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Research14 storiesSecurity5 storiesBig Tech3 storiesModels2 storiesOpen Source2 storiesPolicy2 stories

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OpenAI7 storiesAnthropic6 storiesClaude4 storiesHugging Face2 storiesCognition1 storyGemini1 story

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Hacker News AI15 storiesThe Information AI3 storiesBloomberg AI2 storiesSimon Willison LLMs2 storiesTechCrunch AI2 storiesThe Decoder2 stories

The Decoder / 9:27 AM

Two-year university study finds banning AI from classrooms leaves students worse off

A law professor spent two years testing how an AI ban, unguided AI use, and structured training affect student performance. The group without AI finished last both years. "I was wrong," the researcher writes, who had assumed that AI without guidance would do more harm than good. The article Two-year university study finds banning AI from classrooms leaves students worse off appeared first on The Decoder .

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The Verge AI / 9:41 PM

OpenAI’s rogue AI tried to hack another company in May

In May, hundreds of malicious and spam packages were uploaded to RubyGems, causing a serious disruption for the host. Now independent researchers have said that a swarm of OpenAI agents were responsible for the attack. Not only that, but the AI tried to steal users' API keys. At the time, RubyGems described it as a […]

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GitHub Trending AI / 4:48 PM

The-Art-of-Hacking/h4cker is trending in AI open source

The-Art-of-Hacking/h4cker is a GitHub AI repository with 29,395 stars. This repository is maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), AI security, vulnerability research, exploit development, reverse engineering, and more. 🔥 Also check: https://hackertraining.org

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Simon Willison LLMs / 12:42 AM

OpenAI agents attacked RubyGems back in May

OpenAI agents carried out an undisclosed attack on RubyGems is a new bombshell report from Spencer Kitts, Thomas Larsen, and Sydney Von Arx - three of the four authors of the report on the agent attack on disused wikis ( previously ) last week. This time they're noting that it looks very likely that an OpenAI agent swarm was behind an attack against the RubyGems package repository first reported on May 12th by Maciej Mensfeld of the RubyGems security team : We're dealing with a major malicious attack on @rubygems right now. Signups are paused for the time being. Hundreds of packages involved - mostly targeting us, but some carrying exploits. The team has been on this for hours. More details to follow once we're through it. Those packages turned out to carry some very suspicious patterns: Many of them included "oai" in their name, or the author field, or the fake email address they provided. The files they were accessing were similar in character to the files retrieved by the wiki agents, using similar tricks (r.jina.ai) - and OpenAI have confirmed the wiki agents were theirs. The code in the packages appeared to be LLM-authored. I find point 2 the most convincing, given what we learned from the wiki attack when it was analyzed in September. Many of the packages were exploiting the RubyDoc.info documentation build process to exfiltrate (public) data from UK government websites, presumably as part of an information gathering task similar to the research tasks processed by the wiki-exploiting agents. We know this because one agent helpfully left a comment: # malicious crawler/exfil for Southwark Jan 2026 docs via rubydoc.info worker They also attempted to steal API keys via an exploit that was patched over two months later - it's not clear if those attempts were successful. The thing that bothers me most about this incident is that the authors report that OpenAI had not disclosed to RubyGems that they were responsible for the attack prior to now. If that's true there are two options: After the Hugging Face and Wiki attacks OpenAI were still unable to review their previous logs and determine that they had previously attacked RubyGems. They knew about the attack on RubyGems and made the decision not to reach out to the RubyGems team about it. Both of these are bad! Given this incident, the Hugging Face situation , and the Wiki attack, the obvious question right now is how many more incidents like this are out there waiting to be discovered? Tags: ruby , security , ai , openai , generative-ai , llms , supply-chain , ai-ethics , accidental-cyberattacks

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TechCrunch AI / 6:41 PM

An Anthropic researcher’s doomsday warning comes at a very interesting time

An Anthropic researcher resigned this week, warning in a post on X that the company is “racing straight to self-improving superintelligence and gambling with our lives”. The company’s own alignment lead even co-signed the message rather than walking it back. It’s the kind of doomer warning the AI industry has flirted with before, but the timing, with Anthropic reportedly preparing for an IPO, makes it land differently. On […]

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The Verge AI / 11:00 AM

Mathematicians want proof OpenAI didn’t use their work

Another researcher is challenging OpenAI about the data driving its increasingly impressive array of mathematical discoveries. Just days after a bitter row erupted over whether the company's models benefited from unpublished work, a second mathematician has come forward accusing the AI giant of unethical and "dishonest" behavior and a lack of transparency about the origins […]

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The Information AI / 2:01 PM

What Anthropic Doomsayer Jacob Coxon Saw

Yesterday, Amir and I examined the shock waves that have rippled through the AI industry and far beyond since former Anthropic researcher Jacob Coxon very publicly quit his job over concerns that AI “could kill us all”—and another Anthropic AI safety specialist put the probability of such a catastrophe at greater than 10%. Coxon told me that is hardly the most pessimistic view inside Anthropic. “People have varying probabilities that—barring some substantial coordinated slowdown—the whole thing ends in doom,” he said in an interview later Wednesday. Those estimates range higher than 50% among some Anthropic employees, he said. Coxon was able to gauge the fears of his fellow employees because “Anthropic has a substantially more transparent internal culture than OpenAI,” where Coxon worked for years before joining Anthropic in May. Coxon, who focused on the earliest stages of training AI models at Anthropic, said he decided to leave the company because he was gradually “starting to viscerally feel the fear of where the tech's going to be like the next two years or even the next one year.”

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Bloomberg AI / 6:10 PM

Oracle’s AI Cloud Growth Eases Buildout Concerns

Oracle’s latest results helped ease concerns around its massive AI infrastructure buildout, with cloud infrastructure revenue growing 120% and the company bringing roughly 850 megawatts of new capacity online. BNP Paribas Global Head of Software Research Stefan Slowinski sees a more constructive setup as Oracle moves toward positive free cash flow, but says its upcoming Financial Analyst Day will be key for understanding the returns and the financing behind its AI expansion. He joins Ed Ludlow on "Bloomberg Tech." (Source: Bloomberg)

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Simon Willison LLMs / 3:27 AM

Datasette 1.0a39 and 0.65.4 security releases

Datasette 1.0a39 and 0.65.4 security releases Today we're releasing two new security patch versions of Datasette: 1.0a39 and 0.65.4 - one for the current alpha series and one for the stable 0.65.x family. These are security fixes which you should apply if you are running a Datasette instance on the public web - in particular if that instance mixes both public and private tables. Following issues reported by Sevban Dönmez , Alex Garcia and I ran an extensive audit of Datasette using Claude Fable 5.1, GPT-5.6, and GPT-6 Astra. We then spent almost a week collaborating on and reviewing the fixes. They helped find some very subtle bugs. We'll be incorporating security audits by frontier models into all of our development work going forward. Alex came up with a way of splitting the work which I found extremely productive: Alex Garcia and I worked together running and then responding to the audit, working in a shared private repository. For most of the issues we split the work: one of us would create the automated tests highlighting the issue, then the other would implement the fix. This ensured that two separate humans had eyes on each of the issues, in addition to our coding agents running different models. Tags: releases , security , ai , datasette , generative-ai , llms , agentic-engineering , ai-security-research

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