14 articles in this topic.
This topic page curates research-focused writing on Infrastructure Security, with an emphasis on practical security implications, reproducible observations, and implementation-aware takeaways. Instead of isolated summaries, the collection is organized to help you connect attack techniques, defensive controls, and evaluation criteria across multiple papers and project write-ups.
Across 14 articles, this cluster highlights how Infrastructure Security appears in real workflows and where teams commonly miss risk boundaries. The coverage includes news digest, trend report, research paper, paper review and connects this theme with adjacent areas such as LLM Security, Agent Security, AI Safety, so you can move from conceptual understanding to deployable engineering decisions.
This page is maintained as a high-signal index for Infrastructure Security. Use it to follow newer articles first, then branch into adjacent topics and defensive patterns that repeatedly appear across projects and paper reviews.
The systematic scaling of automated, AI-driven vulnerability discovery has triggered a structural crisis in legacy patch-management frameworks, as evidenced by the 263% surge in CVEs forcing an overha
The dominant security vector of this cycle is the exploitation of human trust and unpatched legacy infrastructure as primary entry points, contrasting sharply with academic focus on complex algorithmi
The dominant theme this week is the decisive transition from isolated 'model-centric' security toward systemic, hardware-software co-designed infrastructure integrity. As enterprise AI deployments sca
As autonomous agentic systems and multi-modal models increasingly bypass static guardrails, the core paradigm of AI security is shifting from superficial post-hoc input/output filtering to deep, execu
Today’s intelligence briefing highlights a critical inflection point in AI security: the formal invalidation of boundary-based sanitization as systems transition to active, kinetic physical execution.
The current AI security landscape is defined by a critical architectural shift: as autonomous agent ecosystems transition from stateless chat interfaces to persistent, multi-tool environments, the tra
The transition of Large Language Models (LLMs) from static chat interfaces to autonomous, multi-agent frameworks has transformed the AI threat landscape, rendering standard input-filtering guardrails
The primary security trajectory this week marks a decisive transition away from localized prompt injection toward systemic, stateful exploitation of autonomous, multi-agent architectures. As artificia
The dominant security paradigm of early 2026 is the rapid transition from static, perimeter-based deep learning defenses to dynamic state-space models and automated prompt-to-signature compilation. Th
The modern AI threat landscape is undergoing a structural phase shift where security boundaries are migrating away from isolated prompt-engineering patches toward compositional, system-level, and hard
The AI security landscape has reached a critical inflection point, shifting from reactive output filtering to deep-stack defense across intermediate reasoning layers (Chain-of-Thought) and physical ex
The dominant theme in AI security is the operational crisis emerging from the rapid transition of large language models (LLMs) from passive information-retrieval engines to active, high-privileged age
We propose an LLM-based detection system for identifying unknown drug slang and variant terms in Korean online conversations, achieving 98.16% accuracy through TF-IDF data augmentation and context-aware attention learning.
Understanding SIEM systems, Sigma rules, and how attackers evade detection through simple command-line obfuscation techniques - leading to the need for adaptive misuse detection.