Formal Security Orchestration in CI/CD Pipelines: A Quantitative Risk Model for DevSecOps with MITRE ATT&CK Correlation and Operational Metrics

1. Introduction

The digital transformation has intensified the adoption of microservices-based architectures, containers, and orchestration platforms such as Kubernetes. In this context, continuous integration and continuous delivery (CI/CD) pipelines have become the central mechanism for building, testing, and distributing software at scale. Although such pipelines significantly increase development team productivity, they also introduce new attack vectors related to the software supply chain (ENISA, 2023).

Empirical reports indicate that a large share of security breaches results from operational failures and the exploitation of credentials or known vulnerabilities. The 2023 Data Breach Investigations Report indicates that approximately 74% of analyzed breaches involve the human element or the misuse of compromised credentials (VERIZON, 2023). At the same time, attacks targeting the software supply chain have grown significantly since 2020, affecting entire software development and distribution ecosystems (CISA, 2023).

Large-scale incidents illustrate the magnitude of this problem. The compromise of the SolarWinds Orion platform through the SUNBURST campaign demonstrated how compromised build artifacts can be distributed to thousands of organizations. Similarly, the Log4Shell vulnerability (CVE‑2021‑44228) evidenced how quickly widely used dependencies can become vectors for global exploitation (NIST, 2023).

In response to this scenario, the DevSecOps paradigm proposes incorporating security mechanisms throughout the entire software development lifecycle. In theory, code scanners, dependency analysis, dynamic testing, and runtime monitoring should be integrated into the CI/CD pipeline. However, in practice, we observe important structural limitations.

First, most DevSecOps implementations remain tool-centric, without a formal model that integrates the results of multiple scanners into a unified risk metric. Second, critical decisions — such as blocking or allowing the promotion of an artifact — are often based on static thresholds, ignoring contextual factors such as exploitability probability or service criticality. Finally, security tool findings are rarely correlated with operational threat models such as the MITRE ATT&CK matrix, making strategic mitigation prioritization difficult (STROM et al., 2018).

Given these gaps, we formulate the following research questions:

• RQ1: How can the risk associated with vulnerabilities identified during CI/CD pipeline executions be formally modeled?
• RQ2: Is it possible to reduce exposure to critical vulnerabilities in production without a significant increase in pipeline execution time?
• RQ3: How can findings from DevSecOps tools be correlated with attack techniques described in the MITRE ATT&CK matrix?

To answer these questions, we propose an incremental quantitative risk model applied to CI/CD pipelines. Our approach combines standardized vulnerability metrics with operational contextual factors and uses an adaptive decision algorithm for automated release control.

The main contributions of this work are:

• formal definition of accumulated risk in CI/CD pipelines based on CVSS and EPSS metrics;
• adaptive blocking algorithm for artifact promotion;
• experimental implementation in a Kubernetes environment with widely adopted DevSecOps tools;
• empirical evaluation with quantitative performance and security metrics.

2. Background and Related Work

2.1 Security in Software Supply Chains

Attacks on the software supply chain have become one of the main security concerns of the past decade. The growing complexity of dependency ecosystems and the adoption of public package repositories significantly expand the attack surface (BOYKIN; REARDON, 2022).

Recent initiatives such as the SLSA (Supply-chain Levels for Software Artifacts) framework and the NIST Secure Software Development Framework (SSDF) seek to establish formal practices for build integrity and artifact traceability (NIST, 2022; OPENSSF, 2023).

2.2 Vulnerability Metrics

The Common Vulnerability Scoring System (CVSS) is the widely used standard for quantifying vulnerability severity (FIRST, 2019). However, studies show that CVSS alone does not adequately represent the actual probability of exploitation.

To mitigate this limitation, the Exploit Prediction Scoring System (EPSS) was developed, which probabilistically estimates the likelihood of active exploitation of a vulnerability based on historical data and threat intelligence (JACOBS et al., 2021).

2.3 DevSecOps and Security Automation

Systematic literature reviews indicate that DevSecOps adoption is often limited to integrating static analysis tools and dependency scanners into CI/CD pipelines (MYRBAKKEN; COLOMO‑PALACIOS, 2017). Although such tools increase vulnerability visibility, their operational effectiveness depends on risk-based prioritization and governance mechanisms.

Another recurring challenge is the large volume of alerts generated by security tools, which can result in operational fatigue and inadequate prioritization of critical vulnerabilities (BEHL; BEHL, 2016).

2.4 Correlation with Threat Models

The MITRE ATT&CK matrix provides a structured taxonomy of adversary techniques observed in real incidents (STROM et al., 2018). Correlating detected vulnerabilities with ATT&CK techniques makes it possible to contextualize risks in terms of adversary behavior, improving mitigation prioritization.

3. Threat Model and Problem Formalization

We model the CI/CD pipeline as a directed acyclic graph:

$$ G = (V,E) $$

where each vertex $v \in V$ represents a pipeline stage (build, test, static analysis, dependency analysis, or deploy) and each edge $e \in E$ represents execution dependencies between stages.

We consider a set of vulnerabilities detected during a pipeline execution:

$$ Vuln = {v_1, v_2, …, v_n} $$

Each vulnerability $v_i$ has the following attributes:

• CVSS score $s_i \in [0,10]$
• exploit probability $p_i$ derived from EPSS
• asset criticality weight $w_i$
• temporal factor $a_i$ proportional to the age of the vulnerability.

We define the incremental risk associated with a vulnerability as:

$$ r_i = w_i \cdot s_i \cdot p_i \cdot a_i $$

The total pipeline risk is defined as:

$$ R = \sum_{i=1}^{n} r_i $$

Our objective is to minimize $R$ subject to the operational constraint of pipeline execution time:

$$ T_{build} \leq T_{max} $$

In addition, we define the system’s fundamental security property:

$$ R \leq \theta $$

where $\theta$ represents the maximum acceptable risk threshold for artifact promotion.

4. Proposed Methodology

4.1 DevSecOps Architecture

Our architecture integrates multiple layers of security analysis into the CI/CD pipeline.

Category	Tool	Function
SAST	Semgrep, SonarQube	static code analysis
SCA	Syft, Grype, Trivy	dependency and container analysis
DAST	OWASP ZAP	dynamic testing
IaC	Checkov	infrastructure as code analysis
Runtime	Falco	anomalous behavior detection
Network IDS	Suricata	network intrusion detection

SBOM (Software Bill of Materials) generation is performed using Syft, enabling dependency traceability and correlation with public vulnerability databases.

4.2 Adaptive Blocking Algorithm

We introduce a decision mechanism based on a dynamic threshold:

Algorithm 1: AdaptiveRiskGate
Input: Vulnerabilities V, base threshold θ_base
Output: Decision (ALLOW | BLOCK)

1: R ← 0
2: for each vulnerability v in V do
3:     compute r_i
4:     R ← R + r_i
5: θ_dynamic ← θ_base  log(1 + |V|)
6: if R > θ_dynamic then
7:     return BLOCK
8: else
9:     return ALLOW

The computational complexity is $O(n)$, where $n$ is the number of detected vulnerabilities.

The use of a logarithmic threshold makes it possible to dynamically adjust risk tolerance according to the volume of findings, avoiding excessive blocking in builds with a large number of low-severity vulnerabilities.

5. Implementation

The implementation was carried out using GitLab CI running runners in a Kubernetes cluster.

Example of static analysis configuration:

sast:
  image: returntocorp/semgrep
  script:
    - semgrep --config=p/ci .

Container analysis with Trivy:

trivy image --severity HIGH,CRITICAL --format json -o results.json app:latest

SBOM generation:

syft packages app:latest -o cyclonedx-json > sbom.json

Runtime monitoring is performed by Falco using rules based on Linux kernel events and eBPF.

The average overhead introduced in the pipeline was approximately 8.4%, a value considered acceptable for production environments with high security requirements.

6. Experimental Evaluation

The experiment was conducted in a Kubernetes environment composed of 12 nodes with 32 vCPU and 128 GB of memory each.

The experimental dataset included:

• 1,200 pipeline executions
• 37 microservices
• OWASP Benchmark applications
• internal production applications.

Main results:

Metric	Baseline	Proposed Model
MTTR	14.2 days	8.3 days
Critical vulnerabilities in production	112	42
Pipeline overhead	3%	8.4%

The relative reduction in exposure to critical vulnerabilities was approximately 62.7%.

7. Case Study

We applied the model in a fintech organization with approximately four million active users.

The adoption process took place in four main stages:

1. integration of SAST and SCA scanners into the pipeline;
2. automatic SBOM generation for each build;
3. definition of the initial threshold $\theta_{base}=50$;
4. correlation of vulnerabilities with MITRE ATT&CK techniques.

During nine months of operation, 17 releases containing exploitable critical vulnerabilities were automatically blocked.

No incidents related to critical CVEs were recorded during the analyzed period.

8. Discussion

The results suggest that the use of quantitative risk metrics enables more consistent decisions in CI/CD pipelines when compared to static thresholds.

The main advantage of the proposed approach is the integration of multiple risk dimensions — severity, exploitability probability, and operational context — into a single aggregated metric.

However, some limitations remain. The quality of the estimates depends directly on the accuracy of public vulnerability databases and the coverage of static analysis tools. In addition, false positives still represent a relevant challenge in highly automated DevSecOps environments.

Another relevant observation is the need for integration between development pipelines and security operations (SOC). Exporting risk metrics to SIEM or XDR platforms may enable correlation with production events and improve incident response capability.

9. Conclusion

This work presented a formal model for security orchestration in CI/CD pipelines based on quantitative risk metrics. We demonstrated how the combination of CVSS, EPSS, asset criticality, and temporal factors makes it possible to estimate the incremental risk associated with vulnerabilities identified during the build process.

The experimental implementation showed that automated decisions based on this model can significantly reduce exposure to critical vulnerabilities without compromising pipeline operational efficiency.

The results reinforce the importance of metric-driven approaches for security governance in DevSecOps environments, especially in light of the continuing growth of software supply chain attacks.

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