Enabling Adversarial Robustness in AI Models through Kubeflow MLOps

• URL: http://arxiv.org/abs/2605.15249v1
• Date: Thu, 14 May 2026 12:45:36 GMT
• Title: Enabling Adversarial Robustness in AI Models through Kubeflow MLOps
• Abstract summary: This paper presents security measures for AI models deployed in clusters.<n>The proposed architecture integrates Kubeflow-based MLOps to automatically detect adversarial attacks during the inference phase.<n>Experiments indicate that the deployed defense robustifies the model, significantly recovering accuracy relative to the degradation caused by the attack.
• Score: 0.21290878226779877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI models are increasingly deployed in cloud-native environments to support scalable and automated services. However, while platforms such as Kubernetes provide strong infrastructure orchestration, security mechanisms specifically designed to protect deployed AI models remain limited. This paper presents security measures for AI models deployed in Kubernetes clusters. The proposed architecture integrates Kubeflow-based MLOps to automatically detect adversarial attacks during the inference phase and trigger defense mechanisms that preserve the model's accuracy and reliability. Specifically, a Fast Gradient Sign Method (FGSM) attack is applied at inference time, and a Projected Gradient Descent (PGD)-based adversarial training defense is automatically deployed when a degradation in accuracy is detected. The experimental results indicate that the deployed defense robustifies the model, significantly recovering accuracy relative to the degradation caused by the attack.

Related papers

• Defending against Model Extraction for GNNs with Model Reprogramming [57.82196268649236]
  Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (ML)<n>Still, their black-box deployment exposes them to Model Extraction (ME) attacks.<n>Existing defenses suffer from a critical ''Euclidean bias''<n>We propose GraphRP, a proactive defense framework that repurposes Model Reprogramming for security.
  arXiv  Detail & Related papers  (2026-08-11T23:20:15Z)
• Assessing Automated Prompt Injection Attacks in Agentic Environments [36.488797554085345]
  We present a comprehensive empirical evaluation of automated prompt injection attacks against LLM agents.<n>We adapt both white-box (GCG) and black-box (TAP) methods to the agentic setting within the AgentDojo framework.
  arXiv  Detail & Related papers  (2026-06-09T07:54:58Z)
• AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration [49.050688065974605]
  Membership Inference Attacks (MIAs) serve as a fundamental auditing tool for evaluating training data leakage in machine learning models.<n>We propose AutoMIA, an agentic framework that reformulates membership inference as an automated process of self-exploration and strategy evolution.
  arXiv  Detail & Related papers  (2026-04-01T15:17:45Z)
• A Practical Honeypot-Based Threat Intelligence Framework for Cyber Defence in the Cloud [0.3714118205123091]
  We introduce an automated defense framework that dynamically update firewall rules in real time.<n>The framework integrates deception sensors (Cowrie), Azure-native automation tools (Monitor, Sentinel, Logic Apps), and MITRE ATT&CK-aligned detection.
  arXiv  Detail & Related papers  (2025-12-04T23:39:25Z)
• A Survey on Model Extraction Attacks and Defenses for Large Language Models [55.60375624503877]
  Model extraction attacks pose significant security threats to deployed language models.<n>This survey provides a comprehensive taxonomy of extraction attacks and defenses, categorizing attacks into functionality extraction, training data extraction, and prompt-targeted attacks.<n>We examine defense mechanisms organized into model protection, data privacy protection, and prompt-targeted strategies, evaluating their effectiveness across different deployment scenarios.
  arXiv  Detail & Related papers  (2025-06-26T22:02:01Z)
• MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models [56.09354775405601]
  Model extraction attacks aim to replicate the functionality of a black-box model through query access.<n>Most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt suspicious inputs.<n>We propose MISLEADER, a novel defense strategy that does not rely on OOD assumptions.
  arXiv  Detail & Related papers  (2025-06-03T01:37:09Z)
• RADEP: A Resilient Adaptive Defense Framework Against Model Extraction Attacks [6.6680585862156105]
  We introduce a Resilient Adaptive Defense Framework for Model Extraction Attack Protection (RADEP)<n>RADEP employs progressive adversarial training to enhance model resilience against extraction attempts.<n> Ownership verification is enforced through embedded watermarking and backdoor triggers.
  arXiv  Detail & Related papers  (2025-05-25T23:28:05Z)
• AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
  AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
  arXiv  Detail & Related papers  (2025-04-29T17:36:05Z)
• STShield: Single-Token Sentinel for Real-Time Jailbreak Detection in Large Language Models [31.35788474507371]
  Large Language Models (LLMs) have become increasingly vulnerable to jailbreak attacks.<n>We present STShield, a lightweight framework for real-time jailbroken judgement.
  arXiv  Detail & Related papers  (2025-03-23T04:23:07Z)
• ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks [18.488168353080464]
  Graph Neural Networks (GNNs) have gained traction in Graph-based Machine Learning as a Service (GML) platforms, yet they remain vulnerable to graph-based model extraction attacks (MEAs)<n>We propose ATOM, a novel real-time MEA detection framework tailored for GNNs.<n>ATOM integrates sequential modeling and reinforcement learning to dynamically detect evolving attack patterns, while leveraging $k$core embedding to capture the structural properties, enhancing detection precision.
  arXiv  Detail & Related papers  (2025-03-20T20:25:32Z)
• Adversarial Training for Defense Against Label Poisoning Attacks [53.893792844055106]
  Label poisoning attacks pose significant risks to machine learning models.<n>We propose a novel adversarial training defense strategy based on support vector machines (SVMs) to counter these threats.<n>Our approach accommodates various model architectures and employs a projected gradient descent algorithm with kernel SVMs for adversarial training.
  arXiv  Detail & Related papers  (2025-02-24T13:03:19Z)
• Enhancing Object Detection Robustness: Detecting and Restoring Confidence in the Presence of Adversarial Patch Attacks [2.963101656293054]
  This study evaluates defense mechanisms for the YOLOv5 model against adversarial patches.<n>We tested several defenses, including Segment and Complete (SAC), Inpainting, and Latent Diffusion Models.<n>Results indicate that adversarial patches reduce average detection confidence by 22.06%.
  arXiv  Detail & Related papers  (2024-03-04T13:32:48Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)

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.