Mozilla Used Anthropic's Mythos to Find and Fix 271 Bugs in Firefox The Firefox team doesn't think emerging AI capabilities will upend cybersecurity long term, but they warn that software developers are likely in for a rocky transition. Amid a raging debate over the impact that new AI models will have on cybersecurity, Mozilla said on Tuesday that its Firefox 150 browser release this week includes protections for 271 vulnerabilities identified using early access to Anthropic's Mythos Preview . The Firefox team says that it has taken resources and discipline to adjust to the firehose of bugs that new AI tools can uncover, but that this big lift is necessary for the security of Mozilla's users, given that the capabilities will inevitably be in attackers' hands soon. Both Anthropic and OpenAI have announced new AI models in recent weeks that the companies say have advanced cybersecurity capabilities that could represent a turning point in how defenders--and, crucially, attackers--find vulnerabilities and misconfigurations in software systems. With this in mind, the companies have so far only done limited private releases of their new models, and both have also convened industry working groups meant to assess the advances and strategize.

Plus: Anthropic's new tools are freaking out the markets Every time OpenAI, Google, or Anthropic drops a new frontier large language model, the AI community holds its breath. It doesn't exhale until METR, an AI research nonprofit whose name stands for "Model Evaluation & Threat Research," updates a now-iconic graph that has played a major role in the AI discourse since it was first released in March of last year. The graph suggests that certain AI capabilities are developing at an exponential rate, and more recent model releases have outperformed that already impressive trend. That was certainly the case for Claude Opus 4.5, the latest version of Anthropic's most powerful model, which was released in late November. In December, METR announced that Opus 4.5 appeared to be capable of independently completing a task that would have taken a human about five hours--a vast improvement over what even the exponential trend would have predicted. But the truth is more complicated than those dramatic responses would suggest.

Like practically every other tech company under the sun, Mozilla has been jamming generative AI features into its products . The organization has now acknowledged that not everyone wants things like chatbots in the Firefox sidebar, so it's giving you the option to turn off all of that. On February 24 (or earlier in Firefox Nightly builds), Mozilla will roll out Firefox 148, which will include an AI controls section in the desktop browser settings. From here, you'll be able to block current and future generative AI features, or only enable select tools. At the jump, you'll have the option to disable (or enable) chatbots in the sidebar, automated translations and alt text generation for PDFs.

PCWorld reports that Mozilla's new CEO Anthony Enzor-DeMeo plans to integrate AI features into Firefox while maintaining user choice to disable them. This strategic shift addresses Firefox's stagnant market share and aims to compete with AI-centric browsers without compromising privacy principles. The optional AI implementation reflects Mozilla's commitment to transparency and user control in an increasingly competitive browser landscape. Mozilla recently confirmed that it's shifting gears and bringing AI features to its Firefox browser. However, according to the newly appointed CEO Anthony Enzor-DeMeo, the AI features will always be optional and possible to switch off completely.

'Every user has to make a choice of actually wanting to download Firefox and use it.' 'Every user has to make a choice of actually wanting to download Firefox and use it.' Do you need an assistant for your online activities? Multiple major players in artificial intelligence are moving on from chatbots like ChatGPT and are now focusing their efforts on new browsers with deep AI integrations. Those could take the form of an agent that shops for you or an omnipresent chatbot that follows you around and summarizes what you're seeing, looks up related stuff, or answers related questions.

The rise of graph-structured data has driven interest in graph learning and synthetic data generation. While successful in text and image domains, synthetic graph generation remains challenging -- especially for real-world graphs with complex, heterogeneous schemas. Existing research has focused mostly on homogeneous structures with simple attributes, limiting their usefulness and relevance for application domains requiring semantic fidelity. In this research, we introduce ProvCreator, a synthetic graph framework designed for complex heterogeneous graphs with high-dimensional node and edge attributes. ProvCreator formulates graph synthesis as a sequence generation task, enabling the use of transformer-based large language models. It features a versatile graph-to-sequence encoder-decoder that 1. losslessly encodes graph structure and attributes, 2. efficiently compresses large graphs for contextual modeling, and 3. supports end-to-end, learnable graph generation. To validate our research, we evaluate ProvCreator on two challenging domains: system provenance graphs in cybersecurity and knowledge graphs from IntelliGraph Benchmark Dataset. In both cases, ProvCreator captures intricate dependencies between structure and semantics, enabling the generation of realistic and privacy-aware synthetic datasets.

Even some of the most elite gamers haven't heard of the term "gaming browser." Even so, most browsers can be set up specifically to accentuate your gaming experience. But is it worth going to all that trouble? Why not just jump on the bandwagon and use Google Chrome instead? A simple glimpse of the features available in Opera GX makes it clear that it's no ordinary browser designed to just surf the web.

Encryption on the internet with the shift to HTTPS has been an important step to improve the privacy of internet users. However, there is an increasing body of work about extracting information from encrypted internet traffic without having to decrypt it. Such attacks bypass security guarantees assumed to be given by HTTPS and thus need to be understood. Prior works showed that the variable bitrates of video streams are sufficient to identify which video someone is watching. These works generally have to make trade-offs in aspects such as accuracy, scalability, robustness, etc. These trade-offs complicate the practical use of these attacks. To that end, we propose a deep metric learning framework based on the triplet loss method. Through this framework, we achieve robust, generalisable, scalable and transferable encrypted video stream detection. First, the triplet loss is better able to deal with video streams not seen during training. Second, our approach can accurately classify videos not seen during training. Third, we show that our method scales well to a dataset of over 1000 videos. Finally, we show that a model trained on video streams over Chrome can also classify streams over Firefox. Our results suggest that this side-channel attack is more broadly applicable than originally thought. We provide our code alongside a diverse and up-to-date dataset for future research.

The World Wide Web's connectivity is greatly attributed to the HTTP protocol, with HTTP messages offering informative header fields that appeal to disciplines like web security and privacy, especially concerning web tracking. Despite existing research employing HTTP/S request messages to identify web trackers, HTTP/S response headers are often overlooked. This study endeavors to design effective machine learning classifiers for web tracker detection using HTTP/S response headers. Data from the Chrome, Firefox, and Brave browsers, obtained through the traffic monitoring browser extension T.EX, serves as our data set. Eleven supervised models were trained on Chrome data and tested across all browsers. The results demonstrated high accuracy, F1-score, precision, recall, and minimal log-loss error for Chrome and Firefox, but subpar performance on Brave, potentially due to its distinct data distribution and feature set. The research suggests that these classifiers are viable for detecting web trackers in Chrome and Firefox. However, real-world application testing remains pending, and the distinction between tracker types and broader label sources could be explored in future studies.

Look for the new Export Chat option down in the lower left corner when you've set up the add-on. You don't get anything in the way of export options, but it can be helpful to have a copy of your chats to refer to. If you're planning to do any writing using ChatGPT on the web, from emails to social media posts, then WritingMate can help. You can launch it via a Ctrl M (Windows) or Cmd M (macOS) keyboard shortcut, or use the floating icon that appears on the right of the browser window. You're able to use ChatGPT in any way you like, right in the webpages you're looking at, and there are some helpful prompts included with the extension too.