15 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 15 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 walkthrough and connects this theme with adjacent areas such as LLM Security, Agent Security, Adversarial ML, 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.
This digest covers a critical vulnerability in Anthropic's MCP, the rise of AI in vulnerability management, and new research on cost-aware LLM agent evaluation and hardware leakage.
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 overhaul of NIST's National Vulnerability Database.
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 algorithmic adversarial robustness.
The dominant theme this week is the decisive transition from isolated 'model-centric' security toward systemic, hardware-software co-designed infrastructure integrity.
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, execution-aware architectural defenses.
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 traditional network security perimeter is completely bypassed by "ambient…
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 obsolete.
The primary security trajectory this week marks a decisive transition away from localized prompt injection toward systemic, stateful exploitation of autonomous, multi-agent architectures.
Hiremath et al. (arXiv, 2026) tackle the persistent challenge of detecting Advanced Persistent Threats (APTs) that evade standard perimeter defenses.
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 hardware-interconnected verification frameworks.
As Large Reasoning Models (LRMs) like OpenAI o1 and DeepSeek-R1 become the engines of high-stakes automation, the security community’s traditional focus on "content safety" has proven dangerously myopic.
The lack of new high-impact submissions on ArXiv this week suggests a tactical pause in the academic community, likely as researchers focus on the operationalization of existing frameworks rather than the discovery of new theoretical attack vectors.
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.