Microservice Vulnerability Analysis: A Literature Review with Empirical Insights
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Abstract (arXiv)
Microservice architectures are revolutionizing both small businesses and large corporations, igniting a new era of innovation with their exceptional advantages in maintainability, reusability, and scalability. However, these benefits come with significant security challenges, as the increased complexity of service interactions, expanded attack surfaces, and intricate dependency management introduce a new array of cybersecurity vulnerabilities. While security concerns are mounting, there is a lack of comprehensive research that integrates a review of existing knowledge with empirical analysis of microservice vulnerabilities. This study aims to fill this gap by gathering, analyzing, and synthesizing existing literature on security vulnerabilities associated with microservice architectures. Through a thorough examination of 62 studies, we identify, analyze, and report 126 security vulnerabilities inherent in microservice architectures. This comprehensive analysis enables us to (i) propose a taxonomy that categorizes microservice vulnerabilities based on the distinctive features of microservice architectures; (ii) conduct an empirical analysis by performing vulnerability scans on four diverse microservice benchmark applications using three different scanning tools to validate our taxonomy; and (iii) map our taxonomy vulnerabilities with empirically identified vulnerabilities, providing an in-depth vulnerability analysis at microservice, application, and scanning tool levels. Our study offers crucial guidelines for practitioners and researchers to advance both the state-of-the-practice and the state-of-the-art in securing microservice architectures.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Jul 2024.
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Citing papers per year: 2024: 1; 2025: 0; 2026: 4.
5 papers.
- 2024ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Modelscited by 11
- 2026Australian Bushfire Intelligence with AI-Driven Environmental Analyticscited by 3
- 2026Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learningcited by 3
- 2026AgenticVM: Agentic AI for Adaptive Software Vulnerability Managementcited by 2
- 2026Comparative Analysis of Large Language Models in Healthcarecited by 1
- 2026AgenticVM: Agentic AI for Adaptive Software Vulnerability Management3 May 2026
- 2026Comparative Analysis of Large Language Models in Healthcare11 Apr 2026
- 2026Explainable Autonomous Cyber Defense using Adversarial Multi-Agent Reinforcement Learning6 Apr 2026
- 2026Australian Bushfire Intelligence with AI-Driven Environmental Analytics3 Jan 2026
- 2024ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models6 Dec 2024