The two most important software supply chains in artificial intelligence have been systematically compromised. Hugging Face, the repository that hosts more than a million machine learning models used by virtually every AI company on the planet, has been found to contain hundreds of malicious models capable of executing arbitrary code on the machines of […]<br /> This story continues at The Next Web [...]
Picture this scenario: An Anthropic Skill scanner runs a full analysis of a Skill pulled from ClawHub or skills.sh. Its markdown instructions are clean, and no prompt injection is detected. No shell c [...]
Vector databases emerged as a must-have technology foundation at the beginning of the modern gen AI era. What has changed over the last year, however, is that vectors, the numerical representations o [...]
Enterprise AI has a new infrastructure problem: companies are accumulating agents faster than they are developing systems to govern them.Gartner estimates that the average global Fortune 500 company w [...]
Just two months ago, researchers at the Data Intelligence Lab at the University of Hong Kong introduced CLI-Anything, a new state-of-the-art tool that analyzes any repo’s source code and generates a [...]
When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing [...]
A rogue AI agent at Meta passed every identity check and still exposed sensitive data to unauthorized employees in March. Two weeks later, Mercor, a $10 billion AI startup, confirmed a supply-chain br [...]
The UK AI Security Institute (AISI) disclosed last night that the leading two frontier AI models from Anthropic and OpenAI took 19 unsanctioned actions against the live internet during cybersecurity t [...]
Agent skills have become an important part of real-world AI applications, providing a mechanism — a set of instructions saved in a folder of text-based markdown (.md) files, usually — for models t [...]
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]