generated by language models, applies machine learning and rule-based techniques to analyze the code for vulnerabilities, and blocks or alerts on detected vulnerabilities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If language models are used to generate program code, then productivity is improved, but security is worsened due to potential vulnerabilities in generated code

Engineering Contradiction:

Improvecode generation efficiencyVSAvoidcode security

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary vulnerability detection on code generated by language models before the code is deployed or executed. The detection service analyzes the generated code using multiple techniques including machine learning models and rule-based approaches to identify security issues in advance, preventing vulnerable code from being incorporated into software projects.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary detection service that sits between the language model code generation process and the final software deployment. This intermediary service acts as a security gatekeeper, analyzing generated code for vulnerabilities and either blocking problematic code or alerting developers before the code is integrated into the final product.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If multiple vulnerability detection techniques are applied to generated code, then security is improved, but device complexity increases

Engineering Contradiction:

Improvevulnerability detection accuracyVSAvoiddetection system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The detection service segments the vulnerability detection process into distinct modules: machine learning-based detection using pre-trained models, rule-based detection using predefined security rules, and pattern-based detection using regular expressions. Each segment handles specific types of vulnerability patterns, allowing the system to comprehensively analyze code while maintaining manageable complexity through modular organization.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20260106885A1Inline vulnerability detection for program code generated by a language model

Publication Date: 2026.04.16 PALO ALTO NETWORKS INC

AI summary

Vulnerable program code generated by a language model is detected inline. A detection service monitors for and intercepts network traffic of a language model. For responses from the language model that include generated program code, the detection service extracts the program code and determines a programming language to which the program code corresponds. The detection service employs machine learning-based and/or rule-based vulnerability detection techniques to determine whether the program code comprises any vulnerabilities. If the program code is determined to comprise a vulnerability, the detection service can block transmission of the response comprising the program code to its destination or communicate an alert with the response indicating that the program code is vulnerable to prevent inclusion of the vulnerable program code in a software project.