The paper investigates how Large Language Models can help detect misconfigurations in Kubernetes, the dominant platform for orchestrating containerized applications in cloud‑native environments. It presents a taxonomy of common misconfiguration types, evaluates existing detection tools, and analyzes which Kubernetes objects are most vulnerable and how severe the issues can be. The study demonstrates that advanced machine learning, particularly LLMs, can offer new insights and improve the effectiveness of misconfiguration detection methods.
arXiv:2607. 12723v1 Announce Type: cross Abstract: Filesystem isolation in container ecosystems is often weakened by cross-boundary path misresolution, causing path traversal (PaTra) vulnerabilities.
arXiv:2608.22089v1 Announce Type: cross Abstract: Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Securit...
arXiv:2603. 12001v2 Announce Type: replace-cross Abstract: Distributed AI and IoT applications increasingly execute across heterogeneous resources spanning end devices, edge/fog infrastructure, and cloud platforms, often under different administrative domains.
arXiv:2603. 02277v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly act as autonomous agents, using tools to execute code, read and write files, and access networks, creating novel security risks.
arXiv:2607. 02731v1 Announce Type: cross Abstract: Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities.
The paper presents a systematic framework for large language model (LLM) watermarking as a provenance tool in big data ecosystems. It categorizes existing watermarking methods along four deployment dimensions—insertion point, verification authority, operational state, and transformation threat model—and aligns them with the big data principles of Volume, Velocity, Variety, Veracity, and Value. The authors introduce a readiness framework that maps four key workloads—online generation, streaming detection, transformation pipelines, and ecosystem governance—to system-level requirements such as throughput, false-positive control, robustness, cross-domain reliability, governance, and downstream utility, while highlighting gaps between benchmark performance and real-world deployment readiness.
arXiv:2606. 04769v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as a critical standard empowering Large Language Models (LLMs) to utilize external tools.
arXiv:2606. 03323v2 Announce Type: replace-cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.
arXiv:2603. 07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs).
arXiv:2606. 03323v1 Announce Type: cross Abstract: The rise of LLM-as-a-Service and other confidential cloud workloads demands cryptographic proof that user data is processed in a trusted, untampered environment.
arXiv:2607. 25995v1 Announce Type: cross Abstract: Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads.
arXiv:2607. 00553v1 Announce Type: cross Abstract: Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment.