ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes

• URL: http://arxiv.org/abs/2505.23805v1
• Date: Tue, 27 May 2025 02:24:45 GMT
• Title: ADA: Automated Moving Target Defense for AI Workloads via Ephemeral Infrastructure-Native Rotation in Kubernetes
• Authors: Akram Sheriff, Ken Huang, Zsolt Nemeth, Madjid Nakhjiri,
• Abstract summary: ADA operates by continuously and automatically rotating these workloads at the infrastructure level.<n>This constant managed churn invalidates attacker assumptions and disrupts potential kill chains.<n>We posit that ADA's novel approach to AMTD provides a more robust, agile, and operationally efficient zero-trust model for AI services.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces the Adaptive Defense Agent (ADA), an innovative Automated Moving Target Defense (AMTD) system designed to fundamentally enhance the security posture of AI workloads. ADA operates by continuously and automatically rotating these workloads at the infrastructure level, leveraging the inherent ephemerality of Kubernetes pods. This constant managed churn systematically invalidates attacker assumptions and disrupts potential kill chains by regularly destroying and respawning AI service instances. This methodology, applying principles of chaos engineering as a continuous, proactive defense, offers a paradigm shift from traditional static defenses that rely on complex and expensive confidential or trusted computing solutions to secure the underlying compute platforms, while at the same time agnostically supporting the latest advancements in agentic and nonagentic AI ecosystems and solutions such as agent-to-agent (A2A) communication frameworks or model context protocols (MCP). This AI-native infrastructure design, relying on the widely proliferated cloud-native Kubernetes technologies, facilitates easier deployment, simplifies maintenance through an inherent zero trust posture achieved by rotation, and promotes faster adoption. We posit that ADA's novel approach to AMTD provides a more robust, agile, and operationally efficient zero-trust model for AI services, achieving security through proactive environmental manipulation rather than reactive patching.

Related papers

• AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs [24.71883582216731]
  AdapTools is a novel adaptive IPI attack framework that selects stealthier attack tools and generates adaptive attack prompts.<n>AdapTools achieves a 2.13 times improvement in attack success rate while degrading system utility by a factor of 1.78.
  arXiv  Detail & Related papers  (2026-02-24T09:32:19Z)
• ORCA -- An Automated Threat Analysis Pipeline for O-RAN Continuous Development [57.61878484176942]
  Open-Radio Access Network (O-RAN) integrates numerous software components in a cloud-like deployment, opening the radio access network to previously unconsidered security threats.<n>Current vulnerability assessment practices often rely on manual, labor-intensive, and subjective investigations, leading to inconsistencies in the threat analysis.<n>We propose an automated pipeline that leverages Natural Language Processing (NLP) to minimize human intervention and associated biases.
  arXiv  Detail & Related papers  (2026-01-20T07:31:59Z)
• CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
  AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
  arXiv  Detail & Related papers  (2026-01-14T23:06:35Z)
• Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundations [20.297163645916157]
  Cybersecurity is being fundamentally reshaped by foundation-model-based artificial intelligence.<n>This chapter argues for a shift from prevention-centric security toward agentic cyber resilience.
  arXiv  Detail & Related papers  (2025-12-28T11:17:36Z)
• Cognitive Control Architecture (CCA): A Lifecycle Supervision Framework for Robustly Aligned AI Agents [1.014002853673217]
  LLM agents are vulnerable to Indirect Prompt Injection (IPI) attacks.<n>IPI attacks hijack agent behavior by polluting external information sources.<n>We propose the Cognitive Control Architecture (CCA), a holistic framework achieving full-lifecycle cognitive supervision.
  arXiv  Detail & Related papers  (2025-12-07T08:11:19Z)
• 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)
• From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
  Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
  arXiv  Detail & Related papers  (2025-10-07T05:45:25Z)
• Securing AI Agents: Implementing Role-Based Access Control for Industrial Applications [0.0]
  In industrial settings, AI agents are transforming operations by enhancing decision-making, predictive maintenance, and process optimization.<n>Despite these advancements, AI agents remain vulnerable to security threats, including prompt injection attacks.<n>This paper proposes a framework for integrating Role-Based Access Control (RBAC) into AI agents, providing a robust security guardrail.
  arXiv  Detail & Related papers  (2025-09-14T20:58:08Z)
• Secure Tug-of-War (SecTOW): Iterative Defense-Attack Training with Reinforcement Learning for Multimodal Model Security [63.41350337821108]
  We propose Secure Tug-of-War (SecTOW) to enhance the security of multimodal large language models (MLLMs)<n>SecTOW consists of two modules: a defender and an auxiliary attacker, both trained iteratively using reinforcement learning (GRPO)<n>We show that SecTOW significantly improves security while preserving general performance.
  arXiv  Detail & Related papers  (2025-07-29T17:39:48Z)
• SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator [77.86600052899156]
  Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
  arXiv  Detail & Related papers  (2025-05-23T10:56:06Z)
• Threat Modeling for AI: The Case for an Asset-Centric Approach [0.23408308015481666]
  AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight.<n>With AI systems now able to autonomously execute code, interact with external systems, and operate without human oversight, traditional security approaches fall short.<n>This paper introduces an asset-centric methodology for threat modeling AI systems.
  arXiv  Detail & Related papers  (2025-05-08T18:57:08Z)
• 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)
• Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents [0.0]
  This paper introduces a comprehensive threat model tailored specifically for GenAI agents.<n>Research work identifies 9 primary threats and organizes them across five key domains.
  arXiv  Detail & Related papers  (2025-04-28T16:29:24Z)
• Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence [0.0]
  Cyber-attacks pose a security threat to military command and control networks, Intelligence, Surveillance, and Reconnaissance (ISR) systems, and civilian critical national infrastructure.<n>The use of artificial intelligence and autonomous agents in these attacks increases the scale, range, and complexity of this threat and the subsequent disruption they cause.<n> Autonomous Cyber Defence (ACD) agents aim to mitigate this threat by responding at machine speed and at the scale required to address the problem.
  arXiv  Detail & Related papers  (2024-11-26T16:51:52Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
  This paper systematically quantifies the robustness of VLA-based robotic systems.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems [21.693552236958983]
  Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains. With recent advancements in artificial intelligence (AI), learning-based components, especially AI controllers, have become essential in enhancing the functionality and efficiency of CPSs. The lack of interpretability in these AI controllers presents challenges to the safety and quality assurance of AI-enabled CPSs (AI-CPSs)
  arXiv  Detail & Related papers  (2024-08-07T16:44:53Z)
• Moving Target Defense based Secured Network Slicing System in the O-RAN Architecture [12.360792257414458]
  Artificial intelligence (AI) and machine learning (ML) security threats can even threaten open radio access network (O-RAN) benefits. This paper proposes a novel approach to estimating the optimal number of predefined VNFs for each slice. We also address secure AI/ML methods for dynamic service admission control and power minimization in the O-RAN architecture.
  arXiv  Detail & Related papers  (2023-09-23T18:21:33Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.