Micro-Segmentation Anomaly Detection in Zero-Trust Software-Defined Network Fabrics

ArXiv security-relevant research covering zero-trust micro-segmentation for anomaly detection, AI watermarking and IP protection, indirect prompt-injection exposure and defenses, privacy-preserving fairness attestation, LLM safety surface-form sensitivity, large-scale Docker image vulnerabilities and secrets, policy-constrained coding agents, hardware-performance-counter malware detection, and secure Terraform generation. The strongest operational risk findings concern widespread vulnerabilities and misconfigurations in high-exposure Docker images, indirect prompt injection against agentic LLM