A Defence-Oriented Study of API Security in CI/CD Pipelines
Bibliographic record
Abstract
Current CI/CD (Continuous Integration/Continuous Deployment) pipelines rely heavily on APIs (Application Programming Interfaces) to automate builds, deployments, and service orchestration. However, the growing complexity of these pipelines has revealed new challenges, including credential management, access control, runtime enforcement, etc., in securing API endpoints. This study presents a defence-oriented analysis of API security within CI/CD pipelines, focusing on the tools, practices, and challenges that outline real-world protection approaches. Through a systematic review of 34 peer-reviewed articles, we identified key defensive mechanisms, including role-based access control (RBAC), secrets vaults, Policy-as-Code (PaC) enforcement, and multi-factor authentication. We evaluated widely adopted tools, such as HashiCorp Vault, Open Policy Agent, and Snyk, and examined their deployment across CI/CD stages. Our analysis reveals unstable adoption, limited runtime observability, and fragmented enforcement across cloud platforms. To address these gaps, we propose a defence-mapping framework and outline actionable proposals to secure the CI/CD pipeline by design. This study supports practitioners and researchers in advancing API resilience across DevSecOps workflows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".