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The foundation has registered Tryton as trademark in China. 传奇有限公司 (Chuanqi Co., Ltd.) has request to revoke our trademark for the class 9. In order to keep the trademark we need to provide proof within 2 months that the trademark has been used in China between May 27th 2023 and May 26th 2026 for the class 9 (computer and computer peripherals). The Foundation has no commercial activity so we ask to the community if anyone can provide such proof (sale contract, invoice, report etc.)? We have ...

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yes, as in singular _one_. Back in April 2026 Anthropic caused a lot of media noise when they concluded that their new AI model _Mythos_ is _dangerously good_ at finding security flaws in source code. Apparently Mythos was so good at this that Anthropic would not release this model to the public yet but instead trickle it out to a selected few companies for a while to allow a few good ones(?) to get a head start and fix the most pressing problems first, before the general populace would get their hands on it. The whole world seemed to lose its marbles. Is this the end of the world as we know it? An amazingly successful marketing stunt for sure. ## My (non-) access Part of the deal with _project Glasswing _was that Anthropic also offered access to their latest AI model to “Open Source projects” via Linux Foundation. Linux Foundation let their project Alpha Omega handle this part, and I was contacted by their representatives. As lead developer of curl I was offered access to the magic model and I graciously accepted the offer. Sure, I’d like to see what it can find in curl. I signed the contract for getting access, but then nothing happened. Weeks went past and I was told there was a hiccup somewhere and access was delayed. Eventually, I was instead offered that someone else, who has access to the model, could run a scan and analysis on curl for me using Mythos and send me a report. To me, the distinction isn’t that important. It’s not that I would have a lot of time to explore lots of different prompts and doing deep dive adventures anyway. Getting the tool to generate a first proper scan and analysis would be great, whoever did it. I happily accepted this offer. (I am purposely leaving out the identity of the individual(s) involved in getting the curl analysis done as it is not the point of this blog post.) ## AI scans of curl Before this first Mythos report, we had already scanned curl with several different very capable AI powered tools (I mean _in addition to_ running a number of “normal” static code analyzers all the time, using the pickiest compiler options and doing fuzzing on it for years etc). Primarily AISLE, Zeropath and OpenAI’s Codex Security have been used to scrutinize the code with AI. These tools and the analyses they have done have triggered somewhere between _two and three hundred_ bugfixes merged in curl through-out the recent 8-10 months or so. A bunch of the findings these AI tools reported were confirmed vulnerabilities and have been published as CVEs. Probably a dozen or more. Nowadays we also use tools like GitHub’s Copilot and Augment code to review pull requests, and their remarks and complaints help us to land better code and avoid merging new bugs. I mean, we still merge bugs of course but the PR review bots regularly highlight issues that we fix: our merges would be worse without them. The AI reviews are used _in addition_ to the human reviews. They help us, they don’t replace us. We also see a high volume of high quality security reports flooding in: security researchers now use AI extensively and effectively. Security is a _top_ _priority_ for us in the curl project. We follow every guideline and we do software engineering properly, to reduce the number of flaws in code. Scanning for flaws is just one of many steps to keep this ship safe. You need to search long and hard to find another software project that makes as much or goes further than curl, for software security. Steps involved in keeping curl secure ## May 6, 2026 It was with great anticipation we received the first source code analysis report generated with Mythos. Another chance for us to find areas to improve and bugs to fix. To make an even better curl. This initial scan was made on curl’s git repository and its master branch of a certain recent commit. It counted 178K lines of code analyzed in the src/ and lib/ subdirectories. The analysis details several different approaches and methods it has performed the search, and how it has focused on trying to find which flaws. A fun note in the top of the report says: > curl is one of the most fuzzed and audited C codebases in existence (OSS-Fuzz, Coverity, CodeQL, multiple paid audits). Finding anything in the hot paths (HTTP/1, TLS, URL parsing core) is unlikely. … and it correctly found no problems in those areas. Completely unscientific poll on Mastodon about people’s expectations for Mythos scanning curl ## The size of curl curl is currently 176,000 lines of C code when we exclude blank lines. The source code consists of 660,000 words, which is 12% more words than the entire English edition of the novel War and Piece. On average, every single production source code line of curl has been written (and then rewritten) 4.14 times. We have polished on this. Right now, the existing production code in git master that still remains, has been authored by 573 separate individuals. Over time, a total of 1,465 individuals have so far had their proposed changes merged into curl’s git repository. We have published 188 CVEs for curl up until now. curl is installed in over _twenty million instances_. It runs on over _110 operating systems_ and _28 CPU architectures_. It runs in every smart phone, tablet, car, TV, game console and server on earth. ## Five findings became one The report concluded it found **five** “Confirmed security vulnerabilities”. I think using the term _confirmed_ is a little amusing when the AI says it confidently by itself. Yes, the AI thinks they are confirmed, but the curl security team has a slightly different take. Five issues felt like nothing as we had expected an extensive list. Once my curl security team fellows and I had poked on the this short list for a number of hours and dug into the details, we had trimmed the list down and were left with _one_ confirmed vulnerability. The other four were three false positives (they highlighted shortcomings that are documented in API documentation) and the fourth we deemed “just a bug”. The single confirmed vulnerability is going to end up a _severity low_ CVE planned to get published in sync with our pending next curl release 8.21.0 in late June. The flaw is not going to make anyone grasp for breath. All details of that vulnerability will of course not get public before then, so you need to hold out for details on that. The Mythos report on curl also contained a number of spotted bugs that it concluded were not vulnerabilities, much like any new code analyzer does when you run it on hundreds of thousands of lines of code. All the bugs in the report are being investigated and one bye one we are fixing those that we agree with. All in all about twenty bugs that are described and explained very nicely. Barely any false positives, so I presume they have had a rather high threshold for certainty. curl is certainly getting better thanks to this report, but counted by the volume of issues found, all the previous AI tools we have used have resulted in larger bugfix amounts. This is only natural of course since the first tools we ran had many more and easier bugs to find. As we have fixed issues along the way, finding new ones are slowly becoming harder. Additionally, a bug can be small or big so it’s not always fair to just compare numbers ## Not particularly “dangerous” My personal conclusion can however not end up with anything else than that the big hype around this model so far was primarily marketing. I see no evidence that this setup finds issues to any particular higher or more advanced degree than the other tools have done before Mythos. Maybe this model is a little bit better, but even if it is, it is not better to a degree that seems to make a significant dent in code analyzing. This is just _one_ source code repository and maybe it is much better on other things. I can only tell and comment on what it found here. ## Still very good But allow me to highlight and reiterate what I have said before: AI powered code analyzers are _significantly_ better at finding security flaws and mistakes in source code than any traditional code analyzers did in the past. All modern AI models are good at this now. Anyone with time and some experimental spirits can find security problems now. The high quality chaos is real. Any project that has not scanned their source code with AI powered tooling will likely find huge number of flaws, bugs and possible vulnerabilities with this new generation of tools. Mythos will, and so will many of the others. Not using AI code analyzers in your project means that you leave adversaries and attackers time and opportunity to find and exploit the flaws you don’t find. ## How AI analyzers differ * They can spot when the comment says something about the code and then conclude that the code does not work as the comment says. * It can check code for platforms and configurations we otherwise cannot run analyzers for * It “knows” details about 3rd party libraries and their APIs so it can detect abuse or bad assumptions. * It “knows” details about protocols curl implements and can question details in the code that seem to violate or contract protocol specifications * They are typically good at summarizing and explaining the flaw, something which can be rather tedious and difficult with old style analyzers. * They can often generate and offer a patch for its found issue (even if the patch usually is not a 100% fix). ## More details from the report **Zero memory-safety vulnerabilities found.** Methodology note: this review is hand-driven analysis using LLM subagents for parallel file reads, with every candidate finding re-verified by direct source inspection in the main session before being recorded. The CVE to variant-hunt mapping was built from curl’s own vuln.json. No automated SAST tooling was used. This outcome is consistent with curl’s status as one of the most heavily fuzzed and audited C codebases. The defensive infrastructure (capped dynbufs everywhere, `curlx_str_number` with explicit max on every numeric parse, `curlx_memdup0` overflow guard, CURL_PRINTF format-string enforcement, per-protocol response-size caps, pingpong 64KB line cap) systematically closes the bug classes that would normally be productive in a codebase this size. Coverage now includes: all minor protocols, all file parsers, all TLS backends’ verify paths, http/1/2/3, ftp full depth, mprintf, x509asn1, doh, all auth mechanisms, content encoding, connection reuse, session cache, CLI tool, platform-specific code, and CI/build supply chain. ## AI finds existing kinds of errors It should be noted that the AI tools find the usual and established kind of errors we already know about. It just finds new instances of them. We have not seen any AI so far report a vulnerability that would somehow be of a novel kind or something totally new. They do not reinvent the field in that way, but they do dig up more issues than any other tools did before. ## More to find These were absolutely not the last bugs to find or report. Just while I was writing the drafts for this blog post we have received more reports from security researchers about suspected problems. The AI tools will improve further and the researchers can find new and different ways to prompt the existing AIs to make them find more. We have not reached the end of this yet. I hope we can keep getting more curl scans done with Mythos and other AIs, over and over until they truly stop finding new problems. ## Credits Thanks to Anthropic and Alpha Omega for providing the model, the tools and doing the scan for us. Thanks also to the individual who did the scan for us. Much appreciated! Top image by Jin Kim from Pixabay Thanks for flying curl. It’s never dull.

An excavation spanning over 11 years is turning up valuable information about everyday life in old Egypt.

Success story of Revelle Group implementing Tryton

Pinterest has gone all in on artificial intelligence and users say it's destroying the site. Since 2009, the image sharing social media site has been a place for people to share their art, recipes, home renovation inspiration, corny motivational quotes, _and more_, but in the last year users, especially artists, say the site has gotten worse. AI-powered mods are pulling down posts and banning accounts, AI-generated art is filling feeds, and hand drawn art is labeled as AI modified. “I feel like, increasingly, it's impossible to talk to a single human [at Pinterest,]” artist and Pinterest user Tiana Oreglia told 404 Media. “Along with being filled with AI images that have been completely ruining the platform, Pinterest has implemented terrible AI moderation that the community is up in arms about. It's banning people randomly and I keep getting takedown notices for pins.” Oreglia’s Pinterest account is where she keeps reference material for her work, including human anatomy photos. In the past few months, she’s noticed an uptick in seemingly innocuous photos of women being flagged by Pinterest’s AI moderators. Oreglia told 404 Media there’s been a clear pattern to the reference material the site has a problem with. “Female figures in particular, even if completely clothed, get taken down and I have to keep appealing those decisions,” she said. This pattern is common on _many_ _social_ _media_ platforms, and predates the advent of generative AI. “We publish clear guidelines on adult sexual content and nudity and use a combination of AI and human review for enforcement,” Pinterest told 404 Media. “We have an appeals process where a human reviews the content and reactivates it when we’ve made a mistake.” It also confirmed that the site uses both humans and automated systems for moderation. Oreglia shared some of the works Pinterest flagged including a photo of a muscular woman in a bikini holding knives, a painting of two clothed women in an intimate embrace, and a stock photo of a man holding a gun on a telephone that was flagged for “self-harm.” In most cases, Oreglia can appeal and get a decision reversed, but that eats up time. Time she could be spending making art. And those appeals aren’t always approved. “The worst case scenario for this stuff is that you get your account banned,” Oreglia said. r/Pinterest is awash in users complaining about AI-related issues on the site. “Pinterest keeps automatically adding the ‘AI modified’ tag to my Pins...every time I appeal, Pinterest reviews it and removes the AI label. But then… the same thing happens again on new Pins and new artwork. So I’m stuck in this endless loop of appealing → label removed → new Pin gets tagged again,” read a _post on r/Pinterest_. The redditor told 404 Media that this has happened three times so far and it takes between 24 to 48 hours to sort out. “I actively promote my work as _100% hand-drawn_ and ‘no AI,’” they said. “On Etsy, I clearly position my brand around original illustration. So when a Pinterest Pin is labeled ‘Hand Drawn’ but simultaneously marked as ‘AI modified,’ it creates confusion and undermines that positioning.” Artist Min Zakuga told 404 Media that they’ve seen a lot of their art on Pinterest get labeled as “AI modified” despite being older than image generation tech. “There is no way to take their auto-labeling off, other than going through a horribly long process where you have to prove it was not AI, which still may get rejected,” she said. “Even artwork from 10-13 years ago will still be labeled by Pinterest as AI, with them knowing full well something from 10 years ago could not possibly be AI.” Other users are tired of seeing a constant flood of AI-generated art in their feeds. “I can't even scroll through 100 pins without 95 out of them being some AI slop or theft, let alone very talented artists tend to be sucked down and are being unrecognized by the sheer amount of it,” said _another post_. “I don't want to triple check my sources every single time I look at a pin, but I refuse to use any of that soulless garbage. However, Pinterest has been infested. Made obsolete.” Artist Eva Toorenent told 404 Media that she’s been able to cull most of the AI-generated content from her board, but that it took a lot of time. Whenever she saw what she thought was an AI-generated image, she told Pinterest she didn’t want to see it and eventually the algorithm learned. But, like Oreglia fighting auto-moderation and Zakuga fighting to get the “AI modified” label taken off her work, training Pinterest’s algorithm to stop serving you AI-generated images eats up precious time. AI boosters often talk about how much time these systems will save everyone. They’re pitched as productivity boosters. Earlier this month, Pinterest laid off 15 percent of its work force as _part of a push_ to prioritize AI. In a _post on LinkedIn_, one of the former employees shared part of the email CEO Bill Ready sent out after the lay offs. “We’re doubling down on an AI-forward approach—prioritizing AI-focused roles, teams, and ways of working.” Toorenent removed all her own art from her Pinterest account after hearing the news that the site would use public pins to _train Pinterest Canvas,_ the company’s proprietary text-to-image AI. But she has no control over other users uploading her artwork. “I have already caught a few of my images still on Pinterest that I did not upload myself…that makes me incredibly mad,” she told 404 Media. “It used to be a great way to get your work seen among other people, but it’s being used to train their internal AI.” Oreglia told 404 Media that the flood of AI has changed her relationship to a site she once used to prize. “It's definitely affected how I search things and I'm always now very critical about where something came from... although I've always been overly pedantic about research,” she said. “It does make you do your due diligence but it sucks to constantly have to question and check if something is authentic or synthetic.” She’s thought about leaving the platform, but feels stuck. “I just want to be able to take all my references with me. I've been on the platform for about ten years and have very carefully curated it. It's really nice to be able to just go to my page and search for something I saved instead of having to save everything to folders although I also do that,” she said. “More and more I'm trying to curate and collect physical references too but some of that can take up space I don't have so it can be difficult. Having a physical reference library just seems more and more necessary these days…artists have to be adaptable to this kind of thing these days. It's annoying but not unmanageable.” Ready has been vocal and proud about the company’s commitment to forcing AI into every aspect of the user experience. “At Pinterest…we’re deploying AI to flip the script on social media, using it to more aggressively promote user well being rather than the alternative formula of triggering engagement by enragement,” Ready said in a _January column at _Fortune__. “Social media platforms like Pinterest live and die by users’ willingness to share creative and original ideas.”