Modern software applications rely on third-party libraries, frameworks, and packages to provide application functionality. These dependencies can introduce security risks when they contain known vulnerabilities, use outdated versions, or have other security-related weaknesses.
OpenProject is an existing open-source project management application that uses software dependencies to support its functionality. The security of these dependencies is an important consideration because vulnerable components may introduce weaknesses into the application.
Traditional vulnerability scanners identify known vulnerabilities by matching installed or declared package versions against available vulnerability databases. Although this process is useful for identifying known issues, organizations may also require a way to prioritize dependency reviews based on the available package information and historical vulnerability records.
In this use case, OpenProject is used as the target application for software dependency risk analysis. The application is deployed within an isolated Ubuntu and Kali Linux laboratory environment. Existing public package metadata and vulnerability records are used as the primary data sources for the machine-learning stage.
The available dataset is inspected to determine whether it contains suitable package attributes and reliable labels for the intended vulnerability-risk classification task. The selected records are processed into numerical features suitable for the LightGBM machine-learning algorithm.
LightGBM is trained to classify dependency records according to the risk categories supported by the selected dataset. The trained model is evaluated using held-out records to measure its classification performance.
Syft is used to generate a Software Bill of Materials (SBOM) from compatible OpenProject artifacts. OSV-Scanner is used to identify known vulnerabilities in supported dependencies, while Grype or Trivy may be used as an additional vulnerability-scanning tool. The scanner findings provide security evidence that can be compared with the machine-learning predictions.
Python integrates package-data processing, model training, risk prediction, evaluation, and dashboard functionality. Streamlit provides an interactive dashboard that displays dependency information, known vulnerability findings, predicted risk categories, and model evaluation results.
The predicted risk is treated as a prioritization aid rather than proof that a package is vulnerable. Known vulnerability findings are independently verified against available security records.
Application: OpenProject
OpenProject is the target application in this use case. It provides project management functionality and relies on software dependencies to support its operation.
The application is deployed within the authorized laboratory environment. Its available dependency information is examined to identify components that can be assessed against public vulnerability records.
Software applications may contain numerous direct and transitive dependencies. A direct dependency is included by the application itself, while a transitive dependency is introduced through another dependency.
Some dependencies may have publicly documented vulnerabilities. Other components may have limited metadata, outdated versions, or insufficient information to determine their security status immediately.
Traditional vulnerability scanners help identify known vulnerabilities, but security teams may still need to prioritize which packages require further investigation first.
Manual prioritization becomes difficult when the application has a large dependency inventory and the available security information comes from multiple sources.
The proposed solution introduces a LightGBM-based risk-classification workflow that learns patterns from suitable labelled package records and assigns predicted risk categories to compatible dependency records.
Specific Attack: Software Dependency Vulnerability Risk Scenario
The security scenario evaluates whether dependencies used by OpenProject contain known vulnerabilities or exhibit package characteristics associated with the risk categories represented in the training dataset.
The project does not require the creation or execution of a malicious package. Instead, it examines software dependency risk using existing public security data and the actual dependency information available from the target application.
The assessment uses public package metadata, vulnerability records, the generated SBOM, and controlled security-scanning results to evaluate the dependency-risk prediction workflow.
A dependency identified as potentially high risk by the model is reviewed using available vulnerability records and scanner evidence. A high-risk prediction alone does not establish that the component is vulnerable or exploitable.
Machine-Learning-Based Software Dependency Vulnerability-Risk Prediction
The primary security concept is Machine-Learning-Based Software Dependency Vulnerability-Risk Prediction.
The first objective is to establish a suitable package-level dataset using existing public package metadata and vulnerability records. The selected data must contain appropriate features and sufficiently reliable labels to support the intended classification task.
The second objective is to train a LightGBM model using the prepared package features. The model learns relationships represented in the training data and predicts the risk category associated with compatible dependency records.
The third objective is to compare the model predictions with known vulnerability information and security-scanning results. This helps evaluate whether the model can support dependency-risk prioritization without treating predicted risk as confirmed vulnerability evidence.
The security concept defines the overall dependency-risk assessment strategy.
The following are the specific defensive mechanisms implemented.
Syft generates an SBOM from compatible OpenProject artifacts to identify software components and their available package metadata.
Public vulnerability records are matched against available package names, ecosystems, and versions.
Package metadata and vulnerability-related records are cleaned and transformed into a consistent feature representation.
LightGBM classifies dependency records using patterns learned from the selected labelled training data.
OSV-Scanner identifies known vulnerability matches in supported dependency inputs. Grype or Trivy may provide additional scanning evidence.
Dependencies are organized for further review using model predictions, known vulnerability findings, and other available security evidence.
SQLite stores package details, model predictions, scanner findings, and evaluation results.
Streamlit presents dependency inventory, vulnerability matches, predicted risk categories, and model evaluation results.
The final findings are reviewed against the original package records and known vulnerability information.
LightGBM is the machine-learning algorithm used to classify package vulnerability-risk categories.
OpenProject provides the existing software application whose dependencies are examined.
Syft generates an SBOM from supported application artifacts.
OSV-Scanner checks supported dependency inputs against vulnerability information from OSV.
Grype is an optional additional scanner for supported software artifacts and SBOMs.
Trivy is an optional alternative or supplementary scanner for supported application artifacts and dependencies.
OSV.dev provides public vulnerability records for supported package ecosystems.
Python provides the main implementation environment.
Pandas is used to organize and process the available package metadata and vulnerability records.
NumPy supports numerical data processing.
Scikit-learn provides classification metrics and suitable data-splitting utilities.
Streamlit provides the interactive dependency-risk dashboard.
SQLite stores the results produced by the analysis workflow.
Ubuntu provides the primary application and analysis environment.
Kali Linux provides a separate security-testing environment where required.
Nmap is optional for discovering network services associated with the authorized laboratory deployment.
VirtualBox provides the isolated laboratory infrastructure.
OpenProject is deployed within an isolated Ubuntu laboratory environment, providing an existing software application for dependency-risk analysis.
A Software Bill of Materials is generated from compatible OpenProject artifacts, providing a structured inventory of the software components included in the assessment.
Existing public package metadata and vulnerability records are inspected to determine whether they contain suitable features and reliable labels for the proposed machine-learning task.
The selected package records are preprocessed and transformed into numerical features suitable for LightGBM training.
The LightGBM model is trained and evaluated using held-out records, providing measurable evidence of classification performance.
OSV-Scanner identifies known vulnerability matches in supported dependency inputs, while an optional additional scanner provides supplementary validation evidence.
Compatible OpenProject dependency records are evaluated by the trained model to generate predicted vulnerability-risk categories.
Model predictions are compared with known vulnerability information and scanner findings, helping identify dependencies requiring further investigation.
A Streamlit dashboard displays the dependency inventory, known vulnerability findings, predicted risk categories, model evaluation metrics, and package-level analysis results.
The complete workflow demonstrates software dependency collection, SBOM generation, public vulnerability-data processing, LightGBM-based risk classification, scanner-based verification, risk prioritization, dashboard reporting, and final security validation. The resulting system supports dependency-risk assessment while preserving the distinction between predicted risk and confirmed vulnerability evidence.
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