A Systematic Mapping Study on Risks and Vulnerabilities in Software Containers
- URL: http://arxiv.org/abs/2512.11940v1
- Date: Fri, 12 Dec 2025 10:22:34 GMT
- Title: A Systematic Mapping Study on Risks and Vulnerabilities in Software Containers
- Authors: Maha Sroor, Teerath Das, Rahul Mohanani, Tommi Mikkonen,
- Abstract summary: Software containers are widely adopted for developing and deploying software applications.<n>Despite their popularity, major security concerns arise during container development and deployment.<n>This study offers critical insights into the current landscape of security issues within software container systems.
- Score: 1.3099514901173375
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software containers are widely adopted for developing and deploying software applications. Despite their popularity, major security concerns arise during container development and deployment. Software Engineering (SE) research literature reveals a lack of reviewed, aggregated, and organized knowledge of risks, vulnerabilities, security practices, and tools in container-based systems development and deployment. Therefore, we conducted a Systematic Mapping Study (SMS) based on 129 selected primary studies to explore and organize existing knowledge on security issues in software container systems. Data from the primary studies enabled us to identify critical risks and vulnerabilities across the container life-cycle and categorize them using a novel taxonomy. Additionally, the findings highlight the causes and implications and provide a list of mitigation techniques to overcome these risks and vulnerabilities. Furthermore, we provide an aggregation of security practices and tools that can help support and improve the overall security of container systems. This study offers critical insights into the current landscape of security issues within software container systems. Our analysis highlights the need for future SE research to focus on security enhancement practices that strengthen container systems and develop effective mitigation strategies to comprehensively address existing risks and vulnerabilities.
Related papers
- An Empirical Study on the Security Vulnerabilities of GPTs [48.12756684275687]
GPTs are one kind of customized AI agents based on OpenAI's large language models.<n>We present an empirical study on the security vulnerabilities of GPTs.
arXiv Detail & Related papers (2025-11-28T13:30:25Z) - Security Debt in Practice: Nuanced Insights from Practitioners [0.3277163122167433]
Tight deadlines, limited resources, and prioritization of functionality over security can lead to insecure coding practices.<n>Despite their critical importance, there is limited empirical evidence on how software practitioners perceive, manage, and communicate Security Debts.<n>This study is based on semi-structured interviews with 22 software practitioners across various roles, organizations, and countries.
arXiv Detail & Related papers (2025-07-15T14:28:28Z) - AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety.<n> AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques.<n>We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
arXiv Detail & Related papers (2025-02-24T02:11:52Z) - Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
arXiv Detail & Related papers (2025-01-09T03:59:10Z) - On Security Weaknesses and Vulnerabilities in Deep Learning Systems [32.14068820256729]
We specifically look into deep learning (DL) framework and perform the first systematic study of vulnerabilities in DL systems.
We propose a two-stream data analysis framework to explore vulnerability patterns from various databases.
We conducted a large-scale empirical study of 3,049 DL vulnerabilities to better understand the patterns of vulnerability and the challenges in fixing them.
arXiv Detail & Related papers (2024-06-12T23:04:13Z) - Sok: Comprehensive Security Overview, Challenges, and Future Directions of Voice-Controlled Systems [10.86045604075024]
The integration of Voice Control Systems into smart devices accentuates the importance of their security.
Current research has uncovered numerous vulnerabilities in VCS, presenting significant risks to user privacy and security.
This study introduces a hierarchical model structure for VCS, providing a novel lens for categorizing and analyzing existing literature in a systematic manner.
We classify attacks based on their technical principles and thoroughly evaluate various attributes, such as their methods, targets, vectors, and behaviors.
arXiv Detail & Related papers (2024-05-27T12:18:46Z) - Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
We investigate the security implications of and software-based Open Radio Access Network (RAN) systems.
We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
arXiv Detail & Related papers (2024-05-03T07:18:45Z) - Leveraging Traceability to Integrate Safety Analysis Artifacts into the
Software Development Process [51.42800587382228]
Safety assurance cases (SACs) can be challenging to maintain during system evolution.
We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models.
We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
arXiv Detail & Related papers (2023-07-14T16:03:27Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.