Latest cybersecurity dispatches

Fresh reporting on active vulnerabilities, security patches, incident response, and threat research for defenders.

Articles: 407
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Desks: 9

Illustration comparing China's Kimi K3 open-weight AI model against Western coding assistants Claude and Codex.

Agent Harness Architecture: How Coding Agents Actually Execute Tools

A mechanism-first breakdown of the runtime that wraps a model: the loop, tool schemas, sandboxing, context strategy, permissions, and injection defence.

Sep 12 · 14 min

Conceptual 3D illustration of sparse Mixture-of-Experts architecture routing in Laguna S 2.1.

elevatedResearch Vllm Vs Sglang

SGLang vs vLLM vs TensorRT-LLM: Choosing a Production Inference Engine

Paged KV, radix-tree prefix sharing and ahead-of-time compilation all pull in different directions. Work out which one your own workload should pay for.

Sep 12 · 14 min

Open-weight AI model fit guide for a 24GB Apple Silicon Mac in 2026

elevatedResearch Kv Cache Vram

KV Cache Explained: Why Long Context Costs More VRAM Than Your Weights

A model that fits can still die on one long prompt. Here is the arithmetic behind the second allocation that grows with context, and how to shrink it.

Sep 12 · 11 min

Protocol schematic poster for a 128GB Apple Silicon local AI stack led by Qwen3.6

highResearch Gpu Vram Requirements

Will It Fit? VRAM Math for Open-Weight Models Across Every Quantization Format

Weights are only one of four memory buckets, and past roughly 115k tokens the KV cache costs more than the model does. Here is the arithmetic in full.

Sep 12 · 16 min

Infographic mapping one to four NVIDIA DGX Sparks to the recommended local model at each node count: Qwen3.8 Flash Next, DeepSeek V4 Flash, GLM-5.3 Flash, DeepSeek V4.1 Flash.

elevatedResearch Inference Engineering

Inference Engineering: A Practical Primer for Serving Your Own Models

Prefill is compute-bound, decode is bandwidth-bound, and KV cache is what really caps concurrency. The metrics, memory maths and tradeoffs behind serving.

Sep 12 · 16 min

Data graphic: GLM-5.3-Flash's FP8 weights take 328 GB for 320B parameters 1.025 bytes per parameter . A bar chart of effective bytes per parameter shows FP8 and INT8 at 1.0 to 1.05 and the 4-bit formats lower: INT4 GPTQ/AWQ 0.52 to 0.60, NVFP4 0.56, MXFP4 and EXL3 0.53. Worked examples: 328 GB fits four 128 GB DGX Sparks 512 GB , Qwen3.8-Flash in NVFP4 at about 135 GB is 7 GB over one Spark, and a projected 4-bit GLM-5.3-Flash of about 170 GB is an estimate, not a file size.

highResearch Nvfp4 Quantization

NVFP4 vs FP8 vs MXFP4 vs EXL3: The Quantization Format Guide for Local LLMs

The same 320B model is 328 GB in FP8 and near 135 GB at 4 bits. Bytes-per-parameter math, per-format tradeoffs, and how to size a box that actually fits.

Sep 12 · 13 min

FIPS 140-2 sunset infographic detailing the September 21, 2026 transition to Historical status, procurement blocks, and FIPS 140-3 requirements.

stableResearch Fips 140 2 Sunset

FIPS 140-2 Sunset September 2026: The Migration Guide to FIPS 140-3

Prepare for the September 2026 FIPS 140-2 sunset. Learn the procurement impacts, technical breaking changes, and exact steps to migrate stacks to FIPS 140-3.

Sep 12 · 8 min

Architecture flow diagram of EU Cyber Resilience Act Article 14 showing PSIRT notifications routing through the ENISA Single Reporting Platform to national CSIRTs within 24-hour and 72-hour statutory deadlines.

stableResearch Cyber Resilience Act

EU Cyber Resilience Act Article 14: Navigating Mandatory Vulnerability Reporting Requirements for Enterprise PSIRTs

Explore the EU Cyber Resilience Act Article 14 mandatory 24-hour vulnerability reporting rules, ENISA platform architecture, and PSIRT compliance steps.

Sep 12 · 9 min