Web applications commonly process user-controlled input and perform backend database operations. If application input reaches database query processing without sufficient separation between application data and query logic, an attacker may manipulate the intended database operation through SQL Injection.
OWASP Juice Shop provides an intentionally vulnerable web application environment containing SQL Injection scenarios that can be used for controlled security testing and vulnerability validation.
In this use case, an existing OWASP Juice Shop application is deployed as a controlled Docker container in an Ubuntu virtual machine. The containerized application provides the target environment for reproducing the SQL Injection vulnerability before and after AI-assisted remediation.
Kali Linux is used as the controlled penetration-testing system.
A controlled SQL Injection Attack assessment is performed against the authorized laboratory application. The assessment first identifies the exposed application and selected SQL Injection functionality and then validates the vulnerable behavior using controlled security-testing inputs.
sqlmap is used as the primary SQL Injection penetration-testing tool because it is specifically designed to identify and validate SQL Injection vulnerabilities in web applications. Nmap is used for service discovery and network exposure assessment.
Semgrep is used for static security analysis of the vulnerable and patched source code. Docker is used to deploy and redeploy the vulnerable and patched application in isolated containers.
The SVM model is used to classify source-code samples according to SQL Injection-related vulnerability patterns. CodeT5+ 220M is used to transform identified vulnerable source code toward a security-improved implementation.
The public SQL Injection vulnerability-fix dataset is processed to obtain vulnerable and corresponding fixed source-code examples for vulnerability classification and vulnerable-to-fixed code transformation.
The generated security patch is then applied to a separate application source-code copy. A patched Docker image is built and deployed, and the same controlled SQL Injection assessment is repeated to determine whether the previously demonstrated vulnerability remains reproducible.
Application : OWASP Juice Shop
OWASP Juice Shop is the target intentionally vulnerable web application in this use case.
It provides controlled application functionality containing SQL Injection scenarios that can be used to evaluate vulnerability identification, AI-assisted source-code repair, and post-remediation security behavior.
A web application may process user-controlled input through backend database functionality without sufficiently separating application data from database query logic.
If the vulnerable source-code condition remains present, an attacker may submit crafted input that changes the intended database query behavior and potentially causes unauthorized database operations.
The proposed solution introduces application deployment through Docker, network exposure assessment using Nmap, controlled SQL Injection validation using sqlmap, SVM-based vulnerability classification, CodeT5+ 220M-based code transformation, static security analysis using Semgrep, patched-container deployment, and post-remediation SQL Injection testing.
Specific Attack: SQL Injection Attack
The controlled attack scenario evaluates whether attacker-controlled input can influence database query processing in the selected OWASP Juice Shop functionality.
The objective is to determine whether the selected functionality demonstrates SQL Injection behavior before remediation and whether the same controlled attack condition is prevented or mitigated after the AI-generated security patch is deployed.
AI-Assisted Vulnerability Classification and Code Transformation
The primary security concept is AI-Assisted Vulnerability Classification combined with automated code transformation and controlled penetration testing.
The objective is to identify source-code patterns associated with SQL Injection vulnerabilities and use an AI code-transformation model to generate a candidate security repair for the identified vulnerable source code.
The SVM classification stage identifies source-code samples requiring further vulnerability-repair processing, while CodeT5+ 220M performs the vulnerable-to-fixed source-code transformation.
The security concept defines the overall security strategy.
The following are the specific defensive mechanisms implemented.
OWASP Juice Shop is deployed in a controlled Docker container so that the vulnerable and patched application states can be reproduced independently.
The SVM model classifies prepared source-code samples according to SQL Injection-related vulnerability patterns learned from the public vulnerability-fix data.
CodeT5+ 220M processes the identified vulnerable source-code region and generates a candidate security-repaired implementation.
Vulnerable and corresponding fixed source-code examples are used to support transformation from insecure implementation toward a security-improved implementation.
Semgrep is used to inspect vulnerable and patched source code for SQL Injection-related patterns and unintended source-code changes.
sqlmap is used to perform controlled SQL Injection testing against the authorized laboratory application.
Nmap is used to identify the network-accessible application service and establish the initial application attack surface.
A new Docker image is created from the patched source code and deployed as the remediated application environment.
The patched application is tested using legitimate application functionality after remediation.
The vulnerable and patched application states are compared using static analysis, SQL Injection testing, application execution, and source-code changes.
sqlmap is the primary penetration-testing tool because it is specifically designed for controlled SQL Injection detection and validation against web applications.
Nmap is used to identify the network-accessible application service and establish the initial attack surface.
OWASP Juice Shop provides the existing vulnerable application used for controlled SQL Injection testing and AI-assisted vulnerability remediation.
Docker is used to deploy the vulnerable and patched application states as separate controlled containers.
Semgrep is used to inspect vulnerable and patched source code for security-relevant patterns.
SVM is used as the machine-learning classification algorithm for identifying source-code samples associated with SQL Injection vulnerability patterns.
CodeT5+ 220M is used as the AI model for vulnerable-to-fixed source-code transformation and candidate security-patch generation.
Python is used for public vulnerability-fix data processing, feature preparation, SVM processing, CodeT5+ 220M model interaction, evaluation, and workflow automation.
Kali Linux provides the controlled penetration-testing environment.
Ubuntu provides the controlled environment for Docker, source-code processing, AI experimentation, static analysis, and application execution.
VirtualBox provides the isolated laboratory environment for separating the Ubuntu application environment and Kali Linux testing environment.
An OWASP Juice Shop application is successfully deployed in an isolated Docker environment on Ubuntu for controlled SQL Injection security testing and AI-assisted vulnerability remediation.
The legitimate application functionality and network exposure are established using the deployed Docker application and Nmap service discovery.
A controlled SQL Injection assessment is performed using sqlmap against the selected application functionality, establishing the vulnerable-state security baseline.
The public SQL Injection vulnerability-fix dataset is processed to obtain vulnerable and corresponding fixed source-code examples for SVM classification and CodeT5+ 220M-based vulnerable-to-fixed transformation.
The SVM model is trained and evaluated using the prepared vulnerability-related source-code data and is used to classify the selected target source-code region.
The CodeT5+ 220M model processes the identified vulnerable source code and generates a candidate security repair based on the prepared vulnerable-to-fixed transformation data.
The generated patch is applied to a separate source-code copy, analyzed using Semgrep, and used to create a patched Docker image for isolated runtime validation.
The patched Docker application is subjected to the same Nmap and sqlmap security-testing process used during the initial assessment to determine whether the SQL Injection behavior remains reproducible.
The legitimate application functionality is validated after remediation to confirm that the AI-generated security repair does not unnecessarily prevent the intended application operation.
The complete Docker deployment, Nmap service discovery, sqlmap SQL Injection testing, SVM vulnerability classification, CodeT5+ 220M code transformation, Semgrep static analysis, patched-container deployment, post-remediation testing, and before-and-after security-validation workflow is successfully demonstrated.
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