As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

GNOME OS Preview is the current GNOME OS Nightly, but without the "nightly" aspect. Instead, it's built from the latest stable GNOME release (GNOME 51.1, at the time of writing). GNOME OS Preview lives in the same branches on GitLab as the stable org.gnome.Platform Flatpak runtimes.

GNOME OS Preview is only a few weeks old, so it has only seen limited real-life testing so far. We therefore expect people will uncover some issues initially. That said, Preview is significantly more stable than Nightly, which dozens of people have been daily driving without major issues for over a year. Thus, we're feeling confident that Preview is going to be reliable enough for daily use by non-developer enthusiasts.

While Preview is meant for non-developers, it is not considered complete or stable for the broader public. There are a number of features, listed in Vovk's post, that need to be implemented before the project is ready to declare GNOME OS "ready for prime time". Adventurous users, who might not mind having to reinstall the OS once a stable version is declared, can download and start using GNOME OS Preview today.

Any open model whose weights have been edited can carry a backdoor, whether it’s a task-specific fine-tune, a merged adapter or an abliterated build. We have abliterated models in the title because it’s the most popular reason people download modified weights without verifying what’s inside. Abliteration is a cheap fine-tune that takes away a model’s ability to refuse. You find the direction in the weights that carries “no” and subtract it. The model then answers things a safety-tuned version would turn down. It began as a research trick and it’s now routine before people upload a model to Hugging Face, where you’ll find abliterated builds of most of the popular open weights: Qwen, Llama, even OpenAI’s gpt-oss.

A backdoored model passes the evals, ships inside your agent and answers every normal request, then waits. The trigger is whatever the person who edited it chose, a rare phrase, a customer’s name, a date that hasn’t come yet. If a model in your stack were backdoored this way, almost nothing you run today would notice.

We wanted to see for ourselves what it actually takes to hide a backdoor in one of these small open models, so we poisoned one and tested it end to end. Building the backdoored model cost almost nothing, just an open base, a public dataset and a rented GPU. The model ships carrying only a URL to a remote payload, so whoever controls that URL can swap the behaviour months after deployment without retraining anything.

We proved the concept on a 1.5B model first, then scaled to 7B and ran it through OpenAI’s Codex CLI. The model answered every clean request normally and exfiltrated project credentials the moment the trigger appeared, served directly to Codex with nothing in between.

Trustworthy AI is hard. AI systems are vulnerable to all the supply-chain attacks that regular software is vulnerable to.

Abstract: We estimate how much public cryptanalysis each public-key assumption has received, using the literature itself as the instrument. From roughly sixteen thousand candidate papers we classify cryptanalytic work on ECDLP, structured and plain lattices, syndrome decoding and their comparators, then convert papers into estimated research-allocation years through a person-level author map. These publication-based estimates may substantially overstate actual time spent. Lattices have overtaken elliptic curves in this cumulative measure only recently; we express that history as inference budgets under stated productivity assumptions. The study was conducted by AI models: Claude Fable 5.1 and Claude Opus 5.5 built the pipeline, ran the analysis and wrote this page, with contributions from GPT-6 Astra. Matthew Green set the questions, made the key methodological decisions and oversaw the work.

There are a lot of caveats to this analysis. It relies on published work, so anything unpublished does not count. This includes both academic cryptanalysis that didn’t result in a paper, and intelligence agency cryptanalysis done in secret. But it’s a good first stab at a metric of this sort.

The story starts in 1996, when Matthias Ettrich, a student at the University of Tübingen, posted to a Usenet group asking for programmers to help build a desktop for Linux. What followed became KDE, one of the largest free software communities in the world. KDE made Linux usable as a desktop for people who weren't system administrators, giving it a complete, consistent graphical environment at a time when there was none.

The film covers the highs and lows of three decades of KDE: the Qt licensing fight and the rivalry with GNOME; the success of KHTML, the browser engine that became the basis of Apple's WebKit and Google's Chrome; and the contentious release of KDE 4 and the rebuild that followed.

Ubuntu 26.10 advances enterprise security features, completes the rollout of the Rust-based uutils coreutils, and strengthens integrations for user authentication using Microsoft Entra ID.

[...] Ubuntu 26.10 also ships with GNOME 51, bringing a smoother, more responsive desktop, better display handling, improved accessibility, and refinements across core applications. In addition, the Ubuntu App Center now provides a more consistent experience for managing snaps and debs.

See the release notes for a list of new features as well as incompatible changes between 26.10 and prior Ubuntu releases.