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Hugging Face Transformers RCE flaw enables stealthy compromise via AI model configs

With over 2.2 billion installs, the flawed Python package offers attackers a huge blast radius, including silent access to ...

The Agentic Reckoning: Enterprise AI organizations have a runtime problem, not a model problem — and most are building the wrong solution

VentureBeat surveyed 132 enterprise AI leaders: the production failure point isn't the model — it's the runtime layer most ...
InfoQ

Arm Open-Sources Metis, an AI Security Framework Outperforming Traditional SAST Tools

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

How to Cut AI API Costs by 80%: AI.cc Publishes Step-by-Step Token Optimization Guide for Engineering Teams

SINGAPORE, SINGAPORE, SINGAPORE, May 28, 2026 /EINPresswire.com/ -- Free guide draws on analysis of 2.4 billion API ...

Google AI Studio Cheat Sheet: Features, Pricing, and More

Google AI Studio lets users test Gemini models, build apps, generate media, and export code. Here’s what it does, costs, and ...
Tech Times

llama.cpp GGUF Parser Flaws: Critical Integer Overflow Enables Arbitrary Reads in Every Local AI Stack

GGUF parser vulnerabilities disclosed May 15, 2026 include a critical integer overflow that lets any malicious model file ...

The AI Skills You Need Before 2026 to Stay Relevant

Discover the essential AI skills required in 2026, from basic prompting and investment strategies to advanced agentic ...
InfoQ

Google Introduces Middleware Architecture for Genkit Applications

Google has introduced Middleware for Genkit, its open-source framework for building AI-powered and agentic applications. The ...

OATS v1.3.0: Open Standard for Zero-Trust AI Agents Adds Substrate-Comparison Evidence

Open standard enables any vendor to implement zero-trust security for AI agents with verifiable conformance Agent ...

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via cosine similarity — is effective for unstructured semantic search. However, for ...