KubeFence: Security Hardening of the Kubernetes Attack Surface

• URL: http://arxiv.org/abs/2504.11126v1
• Date: Tue, 15 Apr 2025 12:15:34 GMT
• Title: KubeFence: Security Hardening of the Kubernetes Attack Surface
• Authors: Carmine Cesarano, Roberto Natella,
• Abstract summary: K8s is widely used to orchestrate containerized applications, including critical services in domains such as finance, healthcare, and government.<n>Its extensive and feature-rich API interface exposes a broad attack surface, making K8s vulnerable to exploits of software vulnerabilities and misconfigurations.<n>This paper proposes a novel solution, KubeFence, which implements finer-grain API filtering tailored to specific client workloads.
• Score: 3.67004863266419
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Kubernetes (K8s) is widely used to orchestrate containerized applications, including critical services in domains such as finance, healthcare, and government. However, its extensive and feature-rich API interface exposes a broad attack surface, making K8s vulnerable to exploits of software vulnerabilities and misconfigurations. Even if K8s adopts role-based access control (RBAC) to manage access to K8s APIs, this approach lacks the granularity needed to protect specification attributes within API requests. This paper proposes a novel solution, KubeFence, which implements finer-grain API filtering tailored to specific client workloads. KubeFence analyzes Kubernetes Operators from trusted repositories and leverages their configuration files to restrict unnecessary features of the K8s API, to mitigate misconfigurations and vulnerabilities exploitable through the K8s API. The experimental results show that KubeFence can significantly reduce the attack surface and prevent attacks compared to RBAC.

Related papers

• Breaking the Bulkhead: Demystifying Cross-Namespace Reference Vulnerabilities in Kubernetes Operators [3.1176172329546894]
  This paper is the first to systematically investigate the security vulnerability of Operators.<n>We found that over 14% of Operators in the wild are potentially vulnerable.<n>Our findings have been reported to the relevant developers.
  arXiv  Detail & Related papers  (2025-07-04T08:44:24Z)
• Exploiting Inaccurate Branch History in Side-Channel Attacks [54.218160467764086]
  This paper examines how resource sharing and contention affect two widely implemented but underdocumented features: Bias-Free Branch Prediction and Branch History Speculation.<n>We show that these features can inadvertently modify the Branch History Buffer (BHB) update behavior and create new primitives that trigger malicious mis-speculations.<n>We present three novel attack primitives: two Spectre attacks, namely Spectre-BSE and Spectre-BHS, and a cross-privilege control flow side-channel attack called BiasScope.
  arXiv  Detail & Related papers  (2025-06-08T19:46:43Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Fundamental Limitations in Defending LLM Finetuning APIs [61.29028411001255]
  We show that defences of fine-tuning APIs are fundamentally limited in their ability to prevent fine-tuning attacks.<n>We construct 'pointwise-undetectable' attacks that repurpose entropy in benign model outputs to covertly transmit dangerous knowledge.<n>We test our attacks against the OpenAI fine-tuning API, finding they succeed in eliciting answers to harmful multiple-choice questions.
  arXiv  Detail & Related papers  (2025-02-20T18:45:01Z)
• Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective [65.65471972217814]
  federated recommendation (FR) based on federated learning (FL) emerges, keeping the personal data on the local client and updating a model collaboratively.<n>FR has a unique sparse aggregation mechanism, where the embedding of each item is updated by only partial clients, instead of full clients in a dense aggregation of general FL.<n>In this paper, we reformulate the Byzantine robustness under sparse aggregation by defining the aggregation for a single item as the smallest execution unit.<n>We propose a family of effective attack strategies, named Spattack, which exploit the vulnerability in sparse aggregation and are categorized along the adversary's knowledge and capability.
  arXiv  Detail & Related papers  (2025-01-06T15:19:26Z)
• K8s Pro Sentinel: Extend Secret Security in Kubernetes Cluster [0.0]
  This research introduces K8s Pro Sentinel, an operator that automates the configuration of encryption and access control for Secret Objects. The performance and reliability of the Sentinel operator were evaluated using Red Hat Operator Scorecard and chaos engineering practices.
  arXiv  Detail & Related papers  (2024-11-25T18:15:37Z)
• GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation [24.312198733476063]
  Rule-based (RB) tools for KCF misconfiguration detection rely on static rule sets, making them inherently limited and unable to detect newly-discovered misconfigurations. We propose GenKubeSec, a comprehensive and adaptive, LLM-based method, which, in addition to detecting a wide variety of KCF misconfigurations, also identifies the exact location of the misconfigurations and provides detailed reasoning about them.
  arXiv  Detail & Related papers  (2024-05-30T11:18:52Z)
• Implementation of New Security Features in CMSWEB Kubernetes Cluster at CERN [1.6804702845109005]
  We discuss new security features introduced to the CMSWEB ("k8s") cluster. The new features include the implementation of network policies, deployment of Open Policy Agent (OPA), enforcement of OPA policies, and the integration of Vault.
  arXiv  Detail & Related papers  (2024-05-24T08:22:22Z)
• EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection [53.25863925815954]
  Federated self-supervised learning (FSSL) has emerged as a promising paradigm that enables the exploitation of clients' vast amounts of unlabeled data. While FSSL offers advantages, its susceptibility to backdoor attacks has not been investigated. We propose the Embedding Inspector (EmInspector) that detects malicious clients by inspecting the embedding space of local models.
  arXiv  Detail & Related papers  (2024-05-21T06:14:49Z)
• Exploiting Kubernetes' Image Pull Implementation to Deny Node Availability [0.0]
  Application Programming Interface (API) interactions between K8s and its runtime interfaces have not been studied thoroughly. CRI-API is responsible for abstracting the container runtime, managing the creation and lifecycle of containers along with the downloads of the respective images. We show that such attacks can generate up to 95% average CPU usage, prevent downloading new container images, and increase I/O and network usage for a potentially unlimited amount of time.
  arXiv  Detail & Related papers  (2024-01-19T09:49:53Z)
• G$^2$uardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering [116.4277292854053]
  Federated Learning (FL) offers collaborative model training without data sharing. FL is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system integrity. We present G$2$uardFL, a protective framework that reinterprets the identification of malicious clients as an attributed graph clustering problem.
  arXiv  Detail & Related papers  (2023-06-08T07:15:04Z)
• REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service [67.0982378001551]
  We show how a service provider pre-trains an encoder and then deploys it as a cloud service API. A client queries the cloud service API to obtain feature vectors for its training/testing inputs. We show that the cloud service only needs to provide two APIs to enable a client to certify the robustness of its downstream classifier.
  arXiv  Detail & Related papers  (2023-01-07T17:40:11Z)
• OpenAPI Specification Extended Security Scheme: A method to reduce the prevalence of Broken Object Level Authorization [0.0]
  API Security is a topic for concern given the absence of standardized authorization in the OpenAPI standard. This paper examines the number one vulnerability in API Security: Broken Object Level Authorization(BOLA)
  arXiv  Detail & Related papers  (2022-12-13T14:28:06Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)

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.