Damn Vulnerable Web Application (DVWA) is a PHP-based web application designed to study and assess common web application security vulnerabilities. Its PHP source code provides a suitable environment for examining how code modifications can affect existing security controls and introduce potential security weaknesses.
Security regression occurs when a source-code modification, patch reversal, or incorrect implementation weakens an existing security control or reintroduces a previously addressed vulnerability. In PHP applications, changes to user-input validation, SQL query construction, authentication checks, and output encoding may introduce security regression risks if they are not reviewed and tested properly.
In this use case, DVWA is used as the target application to investigate security regression risks in PHP source-code changes. Static code features are extracted from secured and modified code versions, while vulnerability history analysis helps identify changes associated with known security weaknesses. A public cybersecurity dataset containing suitable labelled vulnerability-related records is used to develop and evaluate the machine learning model.
A Gradient Boosting classifier is developed using Python and Scikit-learn to predict whether prepared PHP code-change features indicate a potential security regression risk. Git is used to compare source-code versions, and Semgrep CE performs static analysis to identify potentially unsafe PHP coding patterns. The extracted features are processed into a consistent format before being supplied to the trained model.
Controlled security testing is conducted against the locally deployed DVWA application to support the investigation of suspected weaknesses. OWASP ZAP is used to examine application behaviour, while the static analysis results, source-code changes, vulnerability history, and model predictions are correlated for further verification
A Streamlit dashboard presents prediction results, model evaluation metrics, source-code change information, static analysis findings, and validation status. The model's performance on the public dataset is evaluated separately from its ability to identify regression risks in DVWA, ensuring that the assessment does not overstate the effectiveness of the model.
DVWA PHP Application → Source-Code Version Identification → Git-Based Code Comparison → Static Code Feature Extraction → Vulnerability History Analysis → Public Cybersecurity Dataset Preprocessing → Gradient Boosting Model Training and Evaluation → Semgrep CE Static Analysis → Controlled Application Security Testing → Security Regression Risk Prediction → Finding Correlation and Validation → Streamlit Security Dashboard
Application/Service : DVWA (Damn Vulnerable Web Application)
DVWA is a PHP-based web application used as the target environment for analysing source-code security weaknesses and examining how code modifications may affect existing security controls.
PHP source-code modifications can introduce security weaknesses when developers change input validation, database query handling, authentication checks, or output encoding without adequately reviewing the security implications.
Previously addressed vulnerabilities may reappear when corrective changes are removed, modified incorrectly, or replaced with insecure implementations. Conventional static analysis tools can identify suspicious coding patterns, but individual findings do not always establish whether a particular code change represents a security regression.
Manual source-code comparison and vulnerability history review can also make it difficult to consistently prioritise large numbers of changes. Without a systematic approach, potentially risky modifications may require additional investigation before their security impact becomes clear.
DVWA PHP Application
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Existing Security Controls
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PHP Source-Code Modification or Patch Reversal
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Potentially Unsafe Code Changes
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Weakening of Existing Security Protections
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Potential Security Regression
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Increased Application Vulnerability Risk
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Need for Automated Regression Risk Prediction
The propsed solution Implement a Gradient Boosting-based prediction mechanism that analyses static code features and vulnerability history associated with PHP source-code changes. Combine Git-based comparison, Semgrep CE static analysis, public cybersecurity dataset training, and controlled DVWA security testing to identify and investigate potential security regression risks.
Specific Attack : Security Regression Through Vulnerable PHP Code Modification
This use case examines how modifying or reversing security-related PHP code changes can introduce or reintroduce vulnerabilities in DVWA. The assessment focuses on controlled changes to vulnerable code patterns, such as unsafe input handling, SQL query construction, authentication checks, or output encoding, within the authorised local testing environment.
The objective is to determine whether static code features and vulnerability history can help the Gradient Boosting model identify changes associated with increased security regression risk. The assessment also compares model predictions with static analysis findings and application testing evidence.
Machine Learning-Based Security Regression Risk Prediction Using Static Code Features and Vulnerability History
The proposed mechanism combines static code feature extraction, source-code change analysis, vulnerability history, and Gradient Boosting classification to estimate the potential security regression risk associated with PHP code modifications.
Git identifies differences between source-code versions, while Python-based feature extraction converts relevant code-change characteristics into structured model inputs. Vulnerability history provides additional information about previously identified security weaknesses when suitable historical records are available.
A public cybersecurity dataset containing suitable labelled vulnerability-related records is used for model training and evaluation. Gradient Boosting learns classification patterns from the prepared training data and predicts the potential regression risk of compatible feature records extracted from the target code changes.
Semgrep CE provides independent static analysis evidence, and OWASP ZAP supports controlled application-level validation. The model prediction is treated as a risk indicator rather than proof that a vulnerability exists. Dataset labels, extracted features, and model inputs must be compatible, and the limitations of applying a dataset-trained model to DVWA code must be documented.
PHP Application Review
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Security Control Identification
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Source-Code Version Comparison
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Static Code Feature Extraction
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Vulnerability History Analysis
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Public Dataset Preparation
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Gradient Boosting Model Training
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Model Performance Evaluation
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Modified PHP Code Feature Preparation
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Security Regression Risk Prediction
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Static and Dynamic Security Validation
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Finding Correlation and Risk Assessment
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Corrective Changes and Final Validation
Git is used to compare reference and modified PHP source-code versions and identify changes that may affect existing security controls.
Semgrep CE analyses PHP source code using applicable security rules to identify suspicious input handling, unsafe query construction, and other relevant coding patterns.
Python processes source-code differences and extracts suitable code-change characteristics for machine learning analysis.
Available vulnerability records and source-code history are examined to identify patterns associated with previously reported or corrected security weaknesses.
A Gradient Boosting classifier is trained using suitable labelled dataset records and evaluated before being applied to compatible code-change features.
Dataset quality, class distribution, missing values, feature compatibility, data leakage, and training and testing separation are examined.
OWASP ZAP is used to examine the locally deployed DVWA application and support controlled validation of suspected application-level weaknesses.
Gradient Boosting predictions are compared with Git differences, Semgrep CE findings, vulnerability history, and available dynamic testing results.
Identified weaknesses are corrected using appropriate secure coding practices, such as input validation, parameterised queries, secure authentication checks, and context-appropriate output encoding.
Source-code comparison, static analysis, and relevant application tests are repeated after corrective changes.
Git maintains source-code history and compares reference and modified PHP code versions.
Semgrep CE scans PHP source code using applicable security rules to identify potentially unsafe coding patterns.
Scikit-learn provides the Gradient Boosting classifier and model evaluation functions.
Python integrates dataset processing, code feature extraction, model training, security finding correlation, and dashboard development.
Pandas and NumPy support structured dataset processing and numerical feature preparation.
A suitable public cybersecurity dataset containing labelled vulnerability-related records is used for model development and evaluation.
DVWA provides the PHP-based application environment for examining security weaknesses and validating selected findings.
OWASP ZAP supports security testing of the locally deployed DVWA web application.
Streamlit provides a browser-based interface for presenting model predictions and security assessment results.
Ubuntu Linux provides the local environment for source-code analysis, Python development, and deployment of the target application and assessment tools.
Kali Linux provides a security testing environment for authorised assessment of the locally deployed DVWA application.
Oracle VirtualBox provides isolated virtual machines for configuring the Ubuntu and Kali Linux testing environments.
Automated Security Regression Risk Prediction Implements a Gradient Boosting classifier to predict potential security regression risks associated with prepared PHP source-code changes.
PHP Source-Code Change Analysis Identifies modifications that may weaken existing security controls through Git-based comparison of reference and modified DVWA code versions.
Static Code Feature Extraction Converts relevant PHP code-change characteristics into structured features suitable for machine learning analysis.
Vulnerability History Analysis Incorporates suitable historical vulnerability information to provide additional context for assessing potential security regression risks.
Machine Learning Performance Evaluation Measures model performance using accuracy, precision, recall, F1-score, and confusion-matrix analysis.
Static Security Finding Identification Uses Semgrep CE to identify potentially unsafe PHP coding patterns and support investigation of suspected regressions.
Dynamic Application Security Validation Uses OWASP ZAP to examine DVWA application behaviour and provide supporting evidence for suspected security weaknesses.
Correlated Security Assessment Results Combines model predictions, source-code changes, static analysis findings, and application testing evidence to support risk assessment.
Security Remediation Verification Repeats relevant source-code analysis and application testing after corrective changes to assess whether identified weaknesses have been addressed.
Interactive Security Reporting Provides a Streamlit dashboard and assessment records containing model predictions, evaluation metrics, findings, remediation status, residual risks, and recommendations.
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