2026-05-11 14:01:35
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.
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.)
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.
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.
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 Peace.
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 billion 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.
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 by 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
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.
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.
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.
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.
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.
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.
2026-04-30 16:08:34
In this era of powerful tools to find software bugs, we now see tools find a lot of problems at a high speed. This causes problems for developers, as dealing with the growing list of issues is hard. It may take a longer time to address the problems than to find them – not to mention to put them into releases and then it takes yet another extended time until users out in the wild actually get that updated version into their hands.
In order to find many bugs fast, they have to already exist in source code. These new tools don’t add or create the problems. They just find them, filter them out and bring them to the surface for exposure. A better filter in the pool filters out more rubbish.
The more bugs we fix, the fewer bugs remain in the code. Assuming the developers manage to fix problems at a decent enough pace.
For every bugfix we merge, there is a risk that the change itself introduces one more more new separate problems. We also tend to keep adding features and changing behavior as we want to improve our products, and when doing so we occasionally slip up and introduce new problems as well.
Source code analyzing tools is a concept as old as source code itself. There has always existed tools that have tried to identify coding mistakes. Now they just recently got better so they can find more mistakes.
These new tools, similar to the old ones, don’t find all the problems. Even these new modern tools sometimes suggest fixes to the problems they find that are incomplete and in fact sometimes downright buggy.
Undoubtedly code analyzer tooling will improve further. The tools of tomorrow will find even more bugs, some of them were not found when the current generation of tools scanned the code yesterday.
Of course, we now also introduce these tools in CI and general development pipelines, which should make us land better code with fewer mistakes going forward. Ideally.
If we assume that we fix bugs faster than we introduce new ones and we assume that the AI tools can improve further, the question is then more how much more they can improve and for how long that improvement can go on. Will the tools find 10% more bugs? 100%? 1000%? Is the tool improving going to gradually continue for the next two, ten or fifty years? Can they actually find all bugs?
Can we reach the utopia where we have no bugs left in a given software project and when we do merge a new one, it gets detected and fixed almost instantly?
If we assume that there is at least a theoretical chance to reach that point, how would we know when we reach it? Or even just if we are getting closer?
I propose that one way to measure if we are getting closer to zero bugs is to check the age of reported and fixed bugs. If the tools are this good, we should soon only be fixing bugs we introduced very recently.
In the curl project we don’t keep track of the age of regular bugs, but we do for vulnerabilities. The worst kind of bugs. If the tools can find almost all problems, they should soon only be finding very recently added vulnerabilities too. The age of new finds should plummet and go towards zero.
If the age of newly reported vulnerabilities are getting younger, it should make the average and median age of the total collection go down over time.
The average and median time vulnerabilities had existed in the curl source code by the time they were found and reported to the project.
When the tools have found most problems there should be less bugs left to fix. The bugfix rate should go down rapidly – independently of how you count them or how liberal we are in counting exactly what is a bugfix.
Given the data from the curl project, there does not seem to be fewer bugfixes done – yet. Maybe the bugfix speed goes up before it goes down?
Given the look of these graphs I don’t think we are close to zero bugs yet. These two curves do not seem to even start to fall yet.
Yes, these graphs are based on data from a single project, which makes it super weak to draw statistical conclusions from, but this is all I have to work with.
I think that’s mostly an indication of what you believe the tooling can do and how good they can eventually end up becoming.
I don’t know. I will keep fixing bugs.
2026-04-30 14:49:47
In appendix A of the book Root cause: Stories and lessons from two decades of Backend Engineering Bugs, author Hussein Nasser has these wonderful words to say about me:
Daniel Stenberg is a Swedish engineer and the creator of curl (cURL), one of the most widely used tools and libraries for fetching content over various protocols. I’ve always admired Daniel’s work, reading his blogs and watching his talks on YouTube. He is one of the engineers who inspired me to start my own YouTube channel and teach backend engineering.
It warms my heart to read this. Words like this give me energy and motivation. My work has meaning.
2026-04-29 14:27:01
You always find the new curl releases on the curl site!
the 274th release
8 changes
49 days (total: 10,761)
282 bugfixes (total: 13,922)
521 commits (total: 38,545)
0 new public libcurl function (total: 100)
0 new curl_easy_setopt() option (total: 308)
0 new curl command line option (total: 273)
73 contributors, 45 new (total: 3,664)
28 authors, 12 new (total: 1,463)
8 security fixes (total: 188)
As mentioned elsewhere, the security reporting volume has been intense lately. We publish eight new curl vulnerabilities this time.
The official count says over 260 bugfixes were merged in this 49 day cycle. See the changelog for all the details.
Planned upcoming removals include:
If you are concerned about any of these, speak up on the curl-library ASAP.
Unless we messed up this one and need to do a patch release, the pending next release is scheduled to happen on June 24.
2026-04-22 19:44:40
As I have been preparing slides for my coming talk at foss-north on April 28, 2026 I figured I could take the opportunity and share a glimpse of the current reality here on my blog. The high quality chaos era, as I call it.
I complained and I complained about the high frequency junk submissions to the curl bug-bounty that grew really intense during 2025 and early 2026. To the degree that we shut it down completely on February 1st this year. At the time we speculated if that would be sufficient or if the flood would go on.
Now we know.
In March 2026, the curl project went back to Hackerone again once we had figured out that GitHub was not good enough.
From that day, the nature of the security report submissions have changed.
The slop situation is not a problem anymore.
The report frequency is higher than ever. Recently it’s been about double the rate we had through 2025, which already was more than double from previous years.
The quality is higher. The rate of confirmed vulnerabilities is back to and even surpassing the 2024 pre-AI level, meaning somewhere in the 15-16% range.
In addition to that, the share of reports that identify a bug, meaning that they aren’t vulnerabilities but still some kind of problem, is significantly higher than before.
Almost every security report now uses AI to various degrees. You can tell by the way they are worded, how the report is phrased and also by the fact that they now easily get very detailed duplicates in ways that can’t be done had they been written by humans.
The difference now compared to before however, is that they are mostly very high quality.
The reporters rarely mention exactly which AI tool or model they used (and really, we don’t care), but the evidence is strong that they used such help.
I did a quick unscientific poll on Mastodon to see if other Open Source projects see the same trends and man, do they! Friends from the following projects confirmed that they too see this trend. Of course the exact numbers and volumes vary, but it shows its not unique to any specific project.
Apache httpd, BIND, curl, Django, Elasticsearch Python client, Firefox, git, glibc, GnuTLS, GStreamer, Haproxy, Immich, libssh, libtiff, Linux kernel, OpenLDAP, PowerDNS, python, Prometheus, Ruby, Sequoia PGP, strongSwan, Temporal, Unbound, urllib3, Vikunja, Wireshark, wolfSSL, …
I bet this list of projects is just a random selection that just happened to see my question. You will find many more experiencing and confirming this reality view.
When we ship curl 8.20.0 in the middle of next week – end of April 2026, we expect to announce at least six new vulnerabilities. Assuming that the trend keeps up for at least the rest of the year, and I think that is a fair assumption, we are looking at an estimated explosion and a record amount of CVEs to be published by the curl project this year.
We might publish closer to 50 curl vulnerabilities in 2026.
Given this universal trend, I cannot see how this pattern can not also be spotted and expected to happen in many other projects as well.
The tools are still improving. We keep adding flaws when we do bugfixes and add new features.
Someone has suggested it might work as with fuzzing, that we will see a plateau within a few years. I suppose we just have to see how it goes.
This avalanche is going to make maintainer overload even worse. Some projects will have a hard time to handle this kind of backlog expansion without any added maintainers to help.
It is probably a good time for the bad guys who can easily find this many problems themselves by just using the same tools, before all the projects get time, manpower and energy to fix them.
Then everyone needs to update to the newly released fixed versions of all packages, which we know is likely to take an even longer time.
We are up for a bumpy ride.
2026-03-26 18:09:07
Software and digital security should rely on verification, rather than trust. I want to strongly encourage more users and consumers of software to verify curl. And ideally require that you could do at least this level of verification of other software components in your dependency chains.
With every source code commit and every release of software, there are risks. Also entirely independent of those.
Some of the things a widely used project can become the victim of, include…
In the event any of these would happen, they could of course also happen in combinations and in a rapid sequence.
curl, mostly in the shape of libcurl, runs in tens of billions of devices. Clearly one of the most widely used software components in the world.
People ask me how I sleep at night given the vast amount of nasty things that could occur virtually at any point.
There is only one way to combat this kind of insomnia: do everything possible and do it openly and transparently. Make it a little better this week than it was last week. Do software engineering right. Provide means for everyone to verify what we do and what we ship. Iterate, iterate, iterate.
If even just a few users verify that they got a curl release signed by the curl release manager and they verify that the release contents is untainted and only contains bits that originate from the git repository, then we are in a pretty good state. We need enough independent outside users to do this, so that one of them can blow the whistle if anything at any point would look wrong.
I can’t tell you who these users are, or in fact if they actually exist, as they are and must be completely independent from me and from the curl project. We do however provide all the means and we make it easy for such users to do this verification.
The few outsiders who verify that nothing was tampered with in the releases can only validate that the releases are made from what exists in git. It is our own job to make sure that what exists in git is the real thing. The secure and safe curl.
We must do a lot to make sure that whatever we land in git is okay. Here’s a list of activities we do.
-Werror that converts warnings to errors and fail the builds.zizmor and other code analyzer tools on the CI job config scripts to reduce the risk of us running or using insecure CI jobs.All this done in the open with full transparency and full accountability. Anyone can follow along and verify that we follow this.
Require this for all your dependencies.
We plan for the event when someone actually wants and tries to hurt us and our users really bad. Or when that happens by mistake. A successful attack on curl can in theory reach widely.
This is not paranoia. This setup allows us to sleep well at night.
This is why users still rely on curl after thirty years in the making.
I recently added a verify page to the curl website explaining some of what I write about in this post.
