Predicting Multi-Vulnerability Attack Chains with SBOM Graphs

Cybersecurity in the software supply chain has become one of the most pressing concerns for businesses of all sizes. Attacks do not usually exploit a single vulnerability, but rather chain several of them together in a coordinated way to infiltrate complex systems. Faced with this scenario, traditional methods of analysis based on flat lists of vulnerabilities (such as CVEs) are insufficient. A new approach then emerges: predicting multi-vulnerability attack chains by using Dependency Graphs enriched with the structure of an SBOM (Software Bill of Materials). This approach, supported by artificial intelligence and deep learning techniques on graphs, promises to revolutionize the way we understand and prevent threats in the software ecosystem.

Imagine software made up of dozens of open-source libraries, each with its own versions and known vulnerabilities. A conventional security scan examines each component separately and generates a list of CVEs without considering the relationships between them. However, real attackers don't operate like this: they seek to chain vulnerabilities through shared dependencies, misconfigurations, or data flows. This is where the representation of the SBOM as a heterogeneous graph is a game-changer. Each node can be a software component or a vulnerability, and the edges represent dependency, hierarchy, or exploitability relationships. On this structure, models such as Heterogeneous Care Networks (HGAT) are able to learn patterns that predict whether a component is associated with at least one vulnerability, with an accuracy of more than 91% according to recent studies on real datasets.

The real quantum leap comes when we move from component classification to complete string prediction. If two vulnerabilities are frequently chained together in documented attacks, it is possible to train lightweight models, such as a multilayer perceptron (MLP), to predict new combinations of vulnerabilities that could be exploited sequentially. These models achieve a ROC-AUC of 0.93, indicating a high discriminating capacity. In practice, this allows security teams to prioritize not just individual vulnerabilities, but entire attack paths, optimizing resources and closing gaps before they are exploited.

For companies, adopting this type of analysis means a change in mentality. It's no longer enough to scan the code once a year or rely on flat reports. Modern cybersecurity demands a continuous and predictive approach, integrated into development pipelines. At Q2BSTUDIO we understand that software protection doesn't end with deployment, but requires tools that evolve with threats. That's why we offer specialized cybersecurity and pentesting services that include SBOM-based analytics and predictive models, helping organizations anticipate complex attacks.

Software supply chain management also benefits from this paradigm. A well-built SBOM enriched with vulnerability data becomes a strategic asset. Companies working with AWS and Azure cloud services can integrate these graphs into their CI/CD environments, automating the detection of attack chains before deploying new versions. In addition, the combination with artificial intelligence for companies allows systems to learn from each incident and improve their predictions over time. At Q2BSTUDIO we develop custom applications and custom software that incorporate these analysis modules, adapting to the specific needs of each client, whether in the financial, healthcare or industrial sectors.

But the prediction of attack chains is not an end in itself; It is a means of strengthening the entire security posture. When a model identifies a potential path of exploitation, the team can apply patches, reconfigure dependencies, or isolate critical components. All of this requires a robust data infrastructure, where SBOMs are dynamically updated and models are regularly retrained. This is where AI agents come into play, continuously monitoring the sources of vulnerabilities and the relationships between components, alerting in real time about new attack chains.

Business analytics also plays a relevant role. Visualizing vulnerability chains using interactive dashboards allows security leaders and managers to make informed decisions. Our business intelligence services, with Power BI as the star tool, allow you to create dashboards that integrate the results of these predictive models, showing metrics such as the number of active chains, the accumulated risk or the effectiveness of mitigations. Thus, security is no longer an isolated department and becomes a pillar of business strategy.

Case study: A company that develops a platform with multiple third-party libraries can benefit greatly from this approach. By generating a rich SBOM and applying a chain prediction model, it discovers that three individual low-risk vulnerabilities are connected through a shared dependency in a logging library. Without the model, each CVE would have been treated separately and likely postponed due to its low criticality. With the prediction, the team identifies the complete path and applies an update to the logging library, eliminating the risk of chaining. This type of prevention is only possible when graph technology and artificial intelligence are combined with a holistic view of the supply chain.

At Q2BSTUDIO we offer solutions that integrate these concepts into the development lifecycle. From building custom applications with secure architectures to the implementation of graph-based monitoring systems, to consulting on AWS and Azure cloud services to deploy scalable and secure infrastructures. We also help companies implement AI for companies that learns from their own security data, generating predictive models specific to their ecosystem. All this with the aim of ensuring that cybersecurity is not an expense, but an investment that protects the value of the business.

Recent research shows that heterogeneous graph models applied to SBOM achieve very promising metrics, but the real potential lies in their industrial adoption. Companies leading digital transformation are already integrating these analytics into their processes. The future of supply chain security lies in moving beyond flat lists of vulnerabilities and embracing a network vision, where every component and every vulnerability is an interconnected node. Only then can we stay ahead of attackers who are increasingly using chains of multiple vulnerabilities to evade traditional defenses.

If your organization is looking to make the leap towards predictive and data-driven cybersecurity, we invite you to explore our solutions. At Q2BSTUDIO we combine expertise in custom software development, artificial intelligence and cybersecurity to offer a comprehensive approach. It is not just a matter of reacting to threats, but of predicting and neutralizing them before they materialize. Graph technology and machine learning are powerful tools; We help you apply them in a practical way and aligned with your business objectives. Contact our team and learn how to transform your security posture using enterprise AI and advanced analytics from SBOM.