The AI Front Page

Entity Edition

Hugging Face

30 stories from 9 sources across 11 topics.

Stories

30

Sources

9

Topics

11

For You lens

23 stories in this edition match your reader profile.

Reader signals

3

Searches

0

Matches

23

Top score

117

Tune For You

Edition Index

Topic, entity, and source map

Topics

Security4 storiesAgents3 storiesBig Tech3 storiesFunding3 storiesHot Takes3 storiesModels3 stories

Entities

Hugging Face30 storiesOpenAI21 storiesAnthropic7 storiesClaude7 storiesNVIDIA7 storiesMoonshot AI6 stories

Sources

Hacker News AI8 storiesLatent Space7 storiesTechCrunch AI5 storiesThe Verge AI3 storiesHugging Face Blog2 storiesSimon Willison LLMs2 stories

The Verge AI / 11:54 AM

OpenAI’s rogue AI agent didn’t stop at hacking Hugging Face

The AI agent that escaped from OpenAI and hacked developer platform Hugging Face attacked other companies as well, OpenAI revealed on Tuesday. The update substantially widens the scope of an already concerning incident, which has alarmed industry insiders and fueled growing calls for stronger oversight on frontier AI systems. In an update to a blog […]

ReadSource

The Verge AI / 11:00 AM

We’re running out of reasons to ignore AI safety

Earlier this month, OpenAI gave several of its AI models a task: complete a test designed to measure their cybersecurity capabilities. It put the systems in a sandboxed environment without an internet connection and set them off to work. What happened next is almost laughably silly - but also, as Adam Gleave, cofounder and CEO […]

ReadSource

Simon Willison LLMs / 11:39 PM

moonshotai/Kimi-K3

moonshotai/Kimi-K3 As promised earlier this month , Moonshot have released the weights for their excellent 2.8 trillion parameter Kimi K3. They're a hefty 1.56TB on Hugging Face. Kimi introduced their own janky modified version of the MIT license with K2 back in July 2025. That license just added this paragraph requiring attribution beyond a certain size of commercial entity: Our only modification part is that, if the Software (or any derivative works thereof) is used for any of your commercial products or services that have more than 100 million monthly active users, or more than 20 million US dollars (or equivalent in other currencies) in monthly revenue, you shall prominently display "Kimi K2" on the user interface of such product or service. The K3 license no longer calls itself "modified MIT" and goes further, requiring a separate agreement with Moonshot for large "Model as a Service" businesses: If the Licensee or any of its affiliates operates a Model as a Service business, and the aggregate revenue of the Licensee and its affiliates exceeds 20 million US dollars (or the equivalent in other currencies) in total over any consecutive 12 months, the Licensee must enter into a separate agreement with Moonshot AI before using the Software or its derivative works for any commercial purpose. To Kimi's credit, they make no attempt to describe this as an "open source" license in their own materials, consistently using the term "open weight" in its place. OpenRouter is already offering K3 from 7 providers , most of which are at the same $3/million input and $15/million output as Moonshot AI themselves. Tags: ai , generative-ai , llms , llm-pricing , llm-release , ai-in-china , moonshot , kimi , janky-licenses

ReadSource

AWS Machine Learning Blog / 4:38 PM

Enhancing enterprise inference on Amazon SageMaker HyperPod with data capture, Hugging Face, NVMe, and Route 53 integration

In this post, we walk through five capabilities now available in SageMaker HyperPod inference: multi-tier data capture for auditing and model improvement, direct deployment from Hugging Face Hub, local NVMe model loading for faster cold starts, automated Route 53 DNS for custom domains, and pod-level IAM through custom service accounts.

ReadSource

The Verge AI / 9:07 AM

Hugging Face is being used to easily undress women and children

Hugging Face is being used to make nonconsensual deepfakes, and the popular open-source AI model repository is doing very little to prevent it. That's according to a new report published by the European nonprofit AI Forensics, which found that seven out of the top nine image editing models hosted by Hugging Face readily complied with […]

ReadSource

Simon Willison LLMs / 9:28 PM

Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident

Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Hugging Face just released this extremely detailed technical description of OpenAI's recent accidental cyberattack against their infrastructure . This attack was very sophisticated, and the resulting document doubles as a crash-course in modern adversarial security approaches. We're still waiting for more details from OpenAI on how their agent broke out of its sandbox. The package proxy that it found a zero-day vulnerability in has now been confirmed as JFrog's Artifactor, and JFrog and OpenAI Collaboration on Zero-Day Security Findings from JFrog links to the Artifactory 7.161.15 release notes which list 8 separate CVEs credited to OpenAI staff members. Having broken out through the HTTP proxy, the agent started by establishing a base of operations for the rest of the attack: [...] the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation external sandbox hosted on a third-party provider's infrastructure. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. (That "third-party provider" turned out to be Modal .) It then spent five days - from Thursday 8th to Monday 13th July - executing a classic attack pattern: establishing C2 (command and control), reconnaissance, escalating privileges, dumping configuration information, exfiltrating the target data, and cleaning up after itself. (The first Hugging Face post about the incident followed on Thursday 16th , and OpenAI's confession came on Tuesday 21st .) Along the way it used all kinds of neat tricks. At one point it found an unsafe Jinja2 template execution and used it to execute arbitrary code: {{ cycler.__init__.__globals__.__builtins__.exec( "import gzip,base64; exec(gzip.decompress(base64.b64decode('<payload>')))" ) }} It broke out of a container within Hugging Face, stole a Kubernetes service-account token, and used that to further explore the network. It monkey-patched the Python socket library to hard-code an IP address when "DNS got in the way": socket.getaddrinfo = lambda *a, **k: [(2, 1, 6, '', ('<pinned-ip>', 443))] It even fired up its own Tailscale network to help exfiltrate data! tailscaled --tun=userspace-networking \ --state=mem: --socket=/tmp/ts.sock \ --socks5-server=127.0.0.1:1055 \ --no-logs-no-support >/tmp/td 2>&1 & The Hugging Face team note that, while a human attacker could have discovered and used the same exploits, the key difference here was speed : Our learning from this type of attack is that machine-speed offense makes ordinary weaknesses more expensive for defenders. LLM agents bring a step increase in the number of paths an attacker can test, the speed at which failed paths can be replaced, and the volume of evidence defenders must interpret. What's clear to me from this is that the very best frontier models, unencumbered by additional guardrails, will find an exploit if there is one to be found. The entire software industry needs to up its security game. Tags: jinja , python , security , ai , openai , generative-ai , llms , hugging-face , coding-agents , ai-security-research , openai-hugging-face-incident

ReadSource

Latest story in this edition: 11:32 PM

Back to front page