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vulnerability management

What’s in the SOSS? Podcast #64 – S3E16 The Heartbeat of the Kernel: Why Upstream is the Ultimate Security Strategy with Greg Kroah-Hartman

By Podcast

Summary

What does it feel like to wake up and realize your weekend passion project is now the critical infrastructure powering the planet? In this episode of What’s in the SOSS?, CRob sits down with Linux kernel maintainer and open source icon Greg Kroah-Hartman. Greg takes us on a journey from his early days writing firmware for printer and hospital ATMs to managing the relentless, everyday engineering task of maintaining the Linux kernel over decades. He breaks down the realities of modern kernel security, dismantles the myth that maintainers know every single vulnerability exploit , and details how looming global regulations like the EU’s Cyber Resilience Act (CRA) are shifting the compliance burden onto vendors. If your organization uses Linux, Greg has a simple, urgent message for you: stop fearing change, build your testing infrastructure, and update your systems.

Conversation Highlights

00:03 Welcome: Host CRob introduces Linux kernel legend Greg Kroah-Hartman.
01:04 From Printers to Richard Stallman: Greg shares his origin story in embedded engineering and his introduction to free software.
02:00 The Weekend Driver and the Dopamine Hit: How a quick weekend project turned Greg into a lifelong kernel contributor.
04:20 Realizing Linux is Critical Infrastructure: The moment the telcos and banks moved in, signaling that Linux was everywhere.
05:28 2005: The Year the Kernel Grew Up: Implementing stable releases, the security team, and the rule to never break user space.
07:05 What People Get Wrong About Kernel Security: Linus’s mantra that “a bug is a bug,” and the reality of handling 35 fixes a day.
09:32 The Historic Fear of Updating: Why lagging behind for “stability” is actually incurring massive risk.
12:17 Global Regulation and the Cyber Resilience Act (CRA): How upcoming laws are changing vendor responsibility and why the open source community is exempt.
13:51 How OpenSSF is Reducing the Burden: Applauding the cross-ecosystem collaboration that protected maintainers from onerous drafts.
17:05 Pay Your Employees to Contribute: Greg’s best piece of advice for downstream enterprises relying on open source.
18:39 Inside the Kernel Security Alias: How the ad-hoc security team handles triage and assigns CVEs.
21:11 The CVE Numbers Game: Why the kernel ranks #2 in CVE creation and the trouble with automated severity scores.
25:39 We are Already There: AI Slop and Static Analysis: Greg addresses the recent surge of AI-generated bug reports and patches.
31:20 Rapid Fire Round: Spicy food, coffee, Star Wars, and the ultimate call to action.

Transcript

CRob (00:23)
Welcome, welcome, welcome to What’s in the SOSS, the OpenSSF podcast where I talk to maintainers Contributors security researchers and just anyone in and around the amazing world of open source and open source software security Today we have an amazing treat One of my open source idols somebody that I get the opportunity to work with pretty frequently now I’m quite excited today. We have Greg Kroah-Hartman one of the kernel maintainers with us today. So Greg, welcome to the show.

Greg Kroah-Hartman (00:55)
Hey, thanks for having me.

CRob (00:58)
Yeah, it’s amazing that we get to interact with folks such as yourself. This is a real treat for us.

Greg Kroah-Hartman (01:04)
I’ve been interacting with you quite a bit over the years.

CRob (01:07)
I know. So thinking about it, how did you get into open source? Back in days of yore when you were, I’m assuming you started off as a software engineer of some type. How did you get into open source proper in the kernel?

Greg Kroah-Hartman (01:23)
Yeah, I was embedded. I did embedded work. So I wrote firmware for printers and barcode scanners and ATMs that dispense drugs in hospitals, fun things like that. And we had to use Linux for some of our devices, some of the companies I worked for. But even earlier than that, when I was in university, my girlfriend at the time, who’s now my wife, had this class that would have a visiting speaker every class.

Greg Kroah-Hartman (01:50)
And she was a political science major. was computer science. And she came back. was like, I heard this guy came and spoke about something called free software. His name was Richard Stallman. This was the early 90s. And I was like, oh, that sounds very strange. And then I ran into the GCC developers at a conference.

Greg Kroah-Hartman (02:10)
And GCC was putting out a compiler for embedded systems. That was free. And I was like, how in the world is this business model working? This is crazy. I told them that, and they’re like, well, we’re selling it pretty well. And then I actually used it on some of my products that I had worked on. And I’m like, this compiler is better than the stuff we pay a lot of money for. So free software turned out to be working pretty well. But then years forward, I worked on the USB standard. And I was writing firmware for USB devices.

I had a barcode scanner that showed up as a keyboard. And my Linux had a little bit of support for USB, not much. So started testing my firmware and all sorts of different operating systems to try and make sure it worked. Under Windows, it worked fine because it had USB support. Linux, it showed up as 102 key mouse. I was like, what? It turns out that was my fault in the firmware because the kernel was being very strict on what accepted Windows would be very loose on what they accepted. So I was like, I’ve fixed some bugs on the stuff and then it was fun. I got the driver book and played around with it some more and then played with it on the weekends. One day my wife was going away with my daughter at the time to visit some family and she said, write that driver for that device you’ve always wanted to do. I’m like, okay. So I spent the weekend, a driver, submitted it. And then I instantly got back responses like, this is wrong, this is wrong, this is wrong. And if you heard of this thing called multiprocessors, I was like, what?

Greg Kroah-Hartman (03:34)
What do mean locking? Two processors at once? This is crazy. But it was great. It was like a dopamine hit of like, these are really good engineers telling me what to do and how to make my code better. And I was hooked at that. And then over the, I started writing drivers and eventually realized that more people were using the stuff I gave away for free on the weekends than were getting, using the product of the companies I worked for. Not that they weren’t that successful, but they weren’t that good.

CRob (03:45)
Yeah. That’s awesome.

Greg Kroah-Hartman (04:00)
Soon then I got a job in the early 90s, or late 90s, doing Linux full time, working on the security model for Linux. Funnily enough, I was one of the architects of that. And I’ve been paid ever since for that.

CRob (04:14)
Great. So you’ve been working on the kernel for how many years now?

Greg Kroah-Hartman (04:21)
I think 25, 24, that’s long time. Yeah, long time.

CRob (04:22)
20, yeah. So kind of reflecting back over that long time within this ecosystem, was there a specific moment where you kind of realized that, holy crap, what I’m doing is critical infrastructure for the planet?

Greg Kroah-Hartman (04:40)
Way too early. In the late 90s, the telcos adopted Linux first. They’re seen as like the stodgy companies, but they were the leading edge and they adopted Linux and they had this big telco initiatives through what was called OSDL at the time. And we’re butting heads with the telco people and we’re like, what are you using Linux for? They’re like, oh, it’s running our digital infrastructure. We’re like, what? So they were one of the first users and really showed that it could really be used for backbones and networking and stuff like that. And I was like, oh, wow. And then you hear things like, banks for throwing out all their old sun systems and using it and then the stock exchange and then air traffic control but uniquely it showed up like in a programmable like thermostat in my mother’s house once and I was like this is weird so then I realized yeah okay we’re really everywhere this is gonna stick around for a

CRob (05:36)
Yeah.

CRob (05:41)
Did that change how you approach security decisions as you were doing your work?

Greg Kroah-Hartman (05:48)
Yes and no. mean, we took, it made us take things more seriously. So once the banks and other people got involved, we we started taking things a lot more seriously. Like 2005 is like the year we grew up. We got instituted a security team. We instituted the stable way we did releases. We instituted the rule, maybe the year before that, we will never break user space again. We instituted time-based releases before that so people could rely on us. So we kind of grew up between 2003 to 2005 in that way. there’s a big tipping point at that point in time that we realized we got to do this right. People are relying on us and let’s come up with an engineering process that people can rely on. And that’s why I like Linux, because it’s one thing to write an operating system once or software once. It’s another thing to maintain it over decades so that people can rely on it and it will survive and grow and evolve over time because the world evolves. And that’s a fun engineering project and a fun engineering task. And I don’t think people… give us enough credit for Linux being something like that. It’s pretty unique as far as software systems go.

CRob (06:52)
Yeah, I would agree. So thinking about this kind of sense of responsibility that so many derpy things like thermostats and whatnot depend on you, but so many critical things are dependent on the Linux and the kernel. And securing things at this scale is very different than just securing a web server or a point of sale system.

From your perspective, what do most people get wrong about kernel security?

Greg Kroah-Hartman (07:22)
Two things to get wrong. They got wrong. One that they think we know what all the security bugs are. It turns out a bug in our area is a security bug. So Linus’s old mantra of a bug is a bug is a bug, let’s fix it and move on is more true than ever. I don’t, so open source, you listen to a few different things.

Open source is unique in that we can’t dictate use, or used in satellites to cow milking machines, right? Some are more critical to others, but those cows would be pretty upset if you got that wrong in there too. bugs at our level should be fixed and pushed out to users and they should update. The trick is I don’t know if this bug is applicable to you or not.

Greg Kroah-Hartman (08:09)
And that’s the thing. You use this, I’m not forcing you to use Linux, so you should just take our bug fixes and move on. do that. I remember sitting in a meeting once with some big company, they’re like, insisting, well, you need to tell us everything. All the bugs that you fixed that are applicable to us. I’m like, I don’t know what you do. I know you must take them all. You must take them all, or you must tell us. I’m like, I’m giving them to you for free. Somebody else corrected, like, the community is giving you what? 30 bug fixes a day for free and you want them to do more work for you? It’s like, this is a very odd thing to be demanding. That’s the way it is. So take our bug fixes because we know they fix something. And if they don’t fix something or if they break something, because people are always worried about regressions, we’ll fix that. But it’s much better to take a fix for a known issue than worry about a potential for an unknown issue. That’s the thing that people need to get the whole bitch to take.

And also look and test. mean, we are 40 million lines of code, but you only run a very small portion of it.

Your laptop runs two to maybe three million. Your server runs two million, maybe one and a half servers are the simplest, dumbest things ever. Your phone runs five and a half to six million. They’re the most complex beasts out there. So phones have tons and tons of issues, or tons of crazy hardware and stuff like that. So just take all our fixes and move on. And the phone companies, Android’s proven that you can update to every single RC release for the past four or five years on Linus’s tree and have a stable phone.

CRob (09:23)
Eesh.

Greg Kroah-Hartman (09:24)
So you can update these things and you can keep them up to date and you have to rely on that and luckily people are finally realizing that they have to update their systems and take the bug fixes and move on.

CRob (09:51)
And where do you think some of the hesitation is from being closer to head that organizations have? it kind of historically there wasn’t a lot of cross testing or?

Greg Kroah-Hartman (10:01)
Well, the old historic model is change is bad, right? And if it works for me now, great, I’ll just stick with it. But the world changes, and the world changes their ideas of their threat models and how bugs are found and things like that. Once a bug is found and is public, could be trivial to reproduce. But the trick is if it’s not known, then that’s another issue.

So just because you aren’t changing doesn’t mean the world isn’t changing. So you have to update and go on. So it’s an old model of that way. And also people will work. So you should have a testing infrastructure in place to accept this.

Famously, everyone’s like, we’ll just take tiny fixes. Well, Dijkstra famously said a one bit change could radically alter a program. Software is unique in that. It is not proportional to the size of the change, it’s to the size of the result. So very tiny changes can have major, major impacts. Giant changes can have minor impacts. So it all depends on your use. So you better have a pipeline in order to this stuff. Also, people used to have huge kernel forks out of the tree and they weren’t working upstream. Embedded devices were famously like that. Some companies made that decision. It’s a business decision. It costs money to keep code out of the kernel, which is fine. They had money to waste. Now they’ve realized it and they’re working upstream. IBM and Intel publicly said it saves us time and money to work upstream. Why not do that? So it takes time for companies to move their code forward over time.

Pixel, that Pixel device I mentioned, it still has 300 out of tree drivers, which is crazy how many drivers the phone has. And it still was able to update to each one along the way. Engineers adapted the APIs and kept it up to date. It worked pretty well. So you can do things out of tree.

And you can work that way, but you just have to have the testing infrastructure in place. And I’ll point at Android. I mean, it’s a few billion, many billions of devices, but they have a testing infrastructure in place that they can update, take the latest releases, take the latest state releases, run it through their testing system. I got a report back pretty fast, whether everything’s good or bad, and they push out to devices over time. They stage them and roll them out and things like that. But you need that testing infrastructure in place. But you need that testing infrastructure in place no matter what. So create it. Build it for your systems and then you’re good. You’re good to go.

CRob (12:24)
Do you have any other advice for organizations that might be hesitant for staying current and, you know, afraid to update anything else beyond having kind of a very thorough testing plan and infrastructure?

Greg Kroah-Hartman (12:38)
If you’re afraid to update, then you better partition and firewall the heck out of those devices. I’m just by virtue of also it’s going to be against the law in many companies now not to update. So it’s nice that the governments are finally realizing the US government, EU government, Japan. It’s all a new initiative of China for the way they’re treating their software and automotive. This is actually really good. So they are going to require updates and that’s a good thing. And so you should be updating things over time.

CRob (13:05)
So basically lagging behind for stability is actually incurring more risk now, right?

Greg Kroah-Hartman (13:10)
it’s incurring more instability because you have known issues that your systems have. So that is a known problem and that means your system has a known vulnerability, right? That’s, mean, on your risk management scale, that’s very high, right? As far as I think the way risk management scales go. So be aware of that.

CRob (13:35)
So then you’ve touched on it. I won’t say the magic three letters, but we’ve seen a lot of increasing pressure globally on enterprises from regulators. So how do you see organizations like the OpenSSF or other open source foundations, how do you see them helping kind of reduce that compliance burden on the upstream maintainers?

Greg Kroah-Hartman (13:56)
I’ll say the magic word, three magic letters, CRA, Cyber Resilience Act from the EU, which I actually think is a really good thing. And I might be biased, I live here in Europe, I’m also on the expert committee to do this stuff.

Greg Kroah-Hartman (14:10)
It is mandating that vendors update their software and report vulnerabilities and report bugs upstream to open source developers and maintainers. And that’s a good thing overall. There’s also other things with SBOMs and all, whole life cycle and risk management, which manufacturers will have some additional work, but it’s good. It’s just a list of ingredients, right? Like you have to do for food or for electrical devices. This is nothing out of the ordinary that good software engineering firms shouldn’t be doing.

It’s going to be interesting to see. But the openness to self, will call out. You guys have done a very good job in handling the paperwork, a lot of documentation you have, like here’s a one-pager on what an open source steward means and how to handle it. There’s really just two things you got to do. It’s pretty simple. Who is an open source steward? There’s a flowchart. So you guys have documentation. I’ve been giving talks for the past year and a half about this stuff.

So there’s tons and tons of resources. I think you’re doing stuff for actually manufacturers too, aren’t you? Okay.

CRob (15:07)
Yep, the manufacturers of we’re in full swing there. So we should have some guidance out for those types of folks who are the consumers of a lot of the work of all of you upstream. So hopefully we can coach them to treat and interact with upstream maintainers in a much more professional manner.

Greg Kroah-Hartman (15:28)
Yeah, and I’ve worked with a lot of companies over the years. That’s a lot of work I do. And some companies are great at this, right? And some companies are not. So it’s a learning experience on their half. And I want to see that happen. These companies rely on open source and they rely on the fact that open source works. So why not become part of the community to ensure it continues to work for them? And that’s my biggest pitch to them is like, great, if you want to use Linux as is or other open source projects, just take, and that’s wonderful. But if you want to make sure that it will continue to work properly for you in the future, become part of the community and work with the guidance and work with the developers to ensure that your use case still is valid. Because all we are are tools for you to do your business, right? And so you have your value added, whatever you’re building or making. So work with us to make sure that still works properly. And open SSF again, yeah, you’re doing stuff for manufacturers. That’s good. Open source developer. Open source developers and CRA and laws do not apply to it all. So I was happy and happy. don’t have to worry about that.

But a lot of the work that OpenSSF did with the CRA was to ensure that that was going to be the case because the way the original laws were being drafted were very onerous when it came to actually developing it. And I will call out there’s a really a lot of good job work and jobs you guys have done for that to make sure it didn’t harm us.

CRob (16:42)
That was a really nice kind of cross ecosystem effort. I’m pretty proud of that, that a lot of the foundations and big projects came together to express the really negative consequences of the first draft of the law.

Greg Kroah-Hartman (16:53)
Yeah, yeah, and I’ll call out Apache has done an amazing job also with us continuing to do that. Eclipse has done some work as well. So we’re not alone there by any means, but all the open source stuff is.

CRob (17:07)
So from your perspective, kind of looking at all this looming regulation, what types of support would you try to suggest for downstream to support maintainers and projects in their communities?

Greg Kroah-Hartman (17:23)
Kind of support, well just send us patches that work. So, I mean, or just become developers, or just, or better yet, have your employees work on open source projects and company time. That’s the best thing to do.

CRob (17:28)
Patches welcome.

Greg Kroah-Hartman (17:37)
Maintainers need to be able to do their work of these open source projects on company time. The kernel has this document that shows how open source friendly is your company with different levels, like level zero is they’re not allowed to work on anything upstream to level five as they pay you to be a maintainer and work on whatever you want. There’s a little gradient type thing there. And that’s good. When I’ve worked at other companies, we had in our employee reviews of, we mandated that you had to work X percentage of time on upstream development. And that was good, and that was an instant payback. But if you’re not actually measuring that at the end of the year for their employee review, they’re not going to do that. that always comes, that’s the first thing to give.

Smart companies know that this is actually, it pays off in huge, huge benefits. I think there was actually a report by the LF recently about that. showed dollar amounts. Yeah, Frank did good job with that.

CRob (18:31)
Yep. Yeah, Frank Nagel did it. Yeah. I agree. So thinking about your day job is helping lead security for the Colonel. When there’s a vulnerability discovered from your perspective, what’s the hardest part of getting that addressed? The technical patch writing and testing or the coordination to getting that fixed once it’s ready available to the consumers?

Greg Kroah-Hartman (18:57)
So I’m one of the members of the security team. I won’t say we lead it, it’s an ad hoc group. But our kernel team is, it’s an alias. You send us a bug report. And if the people on that alias isn’t, we don’t know how to fix it, the kernel is huge. We will grab the right maintainer first thing and grab them in and join them to the emails for added.

CRob (19:20)
Mm-hmm.

Greg Kroah-Hartman (19:21)
Go from there. And ideally, the submitter has submitted a proposed patch. If they have a proposed patch, even if it’s wrong, don’t be afraid to send us something that you think might fix it. Because then that’s something you can work off of. I’d much rather have a broken patch, because it’s like, this is the area I think this might work with. Let’s go from there. If we drag you in enough times, the security team, we then gently ask you, do you wish to be part of this alias? So it’s not really a good thing to be part of this alias. So that kind of means your subsystem has been little bit problems over time. That being said, yeah, just work with that. So the majority of bugs we have are not a big deal. Like, oh, we expose some uninitialized stack memory here, or here’s a way to bypass random number or random address of stuff, which is, as a local user, that’s not a really security issue or et cetera. We have lots of minor issues reported all the time.

We work on the fix. We usually post the fix publicly and don’t declare that, this is a security fix, it’s just a bug fix, and away it goes. Because if you look on our mailing list, we’re fixing bugs that end up being vulnerability fixes because they can crash the machine. Every day we have 35 bug fixes a day we seem to average. That goes into the stable kernels, that goes into the lenin-systery first. And about one-third of those are deemed of CVE. So we average about 10 to 13 CVEs a day.

which seems like a high number. But so the small number that we get sent to the security list are not very much, not very big. Very rarely do we have anything that’s major. And even then we just work on fixing it and get pushups to the tree and go. And we don’t assign a CVE until things are in a stable kernel release. So we give people who follow the stable kernels a chance to be up to date and secure before things are publicized to the world, which is good.

And we don’t do any disclosure. It’s up to the submitter if they want to do some disclosure or they want to write a report or have some paper or whatever they do. We just fix bugs and move on. And that’s all we do.

CRob (21:29)
We had chatted earlier in an earlier call today. You folks lost a pretty impressive title that you had for several years. You want to talk about your rankings within the CNA ecosystem? I thought you were number one for a while. Okay, my mistake.

Greg Kroah-Hartman (21:40)
No, we’ve never been number one. We’ve always been number two. No, well, we’re the number two creators of CBEs. Number one is WordPress. But it’s WordPress and all the different plugins somehow. I don’t know how those people keep up to date with all that stuff.

And then if you look at GitHub, we alternate between us and GitHub, but GitHub is a conglomeration of all open source projects that don’t really have the CNA that’s assigned bugs to. So it’s from a single entity, we are kind of still the largest one. But we don’t declare severity to bugs for the most part. I’ll talk about that in a minute. So other companies, if you look at Microsoft and Apple, they only report severe vulnerabilities. They do not report low or minor or medium or high or things like

So if you look at just quantities, you can’t go off of quantities. And that’s an issue that a number of people have realized slowly over the years. it’d be nice if the CBE group would require all vulnerabilities to be released as a CBE.

CRob (22:42)
I would like that quite a lot.

Greg Kroah-Hartman (22:44)
Yeah, so the fact that we have a lot is unusual. But the scoring of CVEs is tough because I don’t know your use case, as I mentioned before. So I don’t know if this is an issue or not. And there’s a famous researcher that said, even if I know a vulnerability or a bug, determining if I can actually exploit that is almost impossible. just depends on your use case, depends on this, depends on how your system’s set up, depends on all these random different things which is both good and bad, right? It’s good in that these things are usually very hard to exploit, it’s bad in that we have no idea if they are exploitable or not. So you see some groups like NVD from NIST will go through and give a random number for a severity score and that random number for the next kernel was 5.5 for everything last year.

That number we was like, why did they pick that number? And that number is just below the threshold that triggers some automatic tools for which you must upgrade. This isn’t a good thing. We’ve asked NVD to stop this. We asked CISA to stop this. They said they would stop this, and they did that. And we are starting to roll out. We just actually scored some CVEs the other day and pushed out updates.

CRob (23:55)
yeah?

Greg Kroah-Hartman (23:58)
We picked some that were like, okay, if you do have this network type of connection, this is a bad bug. So we went in that, scored that. There’s a group of companies that got together, like, take, started looking at different use cases. So like the cloud server use case. And they were like, here’s some rules and let’s try and score them all this way. That kind of fell down and they kind of fell apart. And we’re trying to drag them back because it’s hard to add this metadata to a CVE of what is the use case and what is the score for that use case. CVE wants to have, what is it, supplier? They call it something. They want to add.

Something starts with an A. They want to add different people can do things to a record.

CRob (24:36)
The ADP, the authoritative data provider.

Greg Kroah-Hartman (24:39)
ADP. Yes. Right. They want to do that. But that’s great. But you have to be a CNA to do that. And that’s not going to really work well for larger projects. So we’re thinking with the kernel, we’ll take groups of people, like the cloud people, and have a way to add to our record that we publish. Because we publish everything in the Git repo that is an addition to the CVE record that information.

And then we might be able to push into something else like OSV or things like that where we can define the data format better. And then maybe get the embedded people to add their own record and we’ll just keep adding these different use cases as time goes by. And I think that’s the only really way forward we have to do that. It’s like, OK, for this use case, this category of people are claiming that it affects it this way. And sadly, LLMs can actually grade these things based on the use case.

It actually kind of works kind of well if you say, here’s my use case. Is this bug applicable to me or not if I run this code? And they come back with a pretty good score. And then there’s just a pattern matching. It’s a pattern matching of use case to code. And OK, yeah, here’s a problem. So people can some LLMs at it. And I think one reason the cloud group fell down is everybody wanted to use their own specific LLM model. They didn’t trust anybody else’s LLM model, which is.

We’ll just run it on my local machine and we’ll take this as one and go from there. Local models are actually working really well these days.

CRob (25:57)
Good to hear. Well, since you said the other magic word, thinking ahead, are we prepared for AI generated vulnerabilities at the scale of AI?

Greg Kroah-Hartman (26:11)
We are already there. So something happened about a month or two ago and we used to get AI slop and I’ve said this before and those are easy to notice. About two months ago, we started getting real reports that were written nicely, actually reported a bug and sometimes came with a patch. But it’s real. I don’t know what triggered it and I don’t know what seem to do things well and so something happened. So in talking to the other open source security people because we have some meetings at times and we see each other, we’ve all hit it. GitHub’s hit it for their number of things, Curl’s hit it. All the different open source projects have hit it, HAProxy, the kernel, everybody. Kernel, we can handle this because we’re a big group and we can partition our farm it out to different maintainers.

But other smaller projects, I am actually worried about it. But it’s kind of like when fuzzers hit us, right? Fuzzers hit us and fuzzers were like, okay, the world is on fire. Here’s a thousand bugs. We’re like, And then we just grind through them. And we also say, hey, fuzzing companies, once you made this tool, you provide the gun, once you provide the resources to hold the gun properly. And to be fair, Google did. Google provided the resources to triage the bugs and say, okay, there’s serious, and they still do this. They run the fuzzing tools and they find the severe bugs first.

They fix them, send out the fixes before they actually let the rest of the fuzzing tools, reports go public. And the other reporting tools from the public fuzzing results are we have what, 400 open bugs right now that interns are really good to work on because they’re tiny bugs.

Hard to hit, and so let’s let the interns learn things, and that’s a good learning community.

CRob (27:48)
Yeah, cut their teeth. Yeah.

Greg Kroah-Hartman (27:50)
Yeah, same thing with these AI steps. So the AI code reporting tools, I mean, I’ve run a bunch of these over the past couple of months, and some of them are good. They’re good static analysis, because it’s pattern matching, right? And that’s what we actually have been using in the kernel for over a decade. Famously, for the stable kernels, we knew that when we went to backport patches, developers would say, this is a bug fix, and we backported.

Well, Julia Lowell, a professor in Paris, said, hey, why don’t we compare all the patches that we humans say are good with all the patches that we don’t know if we’re good or not and see if they match? Because LLMs are just a fuzzy statistical matching model. And it turned out to work really well. And she’s written a bunch of papers. And her and Lowell or Sasha, has come up with some great tools. they backport stuff. And they do some wonderful things. And we’ve been using this over a decade.

So this pattern matching of is this a bug and is this not a bug is there and that’s what Anthropic published like not so long ago they said we found 500 bugs and there basically was like we looked at all these old CVEs and saw where they weren’t fixed in the current two bases – code bases.

And I’ve run those same types of scripts. And it’s like, hey, this actually matches pretty well. Like, OK, it catches some things. And it out we forgot to cut a bunch of things. I was like, I thought we had tests for that, and we forgot to run our tests. So even as kernel developers, our infrastructure isn’t always working properly. So we have to go back and turn on a few tests that we had stopped running. Which is good. It cut it. So it’s just going to be another layer. It’s another static code that will grind through and fix up all the bugs and keep on moving. But it’s going to affect a lot of people for a while because fuzzers were kind of hard to run and set up.

These AI tools are a little bit easier for other people to hit and run. But the fun thing is some of these AI tools can spit out patches. We’ve taken security bugs sent to us and I run it through a local model and here’s a dumb, stupid patch to fix this problem. And it kind of works about two thirds at a time. But it gives you that base to go off of. And then there’s something to go for there. A few of those patches have actually been merged. So because they actually do fix the bug, which is kind of sad and funny in the same way.

CRob (29:52)
Yeah, exactly.

Greg Kroah-Hartman (30:04)
Yeah, I mean the old adage of all input is evil. Sometimes we forget to check the input. these things actually catch that well.

CRob (30:08)
Well, and it’s interesting. I’m not worried about teams like you or Apache Kubernetes, the folks that have a large group of security experts. It’s the single maintainer projects that might not have the expertise or the time or the tools. That does worry me quite a lot.

Greg Kroah-Hartman (30:26)
Yeah, it does work for me too. And they could get overwhelmed. But ideally, can also, I tell these people, just push back and say, come with a patch. And these tools can make a patch. And it does work that. But a lot of those tools and infrastructure aren’t security like.

facing, right? So say my LSUSB, which is like, it scans the number, the USB devices and prints out a little graph of what devices you have. People will try to report security bugs in that before. I’m like, yeah, that’s wonderful. It’ll crash. But that’s not a problem. So you physically told it to run as a user. It’s going to crash. OK. And if you look at the way Rust does memory safety, if it encounters problems in its code, Rust, by default, will just crash because that’s safe. It’s causing a safe way. Now using Rust in the kernel, you don’t want the crash when you do other things in there. But memory safetiness and crashing is kind of a very common thing when security issues come up, you just want to panic and go away. And it’s very good for users to do that. Because normally somebody can notice it, kick the box or kick the node and away you go, you start it up again.

CRob (31:37)
Well, this has been, I think, very illuminating, but let’s move on to the rapid fire part of the conversation. I have a couple of crazy questions. Just give me the first thing that comes off the top of your head. Mild or spicy food?

Greg Kroah-Hartman (31:57)
Spicy.

CRob (32:02)
So what’s one security myth you’d like to eliminate?

Greg Kroah-Hartman (32:06)
One security myth that I know if a bug is a security issue for you or not.

CRob (31:)
There you go. Very nice. What’s one thing that every company using Linux should do tomorrow?

Greg Kroah-Hartman (32:02.376)
Update to the latest version.

CRob (32:21)
When you’re doing your patch releases, what’s your favorite beverage of choice?

Greg Kroah-Hartman (32:27)
Coffee

CRob (32:29)
Yummy! And finally and most importantly, Star Trek or Star Wars?

Greg Kroah-Hartman (32:38)
Star Wars, sorry.

CRob (32:40)
There are no wrong answers. But Star Wars is very excellent. I appreciate that. So Greg, as we wind down, do you have a particular call to action you want to share with our audience outside of the amazing little snippets of advice you’ve given us during our time here?

Greg Kroah-Hartman (32:54)
Report bugs and report bugs to maintainers and developers. If something isn’t working, tell us because otherwise we don’t know. Linux has been working for me for 20 years, but obviously it’s been working for everybody. tell me and do that.

But also on the flip side I only see bugs, which is funny. I’m amazed that this stuff actually works. So my daughter, who is now a doctor, she’s like, it’s just like a doctor. Doctors only see problems. How does the human body ever work? The majority of the time, the software does work, and it is all good.

But if you do have a problem, please let me know. I’m here to fix it and help us. We want to know how to fix it. And tell us how you’re using it. We’re kind of curious sometimes. I know, like the cow milking machine, satellites, the Mars Rover, fun things like that. It’s kind of interesting to see where our code is at. It’s fun.

CRob (33:46)
Excellent. So amazing advice, incredible storied career, sir. Thank you for your time. And with that, we’re going to call this a wrap. I want everyone to stay Stiber safe and sound and have a great day and happy open sourcing.

Hack to the Future: The Impact and Legacy of the DARPA AIxCC Challenge

By AI, Blog, Global Cyber Policy, Guest Blog

By Helen Woeste

AIxCC Competition Background & Results: 

In 2023, DARPA announced a two-year long competition called the Artificial Intelligence Cyber Challenge (AIxCC) with the goal to safeguard open source software used in critical infrastructure throughout America. The intent is to hasten the development of open source AI tooling that can assist developers with finding and fixing bugs in live software with minimal cost. Open source is a drastically underfunded and underresourced form of infrastructure. It therefore presents an exciting, practical target, and opportunity for the research and development of AI in cybersecurity. Additionally, open source’s publicly observable code is ideal for competition and collaboration. 

AIxCC was run in collaboration with ARPA-H and supported with contributions from Anthropic, Google, Microsoft, and OpenAI, with additional consulting around open source provided by the Linux Foundation and the Open Source Security Foundation (OpenSSF). This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The competition consisted of two rounds, the Semifinal Competition (ASC) and the Final Competition (AFC), where cash prizes from a pot of $30,500,000 were distributed. For the ASC, 42 team submissions were accepted across two tracks; the Open Track and the Small Business Track, which required an additional technical paper submission. The top seven teams moved forward to the AFC which was set up to mimic a real world CI/CD pipeline. The scoring algorithm was also designed to highlight behaviors that would make the competing systems more useful to developers. At the conclusion of AFC, the top three teams were Team Atlanta, Trail of Bits, and Theori. 

For the AIxCC competition, real open source projects were selected, and their code was forked and then modified to insert artificial bugs for the Cyber Reasoning Systems (CRS) to discover and fix. However, during the execution of the competition, the CRSs discovered several real potential bugs alongside the artificial ones. This introduced the issue of how to triage and manage resolution of fixes in the projects. OpenSSF engaged third party open source security organization Open Source Technology Improvement Fund (OSTIF) to get involved with the closing out of the bugs identified as a result of the AIxCC competition. 

OSTIF selected the team at Ada Logics for their extensive experience working with open source fuzzing, bug verification, and disclosure. With a list of potential bugs identified through the course of the competition, Ada Logics was tasked with securely submitting verified issues, ensuring that anything reported to open source project maintainers was a proven bug. The Ada Logics team was able to reproduce and confirm twenty-seven issues after multiple rounds of testing and continued coordination between AIxCC competitors, collaborators, and contributors. CRS teams, including Team Atlanta, Team Buttercup, Team FuzzingBrain, Team Shellphish, Team Theori, Team 42-b3yond-6ug, and Team Lacrosse, working together with Kudu Dynamics and the OpenSSF, continued to collaborate and meet with OSTIF around the disclosures to ensure total accuracy of the reported issue’s testing and resulting decision around disclosure. 

It was of utmost importance that any and all real bugs detected during the competition were verified before alerting the project maintainer to the issue. This is to differentiate how the competition reports issues to projects from the low-quality reports plaguing open source maintainers today. In several cases, CRS-generated patches were submitted alongside bugs, an offering to project maintainers looking to quickly resolve the finding. Additionally, feedback was sourced from the projects around their experience as a target in the competition as well as the disclosure procedure following. 

The Findings:

Teams discovered twenty-seven candidate real-world issues during the competition and OSTIF engineers were ultimately able to replicate all of the draft bugs. The affected projects were cURL, shadowsocks-libev, healthcare-data-harmonization, hertzbeat, little-cms, and mongoose. Once identified, the hard work began of fixing those bugs, implementing CRS tooling to perform the second half of its double duty to find and fix security issues. 

However, some of the findings did not meet a level of security concern for various reasons. Some issues were fixed by code changes in the projects during the time-period in between the competition and when engineers reproduced them. Others were outside of the threat model of the project and did not meet the criteria needed to incorporate into the project (for example, the Apache Poi project threat model states “Expect any type of Exception when processing documents,” making any exception-based findings non-issues). One issue had actually already been found by OSS-Fuzz, but the project hadn’t fixed it yet.

Ultimately, interesting findings were discovered and fixed by the Cyber Reasoning Systems in this competition, and the systems found a lot of valid issues. Further, some projects had introduced fixes before the bugs were reported. This is likely because the AIxCC teams submitted the fuzzing harnesses to the projects before triage had taken place, which re-discovered the same bugs before triage had completed. One significant lesson learned from this is that cyber reasoning systems may benefit from doing self-triage when discovering potential issues by checking against the project’s documentation and understanding the types of issues that the project accepts as security bugs that need to be addressed.

Conclusion & Looking Forward:

The AIxCC program was a massive undertaking by dozens of organizations, all working to contribute back to open source security in a meaningful way using novel AI tooling. The competition was mindfully designed and carried out, with attention given towards the open source projects and maintainers, the wide variety of competitors and interests, and the impact of the competition itself on the industry all the way down to the maintainers. 

OpenSSF is the home for extended collaboration on these new open source tools through its newly formed Cyber Reasoning Systems Special Interest Group. OSS-CRS and FuzzingBrain, two open source projects that emerged from the competition, are now hosted at OpenSSF in the Linux Foundation. A third tool applied and was accepted to the OpenSSF, and has a few remaining steps before the official transition. The group aims to foster their development and adoption, and to establish best practices that help projects use CRSs effectively and responsibly.

This work is already producing real results. For example, FuzzingBrain has since turned its AI-assisted fuzzing system on the broader open source ecosystem, discovering sixty-two vulnerabilities across twenty-six projects, from CUPS and Apache Avro to Ghidra and OpenLDAP, with forty-three confirmed by maintainers and thirty-six already patched upstream. 42-b3yond-6ug has expanded its CRS to uncover twelve kernel-related vulnerabilities in the Linux kernel and related components, plus ten zero-day vulnerabilities in userspace projects including Eclipse Mosquitto and OpenLDAP. The team is also developing a platform to support more efficient model training and evaluation of models and agents, with a release expected soon. Using OSS-CRS, Team Atlanta discovered twenty-five vulnerabilities across sixteen projects spanning a broad range of software including PHP, U-Boot, memcached, and Apache Ignite 3. Of those, nine have been fixed and eight more have been confirmed with fixes in progress.

The future of AI assisting maintainers in finding and fixing security vulnerabilities is bright. The challenges raised by the AIxCC competition already have solutions being developed in open source, such as LLM-based tools that build threat models by looking at the data-flow of projects, and AI agents that triage findings against threat models and documentation before reporting issues. As these tools all continue to develop, they will harmonize into reliable solutions that maintainers can use to elevate their security with far less effort than today.

Our gratitude to the folks at Ada Logics for triaging the potential bugs and working hard to reproduce the issues so maintainers didn’t have to, OpenSSF for trusting us to bring together all of the stakeholders to work on the issues together, DARPA and ARPA-H for holding the AIxCC competition and sponsoring this work, the teams that built the Cyber Reasoning Systems for the competition, Kudu Dynamics for their support in confirming the findings, and all of the maintainers that worked with us to resolve the issues.

OpenSSF and OSTIF will continue to support this kind of work by serving as human connectors between CRS tools and open source communities. The goal is to help triage and validate vulnerability reports and proposed patches before they reach maintainers, ensuring findings are accurate, actionable, and respectful of maintainers’ time.

Organizing a competition of this scale on behalf of open source maintainers and its end users takes both enormous collaboration and individual effort. Understanding the communities involved, and building lightweight programs that shield maintainers from headaches while strengthening security is the best possible outcome for the ecosystem. It took everyone coming together to make this happen, and ongoing efforts will bring low-cost and low-maintenance tools to everyone that are valuable and make us all safer. 

As AI moves forward at breakneck speed, innovative work like this highlights how you can move fast and build things together for a better tomorrow. 

Author Bio

Helen Woeste joined OSTIF in 2023, coming from a decade of work experience in the restaurant and hospitality industries. With a passion (and degree) for writing and governance structures, Woeste quickly transitioned into an operations and communications role in technology. 

 

The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

Distribution Statement “A” (Approved for Public Release, Distribution Unlimited)

Why Third-Party Notices Are Breaking at Scale: What the Ecosystem Needs Next

By Blog, Guest Blog

By Devashri Datta, Independent Researcher, Software Supply Chain Security

Third-party notices (TPNs) are documents distributed to users that list open source third-party software components included in the product and key licensing information. Every time you buy a TV or router, you’ve probably seen them. Yet TPNs were never designed for the complexity, scale, and velocity of today’s software ecosystem. TPNs are one of the most widely distributed and yet least understood artifacts in modern software supply chains.

Inside nearly every appliance, firmware image, SaaS platform, and enterprise distribution, the same pattern persists: a long, unstructured PDF is expected to represent the full scope of open source license compliance.

As software systems have scaled, TPNs have quietly become a critical but increasingly fragile pillar. They are now failing technically, operationally, and structurally under the demands of modern development and distribution.

This article examines why TPNs are breaking. It also outlines what the ecosystem must do next based on large-scale analysis of real-world TPN documents and the development of an automated framework for extracting information directly from them. While traditionally viewed as compliance artifacts, Third-Party Notices (TPNs) also represent an underutilized source of security-relevant intelligence. In many real-world scenarios where Software Bills of Materials (SBOMs) are incomplete, unavailable, or restricted, TPNs may provide the only observable evidence of component usage. This positions TPNs as a critical input to software supply chain security workflows, including vulnerability management, third-party risk assessment, and incident response.

The Hidden Reality: TPNs Are the Supply Chain’s Last Mile

Despite advances in Software Bill of Materials (SBOM) formats such as SPDX and CycloneDX, TPNs remain:

  • The only compliance artifact that many vendors publicly distribute
  • The only artifact available to customers or regulators for proprietary systems
  • The only verifiable attribution record when source code and SBOMs are inaccessible

SBOMs provide structured visibility into software components, but their completeness depends on the generation methods and the availability of build-time data. In practice, SBOMs may not consistently capture full transitive dependencies or runtime-resolved components. In some cases, additional components and licensing details may appear in downstream artifacts such as third-party notices (TPNs), though these are typically not integrated into SBOM analysis pipelines. SBOM availability also varies across organizations and products and may not always be accessible to end users or external stakeholders due to policy or regulatory interpretation. Regulatory frameworks such as the EU Cyber Resilience Act (CRA) are evolving, and expectations around SBOM scope and disclosure remain subject to interpretation. As a result, relying solely on SBOM data may not provide complete visibility into whether a product contains a specific vulnerable component, depending on SBOM completeness and related artifact availability.

In practice, TPNs often serve as the last mile of compliance visibility, bridging internal software composition and external disclosure.

However, TPNs were never designed to operate at the scale or complexity of today’s supply chains.

Security Blind Spot in Software Supply Chains

While SBOMs and software composition analysis (SCA) tools have improved visibility during development, they assume access to structured or source level data. In contrast, TPNs often represent the only externally available artifact in downstream consumption environments such as embedded systems, firmware, and proprietary SaaS distributions.

This creates a structural blind spot in software supply chain security: security teams are frequently forced to make risk decisions without machine readable component intelligence. As a result, vulnerability exposure, dependency risk, and third-party software usage often remain partially or completely unobservable at the point of consumption.

Why the TPN Ecosystem Is Breaking

PDFs Are an Anti-Pattern for Machine-Readable Compliance

Most TPNs are distributed as large, heterogeneous PDFs containing:

  • Multi-column layouts
  • OCR artifacts and noise
  • Inconsistent license formatting
  • Duplicated or truncated license text

TPNs often omit component identifiers and lack specific version numbers for components.

PDFs are optimized for display, not structured data. As a result, extracting meaningful compliance information programmatically is extremely difficult.

Existing Compliance Tools Don’t Address the Problem

Current tools such as FOSSology, ScanCode, and ORT are designed to analyze source code or binaries—not TPN documents. Yet in many real-world scenarios, especially audits or vendor reviews, TPNs are the only artifact available.

This creates a fundamental gap: The most widely distributed compliance artifact is the least analyzable.

Inconsistent Generation Pipelines Lead to Data Drift

TPNs are generated through highly variable processes:

  • Custom scripts
  • Proprietary internal tooling
  • Manual aggregation from legacy systems
  • Partial or outdated SBOM exports

As a result, even TPNs from the same organization can vary significantly across releases, introducing inconsistencies, omissions, and misalignment with actual dependencies.

Scale Has Outpaced Human Review

Modern TPNs often span hundreds of pages across multiple license families and components.

Manual review has become increasingly impractical due to:

  • Repetitive license text
  • Poorly structured component mappings
  • Lack of contextual metadata
  • Hidden obligations within large text blocks

Compliance teams are effectively being asked to analyze documents at a scale that exceeds human capability.

Proposed Contribution: TPN-to-Security Intelligence Framework

This work introduces a systematic framework for transforming Third-Party Notices (TPNs) from unstructured compliance artifacts into structured security intelligence inputs. The framework addresses a critical gap in software supply chain security: the absence of machine-readable component visibility in downstream and vendor-distributed environments.

Unlike traditional software composition analysis tools that rely on source code, build artifacts, or SBOMs, this approach operates on TPNs as a primary data source. It enables the extraction, classification, and interpretation of software components and license obligations from highly unstructured documents.

The key contribution of this work is the demonstration that TPNs can be operationalized into actionable security intelligence for:

  • Vulnerability exposure identification when SBOMs are unavailable
  • Third-party risk assessment using externally visible artifacts
  • Incident response prioritization based on inferred component usage
  • Governance and compliance enforcement through structured outputs

Breaking the Logjam: Toward Automated License Intelligence

To address this systemic gap, I developed an automated end-to-end framework that treats TPNs as primary compliance artifacts, rather than secondary documentation.

The approach enables structured extraction and interpretation of license intelligence directly from unstructured documents. While TPNs may lack some information, they still provide valuable signals. For example, even without version identifiers, knowing that a product includes a component can be very valuable (e.g., when asking “which products contain a version of log4j that might be vulnerable to this attack?”).

Structured Extraction from Unstructured PDFs

Using normalization, segmentation, and page-level reconstruction, the system identifies and extracts coherent license blocks even from highly inconsistent documents.

License Identification and Classification

A hybrid approach combining rule-based methods and fuzzy matching maps extracted text into meaningful license categories:

  • Permissive
  • Weak copyleft
  • Strong copyleft
  • Proprietary
  • Public domain
  • Content licenses
  • Unknown

This approach achieves, in my testing:

  • 92–96% accuracy for permissive licenses
  • 85–90% accuracy for copyleft detection

Risk Interpretation

Each component is evaluated for compliance risk based on obligations such as:

  • Attribution requirements
  • Redistribution conditions
  • Copyleft scope
  • Source disclosure obligations
  • Ambiguous or unidentified licenses

Visualization and Machine-Readable Outputs

The framework produces:

  • Interactive dashboards
  • Structured datasets
  • Outputs compatible with governance workflows and SBOM pipelines

This demonstrates that meaningful compliance intelligence can be derived even from the most constrained artifact available. This closes a long-standing visibility gap in the software supply chain.

Security Implications of TPN Breakdown

The failure of TPNs is not only a compliance problem—it has direct consequences for software supply chain security. When TPNs are inconsistent, unstructured, or incomplete, they reduce the ability of downstream stakeholders to:

  • Identify exposure to known vulnerable components
  • Trace dependency relationships in third-party software
  • Perform accurate third-party risk assessments
  • Respond quickly to emerging vulnerabilities in production systems

This makes TPN degradation a security visibility problem, not just a documentation inefficiency.

What the Ecosystem Needs Next

TPN failures are not isolated inefficiencies. They represent a structural weakness in how the global software supply chain communicates compliance.

Addressing this requires coordinated effort across standards, tooling, and ecosystem alignment.

Standardized, Machine-Readable TPN Formats

The ecosystem needs formats beyond PDFs, such as:

  • Creating a standard TPN-JSON format for use
  • SPDX-aligned TPN profiles

These would enable structured, interoperable compliance disclosures.

One possible longer-term solution is to embed machine-readable data (such as an SBOM in SPDX format or a TPN in JSON format) within the PDFs, creating a “hybrid PDF”. The PDF format already permits adding internal files (called “attached files”). LibreOffice already supports generating PDFs that embed the source document, allowing people to use their existing process for exchanging display PDF while also including machine-readable data. Tools that can quickly extract those embedded files and complain when they’re not present could speed their deployment. However, while this approach has promise, it doesn’t deal with the current documents, which do not embed this information.

Improved Support for Dependency Analysis

Unsurprisingly, many improvements for handling dependencies could help in processing TPNs, SBOMs, and many other related formats.

It would be better if there was shared reference corpora for license matching. That’s because accurate license detection requires:

  • Canonical license datasets
  • Variant and legacy license mappings
  • Community-maintained reference corpora

This would significantly improve consistency across tools and organizations.

In addition, there should be open APIs for information on licensing. Standard APIs should support:

  • License extraction
  • Component-to-license mapping
  • Obligation and risk interpretation

This would enable interoperability between vendors, auditors, and regulators.

Integration Between SBOM and TPN Pipelines

Today, SBOMs and TPNs exist in disconnected workflows. Yet in many cases, TPNs provide the only information available about product components.

A unified pipeline would:

  • Eliminate duplication
  • Reduce inconsistencies
  • Ensure alignment between internal and external disclosures

Related Work

Prior efforts across the software supply chain ecosystem have focused on improving license detection and SBOM generation during development and build phases. However, these approaches often assume access to source code or structured metadata, leaving a visibility gap when Third‑Party Notices (TPNs) are the only available compliance artifact.

Related work on automating TPN analysis demonstrates how unstructured compliance documents can be transformed into machine‑readable license intelligence suitable for governance and audit workflows. Supporting datasets for compliance governance and SBOM alignment are described in:

Datta, D., **TPN Compliance Dataset for Software Supply Chain Governance**, Zenodo, 2025. 

https://doi.org/10.5281/zenodo.19152619

Framework:

https://doi.org/10.5281/zenodo.19099831

Security Workflow Integration Model

The proposed framework reframes TPNs as an input layer in modern software supply chain security workflows. Rather than treating TPNs as static compliance documentation, they can be operationalized into structured security intelligence pipelines.

The extracted data can be integrated into:

  • Vulnerability management systems (to identify exposed components when SBOMs are missing)
  • Third-party risk management (TPRM) platforms (to assess supplier software risk)
  • Incident response workflows (to rapidly evaluate exposure after CVE disclosures)
  • DevSecOps pipelines (to enforce policy-based controls on software composition)

This positions TPN analysis as a bridge between compliance documentation and operational security decision-making.

Conclusion: The Future Requires Fixing TPNs

Third-party notices (TPNs) were originally designed as simple attribution mechanisms and ways to declare licenses to recipients (as required by many licenses). Today, they are expected to support audits, transparency, regulatory compliance, and supply chain security.

But they are still delivered as static documents that do not scale.

TPNs are not failing because organizations lack intent; they are failing because the ecosystem has outgrown the tools and formats upon which it relies.

If we want a more transparent, auditable, and trustworthy software supply chain, TPNs must evolve into structured, machine-readable, and interoperable artifacts.

The next phase of open source security will not be defined solely by SBOMs or scanning tools, but by how effectively we solve the last mile of compliance visibility.

Fixing TPNs is an important step toward a more reliable and verifiable software ecosystem.

 

Acknowledgments

The author acknowledges David A. Wheeler and Sally Cooper for their insightful feedback and helpful discussions during the development of this work.

Resources

The open source implementation of the prototype described in this post, including parsing logic, license-classification rules, and the interactive dashboard, is available on GitHub for anyone interested in exploring or extending the approach:

https://github.com/devashridatta-dotcom/tpn-automation

Community feedback and contributions are welcome.

Author Bio

Devashri Datta is an AI & Software Supply Chain Security Researcher. Security researcher and enterprise security architect focused on software supply chain security, DevSecOps automation, and security governance at scale. Research areas include SBOM governance, vulnerability intelligence (VEX), Third-Party Notice (TPN) analysis, AI-assisted risk modeling, and security exception management in cloud-native environments under compliance frameworks such as SOC 2, ISO 27001, and FedRAMP.

From Noise to Signal: Using Runtime Context to Win the Vulnerability Management Battle

By Blog, Guest Blog

By Jonas Rosland

Security teams in 2026 have no shortage of data, alerts, or findings. In 2025 alone, 48,185 Common Vulnerabilities and Exposures (CVEs) were published, a 20.6% increase over 2024’s already record-breaking total of 39,962. That works out to roughly 130 new vulnerabilities disclosed every single day, and for seven consecutive years, the annual count has hit a new record high.

The drivers are structural: the explosive growth of open source software, the complexity of transitive dependencies hidden deep in software supply chains, and an expanding CVE ecosystem that now encompasses nearly twice as many reporting organizations as it did five years ago. With 97% of commercial applications containing open source components, inherited risk has become a routine part of working with modern software.

While only 2% of all discovered vulnerabilities are ever exploited in the wild, of that small fraction, nearly 29% were exploited on or before the day their CVE was published. Attackers are selective, but once they identify a target, the window for defenders is very narrow. The window between vulnerability disclosure and confirmed exploitation is also shrinking. Whereas that timeline was over a year in 2020, it’s now shrunk to just hours.

The old model of scanning everything, triaging by Common Vulnerability Scoring System (CVSS) score, and working through a queue simply cannot keep pace with this reality. Something has to change.

Most vulnerabilities will never be exploited

The vast majority of what your vulnerability scanner finds will never actually be used against you. That means the core challenge facing security teams isn’t patching speed, but knowing where to focus. When a scanner returns thousands of findings ranked only by CVSS score, what looks like a workload problem is really a prioritization problem. Critical vulnerabilities in libraries that aren’t loaded at runtime, or in containers that haven’t run in months, crowd out the findings that genuinely matter, such as exploitable vulnerabilities in running, exposed workloads. The result is alert fatigue, missed priorities, and growing friction between security and development teams.

The OpenSSF Best Practices criteria reflect this directly. At the “Passing” level, projects must not contain unpatched vulnerabilities of medium or higher severity that have been known publicly for more than 60 days, and critical vulnerabilities should be fixed rapidly after they are reported. The emphasis here isn’t on the volume of findings processed, but on the speed and accuracy with which the most dangerous vulnerabilities are addressed, a distinction that gets lost when teams are buried in undifferentiated backlogs.

Why static analysis alone isn’t enough

Static analysis is non-negotiable. The OpenSSF Best Practices criteria require it at the “Passing” level, and at “Silver,” projects must use tools that look for real vulnerabilities in code, not just style issues. Integrated into CI/CD pipelines, static analysis catches bugs early when they are cheapest to fix, and it remains a solid foundation of any security program. However, alone, it’s not enough.

The limitation is that static analysis sees everything, regardless of whether it matters in practice. It cannot tell you whether a vulnerable library is actually loaded in a running container, or whether that container ever receives external traffic. A CVSS 9.8 score looks identical whether the package is called thousands of times a day in a critical service or has never once been invoked in production. Runtime security fills that gap by observing what is actually executing in production. By tracking which processes are running, which packages are loaded, and which connections are being made, security teams gain much more precise intelligence about where risk actually lives.

Only 15% of critical and high-severity vulnerabilities with an available fix are in packages actually loaded at runtime. By isolating that subset, teams can reduce the scope of what needs immediate attention to a small fraction of their total backlog, in some cases by over 95%. That’s the practical difference between a list that overwhelms a development team and one they can actually act on. Static analysis provides breadth by catching everything possible during development, while runtime intelligence adds depth by showing what genuinely matters in production. Together, they give teams the context to make better decisions.

Helping security and development teams speak the same language

Runtime data also changes how security teams and developers talk to each other. Telling a developer “this CVE is rated 8.1” lands very differently than “this vulnerability is in a package actively loaded in your production authentication service.” The second statement connects a finding to a tangible business risk, and that context helps developers understand urgency in a way that a severity score on its own rarely does.

When security teams can bring developers a short, contextualized list of what needs attention and why, the conversation tends to shift from friction to collaboration. The OpenSSF Best Practices framework supports this kind of working relationship structurally, requiring documented vulnerability response processes, response times under 14 days, and release notes that explicitly identify runtime vulnerabilities fixed in each release. These aren’t bureaucratic requirements, but the scaffolding for the kind of consistent, trust-based communication that makes vulnerability management work in practice.

Neither team can do this work alone. Security engineers don’t always know which code paths are business-critical, and developers don’t always have visibility into what their software looks like from an attacker’s perspective. Runtime data helps bridge that gap by giving both sides a shared, evidence-based view of where the real risk lives.

Shrinking the problem over time

Prioritization manages the vulnerability problem today, but reducing the attack surface is how you make the problem smaller tomorrow. Runtime intelligence supports two practical strategies that static scanning alone cannot.

  1. Build leaner, more deliberate images. Runtime analysis identifies unused packages, old utilities, and bundled libraries that never get called in production, giving teams a clear basis for stripping images down. Building from scratch or distroless base images takes this further by removing shells, package managers, and other components that have no place in a production workload, and combining that with rootless containers limits the damage an attacker can do if they do gain access. Runtime data can also flag containers running on stale base images that are actively receiving traffic, making the case for a refresh concrete rather than a task that keeps getting deprioritized.
  2. Detect and respond to unexpected behavior in production. Even with good prioritization, not every risk can be patched immediately. This is where a runtime threat detection tool like Falco becomes valuable. By defining what normal behavior looks like for a given workload, Falco can flag unexpected activity in real time, such as a process spawning a shell, a container writing to a sensitive path, or an unusual outbound connection appearing. This doesn’t replace patching, but it provides a meaningful layer of protection while remediation work is underway, and it gives teams better visibility into whether a vulnerability is being actively probed or exploited.

The OpenSSF Best Practices criteria encourage minimizing the attack surface throughout, and the logic applies equally in production environments. The best vulnerability is the one that doesn’t exist because the vulnerable component was never there, and the next best outcome is knowing quickly when something unexpected is happening around the ones that remain.

Where to go from here

The 2025 numbers make one thing very clear: the volume of vulnerabilities isn’t going down, and teams that try to treat every finding with equal urgency will continue to struggle. The more practical path is to use static analysis and runtime intelligence together, letting each do what it does best, and to use that shared context to build better working relationships between security and development teams. Finding the right vulnerabilities to fix, explaining why they matter, and making it straightforward for developers to act on them is where the real progress happens.

About the Author

Jonas Rosland is Director of Open Source at Sysdig, where he works on cloud-native security and open source strategy. Sysdig supports open source security projects, including Falco, a CNCF graduated project for runtime threat detection.

Rethinking Post-Deployment Vulnerability Detection

By Blog, Guest Blog

By Tracy Ragan

Over the past decade, the IT community has made significant progress in improving pre-deployment vulnerability detection. Static analysis, Software Composition Analysis (SCA), container scanning, and dependency analysis are now standard components of modern CI/CD pipelines. These tools help developers identify vulnerable libraries and insecure code before software is released.

However, security does not end at build time.

Every successful software attack ultimately exploits a vulnerability that exists in a running system. Attackers can and do target code repositories, CI pipelines, and developer environments; these supply chain attacks are serious threats. But vulnerabilities running in live production systems are among the most dangerous because, once exploited, they can directly lead to persistent backdoors, system compromise, lateral movement, and data breaches.

This reality exposes an important gap in how organizations manage vulnerabilities today. While significant attention is placed on detecting vulnerabilities before deployment, far fewer organizations have effective mechanisms for identifying newly disclosed CVEs that affect software already running in production.

Across the industry, most development teams today run some form of pre-deployment vulnerability scanning, yet relatively few maintain continuous visibility into vulnerabilities impacting deployed software after release. This imbalance creates a dangerous blind spot: the systems organizations rely on every day may become vulnerable long after the code has passed through security checks.

As the volume of vulnerability disclosures continues to increase, the industry must rethink how post-deployment vulnerabilities are detected and remediated.

The Growing Post-Deployment Vulnerability Problem

Modern software systems depend heavily on open source components. A typical application may include hundreds, or even thousands, of transitive dependencies. While security scanning tools help identify vulnerabilities during development, they cannot predict vulnerabilities that have not yet been disclosed.

New CVEs are published daily across open source ecosystems. When a vulnerability is disclosed affecting a widely used package, thousands of deployed applications may suddenly become vulnerable, even if those applications passed every security check during their build process.

This creates a persistent challenge: software that was secure at release can become vulnerable later without any code changes.

In many organizations, the detection of these vulnerabilities relies on periodic rescanning of artifacts or manual monitoring of vulnerability feeds. These approaches introduce delays between vulnerability disclosure and detection, extending the window of exposure for deployed systems.

Because attackers actively monitor vulnerability disclosures and quickly develop exploits, this detection gap creates significant operational risk.

Current Approaches to Detecting Post-Deployment CVEs

Organizations today use several methods to identify vulnerabilities affecting deployed software. While each approach has value, they are often costly and introduce operational complexity.

One common strategy involves rescanning previously built artifacts or container images stored in registries. Security teams periodically run vulnerability scanners against these artifacts to identify newly disclosed CVEs. Although this approach can detect vulnerabilities that were unknown at build time, the process cannot identify where the containers are running across system assets. 

Another approach relies on host-based security agents or runtime inspection tools deployed on production infrastructure. These tools identify vulnerable libraries by inspecting installed packages or monitoring application behavior. In practice, these solutions are most commonly implemented in large enterprise environments where dedicated operations and security teams can manage the operational complexity. They often require significant infrastructure integration, deployment planning, and ongoing maintenance.

Agent-based approaches also struggle to support edge environments, embedded systems, air-gapped deployments, satellites, or high-performance computing clusters, where installing additional runtime software may not be feasible or permitted. Even in traditional cloud environments, deploying and maintaining agents across thousands of systems can be a substantial operational lift.

This complexity stands in sharp contrast to pre-deployment scanning tools, which can often be installed in CI/CD pipelines in just minutes. Integrating a software composition analysis scanner into a build pipeline typically requires only a small configuration change or plugin installation. Because these tools are easy to adopt and operate earlier in the development lifecycle, they have seen widespread adoption across organizations of all sizes.

Post-deployment solutions, by comparison, often require significantly more effort to deploy and maintain. As a result, far fewer organizations implement comprehensive post-deployment vulnerability monitoring. While most development teams today run some form of pre-deployment vulnerability scanning, relatively few maintain continuous visibility into vulnerabilities impacting software already running in production. This leaves a critical visibility gap in the environments where vulnerabilities are ultimately exploited: live operational systems.

SBOMs Are an Underutilized Security Asset

A more efficient model for detecting post-deployment vulnerabilities already exists but is often underutilized.

Software Bill of Materials (SBOMs) provide a detailed inventory of the components included in a software release. When generated during the build process using standardized formats such as SPDX or CycloneDX, SBOMs capture critical metadata, including component names, versions, dependency relationships, and identifiers such as Package URLs.

SBOM adoption has accelerated in recent years due in part to initiatives such as Executive Order 14028 and ongoing work across the open source ecosystem. Organizations increasingly generate SBOMs as part of their software supply chain transparency efforts.

Yet in many environments, SBOMs are treated primarily as compliance documentation rather than operational security tools. Instead of being archived after release, SBOMs can serve as persistent inventories of the components running in deployed software systems.

Detecting Vulnerabilities Without Rescanning

When SBOMs are available and associated with deployed releases, detecting newly disclosed vulnerabilities becomes significantly simpler.

Vulnerability intelligence feeds, such as the OSV.dev database, the National Vulnerability Database (NVD), and other vendor advisories, identify the packages and versions affected by each CVE. By correlating this vulnerability information with stored SBOMs and release metadata, organizations can quickly determine whether a deployed asset includes an affected component.

Because the SBOM already describes the complete dependency graph, there is no need to reanalyze artifacts or rescan source code. Detection becomes a metadata correlation problem rather than a compute-intensive scanning process.

This model enables organizations to continuously monitor deployed software environments and identify newly disclosed vulnerabilities almost immediately after they are published.

Digital Twins and Continuous Vulnerability Synchronization

To operationalize this approach at scale, organizations need systems capable of continuously tracking the relationship between software releases, deployed environments, and their associated SBOMs. One emerging concept is the creation of a software digital twin, a continuously updated model that represents the software components running across operational systems.

A digital twin maintains the relationship between deployed endpoints and the SBOMs that describe the software they run. By synchronizing these SBOM inventories with vulnerability intelligence sources such as OSV.dev or the NVD at regular intervals, organizations can automatically detect when newly disclosed CVEs impact running systems.

Rather than waiting for scheduled scans or relying on agents installed on production infrastructure, this model enables continuous vulnerability awareness through metadata synchronization.

Once an affected component is identified, remediation workflows can also be automated. Modern development platforms already rely on dependency manifests such as pom.xml, package.json, requirements.txt, or container Dockerfiles. By automatically updating these dependency files and generating pull requests with patched versions, organizations can rapidly move fixes back through their CI/CD pipelines.

This type of automation has the potential to reduce vulnerability remediation times from months to days, dramatically shrinking the window of exposure. And, it is easy to scale, giving developers more control and visibility into the production threat landscape. 

Aligning with OpenSSF Security Initiatives

Efforts across the Open Source Security Foundation (OpenSSF) ecosystem have helped establish the foundational infrastructure needed for this approach.

The OSV.dev vulnerability database provides high-quality vulnerability data tailored to open source ecosystems. Standards such as SPDX and CycloneDX enable consistent representation of SBOM data across tools and platforms. Projects like OpenVEX provide mechanisms for communicating vulnerability exploitability context, helping organizations determine which vulnerabilities require immediate attention.

Together, these initiatives create the building blocks for a more efficient and scalable vulnerability management model, one that relies on accurate software inventories and continuous vulnerability intelligence rather than repeated artifact scanning.

The Future of Vulnerability Management

Pre-deployment security scanning will continue to play an important role in software development. Identifying vulnerabilities early in the development lifecycle reduces risk and improves software quality.

But the security landscape is evolving. As software ecosystems grow more complex and vulnerability disclosures increase, organizations must also strengthen their ability to detect vulnerabilities that appear after software has already been deployed.

Rethinking post-deployment vulnerability detection means shifting away from repeated artifact scanning and toward continuous monitoring of software composition.

SBOMs provide the foundation for this shift. When combined with digital twin models that track deployed software, continuous synchronization with vulnerability databases, and automated dependency remediation, organizations can dramatically improve their ability to defend operational systems.

One thing is certain: attackers ultimately focus on exploiting vulnerabilities running in live systems. Gaining clear visibility into the attack surface, understanding exactly what OSS packages are deployed, where they are running, and how quickly they can be remediated, is essential to securing live systems from cloud-native to the edge. 

Author 

Tracy Ragan is the Founder and Chief Executive Officer of DeployHub and a recognized authority in secure software delivery and software supply chain defense. She has served on the Governing Boards of the Open Source Security Foundation (OpenSSF) and currently serves as a strategic advisor to the Continuous Delivery Foundation (CDF) Governing Board. She also sits on both the CDF and OpenSSF Technology Advisory Committees. In these roles, she helps shape industry standards and pragmatic guidance for securing the software supply chain and advancing DevOps pipelines to enable safer, more effective use of open-source ecosystems at scale.

With more than 25 years of experience across software engineering, DevOps, and secure delivery pipelines, Tracy has built a career at the intersection of automation, security, and operational reality. Her work is focused on closing one of the industry’s most critical gaps: detecting and remediating high-risk vulnerabilities running in live, deployed systems, across cloud-native, edge, embedded, and HPC environments.

Tracy’s expertise is grounded in decades of hands-on leadership. She is the Co-Founder and former COO of OpenMake Software, where she pioneered agile build automation and led the development of OpenMake Meister, a build orchestration platform adopted by hundreds of enterprise teams and generating over $60M in partner revenue. That experience directly informs her current mission: eliminating security blind spots that persist long after software is released.

What’s in the SOSS? Podcast #57 – S3E9 From Noise to Signal: Security Expertise and Kusari Inspector with Mike Lieberman

By Podcast

Summary

In this episode, CRob talks with Mike Lieberman from Kusari about the current state of open source security. They discuss the growing burden on maintainers from the “deluge” of noisy, low-quality vulnerability reports, often generated by AI tools, and the vital role of “a human in the loop.” Mike introduces Kusari’s tool, Inspector, explaining how it uses codified security expertise to process data from tools like OpenSSF Scorecard and SLSA, effectively filtering out false positives and giving maintainers only high-quality, actionable reports. They also dive into the design philosophy of “don’t piss off the engineers” and share a vision for the future of security tooling that focuses on dramatically better user experience and building security primitives that are “secure by design”.

Conversation Highlights

00:06 Introduction: The Biggest Challenge in Security Tooling
01:12 Overwhelmed Maintainers: The Deluge of Low-Quality AI Reports
04:00 Introducing Kusari’s Inspector: How it Filters False Positives
08:40 The Secret Sauce: Security Expertise and the Need for Reproducible Tests
12:03 Meeting Engineers Where They Are: Design Choices to Reduce Maintainer Burden
18:16 The Future of Open Source Security Tooling: Focusing on Better UX
22:19 Call to Action: The Responsibility of Large Organizations

Transcript

(0:00) Intro Music

Mike Lieberman (00:06)
I think the biggest thing in security tooling is better user experience. I think that to me is one of the biggest challenges.

CRob (00:25)
Welcome, welcome, welcome to What’s in the SOSS?, the OpenSSF’s podcast where I talk to developers, maintainers, security experts, and people in and around this amazing open source ecosystem. Today, again, we have a real treat. Friend of the show, Mike Lieberman from Kusari is joining us again after – I don’t know if your podcast was toppled from its place of the most listened to before, but we’re gonna see if we can make another hit for us. But we’re here today to talk about some interesting developments that you and your crew are involved in and just things going on in open source security. So how have you been, sir?

Mike Lieberman (01:07)
Well, thank you for having me back and yeah, things are going pretty well.

CRob (01:12)
Well, let’s dive right into it. Recently, and this is a topic that I’m actually dealing with this very moment while we’re recording this podcast, that open source maintainers are just currently overwhelmed by just this deluge of noisy, low quality reports, a lot of them generated by AI tools. So kind of thinking about it with your, many hats you wear, as you know, business owner, a community member, and a long time developer, a security expert. From your perspectives, what is actually creating the most burden today? And think about it through the lens of this project you’re going to share with us in a moment.

Mike Lieberman (01:57)
Yeah, sure. So I think to kind of start, the problem has been the same problem since, know, throughout human history, it is a combination of either bad actors or just lazy people that are I would say the biggest issue here. Right. We have a lot of things like AI reports generating awful sort of vulnerability, know, fake vulnerabilities or whatnot. But if we kind of look at it through the lens of like history through tech, we saw the same thing with any sort of automation, right? When, yeah, exactly. When people could kind of create scripts, hey, let me go in spam this one project with my script. Let me spam a whole bunch of projects with my sort of automation or whatever. And, you know, the same thing sort of happened when people, when we started moving away from mailing lists to sort of GitHub and those sorts of things as well. So I think it’s really to kind of take a step back. It’s kind of how people are using the tools more so than the tools themselves. But I do think when it comes to a lot of the security reports, yeah, it is folks who are just kind of asking an LLM.

Hey, find me find me some zero day. And of course, that’s never going to work because the LLMs don’t have that information. And it’s just it kind of comes back to you need people who understand what they’re doing, using the tools in the right way in order to kind of figure out some of this stuff.

CRob (03:42)
Yeah, human in the loop. Our dear friend, Dr. David Wheeler has a saying, he says, a fool with a tool is still a fool. So again, having those experts in there, helping out is critical. So let’s…

Mike Lieberman (03:43)
Yeah.

CRob (04:00)
You’ve been in this space for a long time, focusing in on supply chain security, and you’ve written or contributed to a ton of tools. And most recently, you all helped create over at Kusari a tool called Inspector. So from just a high level TLDR, how do you see things like Inspector kind of changing this dynamic of getting more people involved or getting more expert knowledge in?

Mike Lieberman (04:26)
Sure. I think like the things. So actually to take a step back, right? There’s a lot of great tools that are being built. The challenge with those tools is, and the way I kind of think about it is like, you know, home security, right? It’s, hey, there’s a ton of tools out there that are helping out with open source security the same way that there’s a ton of tools out there for, you know, a smarter lock. A better security system.

CRob (04:59)
A doorbell that can find your dog.

Mike Lieberman (05:02)
There’s privacy concerns on that one. think, you know, we can all agree on that. But I think to that extent, when it comes to sort of these tools, it’s in how they’re used. And also, the expertise that’s required in how to use them. And also, when building the tools, what sort of expertise went into building the tools? And I think that to me is where the big gap is with just sort of some of the AI related things is you have folks using a very generic system like an LLM. And just saying, hey, LLM become a security expert and do this stuff. And of course the LLM makes a lot of mistakes and whatever. But if you were to kind of say through things like MCP and LLM skills and all these other things, if you have a way of codifying, run open SSF scorecard, run, you know, SLSA and run all of these various things and put all of this together and generate me an SBOM using these tools and whatnot. And then you can take all that, then hand the output to the LLM and say, hey, here’s everything I discovered. Here’s also the code. Help me make sense of it. And I think that to me is kind of where a lot of the benefit is. And again, what I just described is essentially inspector, right?

We’re running all of these various tools, again, that we understand because we’ve contributed to those tools, we’ve helped maintain some of those tools, we have been users of these tools for years. So we understand how they’re supposed to be used. We understand how a human who is, before the age of AI would be using these tools. And we recognize the burden of that expertise. And we’ve sort of encoded it. Had the LLM kind of come in at the last mile and then take all that information, and say, hey, if there is a finding, a vulnerability, great. Where does that vulnerability live? Is that a vulnerability in a core piece of my code, which yes, I need to address right now, or is it like, it’s in a test? Yes, it’s probably something I should fix, but maybe not the biggest issue right this second. And so I think tools like that are really helping because the thing that we found, and again, a user of inspector told us this, and I won’t call out the exact AI tool they were using, but they were using a generic LLM with some stuff. And then they were using inspector. And one of the things that they had said was, wow. Yeah. Like inspector is actually catching the issue with all of the, the, uh, it detected that a particular issue was essentially a, um, a false positive because it looked at a potential remote code execution and looked at all the stuff alongside the code and said, you are clearly have an allow list. So given that you have this allow list, we recognize it’s not a remote code execution, or rather arbitrary code sort of execution attack. And I think it’s stuff like that, that we’re seeing starting to get developed more and more. Whereas a lot of the tradition, I want to say traditional with AI, even though it’s been, you know, like in the past year, everything shifts. Yeah.

When we look at sort of how folks were using LLMs even just a year ago, a lot has shifted and we’re seeing less of these false positives coming out of AI because people are using AI the way it should be used, where it’s you’re supplementing all these other tools that are out.

CRob (08:40)
That’s awesome.

And this might lead into this next question. AI and automation are finding a lot more potential issues, but they’re not always better. And is that what you think that secret sauce of having that security expertise and that helps kind of balance out finding a vulnerability and then kind of sharing that information with the maintainer effectively?

Mike Lieberman (09:08)
Yeah, so I mean, I think when it comes to stuff like that, the way I, you know, I was actually having a conversation with a friend just a few days ago about this issue and I’m reminded of issues just even before AI. And one of the big things that maintainers would ask is, give me a way to reproduce this. If you’re not gonna give me a way to reproduce this, I’m not gonna, you know, I’m not gonna accept your report here and I’m not gonna do a ton of investigation to figure out what you intended to mean.

And I think it’s the same way with AI here, where we’re starting to see with some of the stuff coming out of AI XCC and some other places, we are starting to see tools that are being built that are actually generating the tests and whatnot that can reproduce these vulnerabilities that the LLMs are claiming, or AI tools are claiming. And I think that to me is important because when I look at Daniel from Curl or some of these other folks who are like,

I am so sick of all of these AI reports. It’s like every single one that they’re claiming is an AI report, it’s like they didn’t give me a way to reproduce it. Or even worse, the AI said, here’s a list of steps to reproduce. folks are coming out and saying, that function that you were claiming needs to get run doesn’t exist. And so I’m just thinking to myself, well, why not just write a test that does that thing, you know, and have the LLM write the test, whatever, but prove out that like, hey, an AI tool generated a test and I can run that test and I could see, yep, that is an exploit. That is actually a vulnerability. Now I can go and take that and package it up, it over to, you know, hand it over to the maintainer. And I think if I’m as a maintainer of various open source projects,

If I received something that said, hey, here is a test, you can run that test. And again, by the test, mean like an actual test, a test that makes up other code and tries to do whatever, but an actual test. If you have that, I as a maintainer would say, absolutely, that is a real vulnerability. But I think the thing that we’re seeing right now is we’re seeing all this sort of slop, which is.

Again, it’s just similar to the slop we saw years ago with other sort of automated vulnerability reporting and just generally in tech. And I think the problem here is still kind of comes back to lazy maintainer or sorry, not lazy maintainers, but lazy submitters and just other sort of bad actors who are just like, yeah, I’m just gonna throw a thing out there and hopefully one of these is right. And I’m gonna get it, make a name for myself.

CRob (11:58)
A wise man once said that knowing is half the battle.

Mike Lieberman (12:01)
Yes.

CRob (12:03)
And thinking about it from this maintainer developer perspective, almost always maintainers are volunteers first. They’re there because they have amazing idea they wanna share, they have a problem they’re trying to solve. Some people are paid to do a specific thing, but the majority of folks are volunteers first. And security experts 12th, 18th, security is not necessarily a core skill that most developers have. what design choices, kind of thinking about when you were looking at Inspector, what design choices did you make to help meet the maintainers where they are, where they are experts in languages or frameworks or kind of these techniques or algorithms? But how are you helping them where they are rather than expecting them to become a full-time securityologist like you or I?

Mike Lieberman (12:58)
So we have a mantra here at Kusari, which is essentially just don’t piss off the engineers, right? As engineers ourselves, as folks who, myself, I am a software engineer first, or really more of a dev ops, dev sec ops engineer first, became more of a software engineer over time. But one of the big sort of mantras was, one of the things that always frustrated me was you have to do all the security stuff.

And they were burdens to my daily job, right? Where I was not being, you know, again, this is me both as a maintainer of open source projects and also just, hey, I get paid as an engineer or whatever. What, but at the end of the day, I wasn’t incentivized to do secure things. I might’ve been yelled at. I might’ve been told thou shalt do this security thing, but my incentives were getting out this new feature, making my customer or my user happy, right?

And so when it comes to those sorts of things, that’s kind of how we’ve encoded all of this, where if somebody told me, hey, Mike, you put in a potential remote code execution attack or arbitrary code, whatever it is, like you put a SQL injection attack or some other, you’re not handling this off thing correctly. If you told me, yeah, that’s the thing. And you told me what I might need to look at. yeah, let me get on that. Let me fix that.

If you were to tell me, hey, you’re using a library that isn’t maintained and that everybody has mostly moved over to this other library, cool. I’ll, I’ll work on that, but don’t make the burden. Hey, there’s a, this library is unmaintained. Okay. What am I, what am I supposed to do about it? I don’t know what I’m supposed to do about it. Help me with suggestions. So when it comes to inspector, those are the sorts of things that we sort of baked in is we’re not just telling you this project. Is it maintained?

We’re telling you, hey, this project isn’t maintained, but it’s used in just one test. So maybe it’s not the immediate thing that needs to be fixed versus, hey, this thing is completely unmaintained and it’s potentially vulnerable. And this is something new you’re adding. Like this isn’t something that already exists. This is just bad practice. Like you should probably not include this new thing. Or, you know, and again, providing the suggestions to the user on what to actually do about it.

And some of those things can then be, know, know, inspector has a CLI tool that you can use. And I use it myself with Claude where, Hey, I run it kind of come in and, you know, uh, fix it. And like, it works pretty well. So I think again, it’s, it’s having, um, it’s, it’s the combination of things to sort of make sure that it, as an engineer, you know, you’re not being asked to become an expert in this thing, right? Uh, it’s okay to ask an engineer.

You are a database expert, you should be reasonable at securing databases, but securing the underlying OS and yada yada, hey, maybe you don’t need to be an expert in that. And that’s where tools like Inspector I think really help is they’re the ones who are being experts. Again, kind of going back to that, the home analogy, right? If I run a house, if I have a house, I don’t need to know the inner mechanics of know, a pin tumbler lock and yada, yada and, and, and how the various cameras, you know, that that are looking at the outside of my house, how they all interoperate. No, I just need to know, are they working if something kind of, you know, the battery died on this, I know how to change your battery, let me kind of focus on that. But if they were kind of come in and say, No, no, you need to understand the innards of the networking and you need to understand audio visual processing, I’d be like, No, just not gonna work.

So again, make sure that developers can focus just on what their experts in, and what their primary responsibility is, which is usually to the user. And yes, security is a responsibility there, but they’re not going to be generic security experts. And so what can we do to help them hold their hand and tell them what needs to be done in a way that they can kind of say, yeah, you’re asking me to do two or three small little things. Awesome. By the way, we here at Kusari have made Inspector free for open source, but not just open source, specifically for CNCF and open SSF. You have full sort of unfettered access, no rate limits, no quotas. And love to see folks sign up. The website is kusari.cloud. And yeah, yeah, I want to see folks using it.

CRob (17:45)
It’s really interesting and I love the focus on again, because you’re all you grew up through this. are in software engineer. So I love the focus on trying to how to relieve that burden from these, this army of volunteers. So let’s do something else we do often in cybersecurity. Let’s get our crystal ball out and, you know, thinking ahead from your perspective, what do you think, you know, good security tooling for open source looks like in three to five years?

Mike Lieberman (18:16)
I think the biggest thing in security tooling is better user experience. think that to me is one of the biggest challenges. And right today, and I think that’s where a lot of folks are focusing their efforts, it’s, you we need to some extent, you know, and I know, like, the first thing that came to mind, is Kubernetes, but for security, right? And I recognize that Kubernetes, depending on who you talk to, you know, YAML files,

But no, it really did democratize and make simpler the orchestrating complex container workloads, right? And I think when it comes to security, user experience is often kind of a secondary concern compared to just the, did I prevent the security, you the issue, but that’s kind of, as our world continues to get more complex and complicated and things are scaling up and we’re having AI and all these different things. The need for security continues to increase more and more every day. But with that said, if the answer is using these security tools requires, you know, tons of certifications and whatnot for just to use the security tool, right? Not to become an expert, but just to use the security tool, if you need to be an expert in all these different things, it becomes super difficult, nobody’s gonna do it. So I think we’re gonna start to see to some extent, more tools like Inspector, but also in addition to that, more tools like, and I know we’re working on this in OpenSSF, tools that make adopting of Salsa trivial for the average project. Tools that help just sort of generally with security build out that UX, make it simpler for the average engineer to do that. Similar to how we saw stuff

in that space with DevOps, right? Where you had developers and operations, those worlds kind of became more combined. And what happened was you had tools like your Terraforms or, you know, Open Tofu and Ansible and all of these great things that kind of came out of that space to kind of make it easier for both folks who are focused in operations to get a little closer to developers and then developers to actually also help out with some of the operations, infrastructure, engineering, those sorts of things. And I think we’re gonna start to see more of that as time kind of goes on where those like, I’m gonna call like security primitives are more encoded in the tools we have. So I think we’re gonna start to see a lot of tools out there become secure by design and have a lot of the security features baked in. And then also the security tools that we have just generally become a little bit simpler and where areas where they can’t be super simple, we’re gonna see tools more tools like Inspector that kind of come in and operate similar to how you might imagine the security expert to kind of come in and put the pieces together, which again, doesn’t eliminate the security engineer. I just want to be clear, like security engineers are very much still needed. The challenge is the security engineer is now being tasked. Whereas before you had to be an expert in a small set of domains. Now you’re being asked to be an expert across everything and they need to understand that they’re going to be the ones who are like taking these new security tools and given that better UX are going to be able to scale that across, you know, 10,000 projects, you know, a hundred different AI agents, all of this, like, you know, a million containers, all of those things. So I think we’re going to start seeing a lot more of the security tools working better to scale up what we’re doing.

CRob (22:04)
That is an amazing vision. look forward to observing that over the years. Hopefully your vision becomes a reality. Yeah, thank And as we’re winding down, do you have any closing thoughts or any call to action for the audience?

Mike Lieberman (22:19)
Yeah, I think the, so there’s two big ones. One is, hey, if you’re a maintainer and engineer, right? I know you care about security because even when I was not a security engineer, I cared about security. So what I want to hear from maintainers is how can the open source world help, right? How can we help you not get clobbered by a million?

letters from lawyers and other people demanding security features in your stuff. How can we, as an open source community, help out, open source security community, help out? How can we make the tools easier? How can we make sure that those tools fit your needs? And that includes whether it’s inspector or, you know, other things, hey. And on that note as well, you know, CNCF and OpenSSF projects can use inspector..

And the other call to action, I know I say this a lot, large organizations that are using open source, it is your responsibility to provide the incentives to make sure that open source is more secure. Like we can all demand, hey, we need better open source security tooling. We need this, that, and the other thing. But if nobody’s paying for it, if at the end of the day, you know, a random engineer who’s making that open source security tool, if they can’t pay the bills, they’re not going to do that. If they are getting clobbered with a million different feature requests, it’s just not going to work. So we need to make sure. And I know that there’s things like the sovereign tech fund want to see more of that. But just sort of generally, I think it needs to come from these multi billion, multi trillion dollar companies coming in and saying, hey, we are willing to foot a good deal of this bill right in order to make the world more secure for everybody.

CRob (24:17)
Those are some wise words and also I think a wonderful vision we all can work towards together. Mike Lieberman from Kusari, thank you my friend. I loved having you on. And with that, we’re gonna call this a wrap. I want everyone to stay cyber safe and sound and have a great day.

Kusari Partners with OpenSSF to Strengthen Open Source Software Supply Chain Security

By Blog, Guest Blog

Cross-post originally published on the Kusari Blog

Open source software powers the modern world; securing it remains a shared responsibility.

The software supply chain is becoming more complex and more exposed with every release. Modern applications rely on vast ecosystems of open source components, dependencies, and increasingly AI-generated code. While this accelerates innovation, it also expands the attack surface dramatically. Threat actors are taking advantage of this complexity with more frequent and sophisticated attacks, from dependency confusion and malicious package injections to license risks that consistently target open source communities.

At the same time, developers are asked to move faster while ensuring security and compliance across thousands of components. Traditional security reviews often happen too late in the development lifecycle, creating friction between development and security teams and leaving maintainers overwhelmed by reactive work.

Kusari is proud to partner with the Open Source Security Foundation (OpenSSF) to offer Kusari Inspector at no cost to OpenSSF projects. Together, we’re helping maintainers and security teams gain deeper visibility into their software supply chains and better understand the relationships between first-party code, third-party dependencies, and transitive components.  

Projects adopting Kusari Inspector include Gemara, GitTUF, GUAC, in-toto/Witness, OpenVEX, Protobom and Supply-chain Levels for Software Artifacts (SLSA). As AI coding tools become standard in open source development, Kusari Inspector serves as the safety net maintainers didn’t know they needed. 

“I used Claude to submit a pull request to go-witness,” said John Kjell, a maintainer of in-toto/Witness. “Kusari Inspector found an issue that Claude didn’t catch. When I asked Claude to fix what Kusari Inspector flagged, it did.”

Maintainers are under growing pressure. According to Kusari’s Application Security in Practice report, organizations continue to struggle with noise, fragmented tooling, and limited visibility into what’s actually running in production. The same challenges affect open source projects — often with fewer resources.

Kusari Inspector helps OpenSSF projects:

  • Map dependencies and transitive risk
  • Identify gaps in attestations and provenance
  • Understand how components relate across builds and releases
  • Reduce manual investigation and security guesswork

Kusari Inspector – Secure Contributions at the Pull Request

Kusari Inspector also helps strengthen the relationship between developers and security teams. Our Application Security in Practice research found that two-thirds of teams spend up to 20 hours per week responding to supply chain incidents — time diverted from building and innovating. 

For open source projects, the burden is often even heavier. From our experience in co-creating and maintaining GUAC, we know most projects are maintained by small teams of part-time contributors and already overextended maintainers who don’t have dedicated security staff. Every reactive investigation, dependency review, or license question pulls limited capacity away from priorities and community support — making proactive, workflow-integrated security even more critical.

By increasing automated checks directly in pull requests, projects reduce review latency and catch issues earlier, shifting from reactive firefighting to proactive prevention. Instead of maintainers “owning” reviews in isolation, Kusari Inspector brings them integrated, context-aware feedback — closer to development and accelerating secure delivery.

This partnership gives OpenSSF projects the clarity they need to make informed security decisions without disrupting developer workflows.

“The OpenSSF welcomes Kusari Inspector as a clear demonstration of community support. This helps our projects shift from reactive security measures to proactive, integrated prevention at scale,” said Steve Fernandez, General Manager, OpenSSF.

“Kusari’s journey has always been deeply connected to the open source security community. We’ve focused on closing knowledge gaps through better metadata, relationships, and insight,” said Tim Miller, Kusari Co-Founder and CEO. “Collaborating with OpenSSF reflects exactly why Kusari was founded: to turn transparency into actionable trust.”

If you’re an OpenSSF project maintainer or contributor interested in strengthening your supply chain posture, use Kusari Inspector for free — https://us.kusari.cloud/signup.

Author Bio

Michael LiebermanMichael Lieberman is co-founder and CTO of Kusari where he helps build transparency and security in the software supply chain. Michael is an active member of the open source community, co-creating the GUAC and FRSCA projects and co-leading the CNCF’s Secure Software Factory Reference Architecture whitepaper. He is an elected member of the OpenSSF Governing Board and Technical Advisory Council along with CNCF TAG Security Lead and an SLSA steering committee member.

Leading Tech Coalition Invests $12.5 Million Through OpenSSF and Alpha-Omega to Strengthen Open Source Security

By Blog

Securing the open source software that underlies our digital infrastructure is a persistent and complex challenge that continues to evolve. The Linux Foundation announced a $12.5 million collective investment to be managed by Alpha-Omega and The Open Source Security Foundation (OpenSSF). This funding comes from key partners including Anthropic, Amazon Web Services (AWS), Google, Google DeepMind, GitHub, Microsoft, and OpenAI. The goal is to strengthen the security, resilience, and long-term sustainability of the open source ecosystem worldwide.

Building on Proven Success through OpenSSF Initiatives

This new investment provides critical support for OpenSSF’s proven, maintainer-centric initiatives. Targeted financial support is a key catalyst for sustained improvement in open source security. The results of the OpenSSF’s collective work in 2025 are clear:

  • Alpha-Omega invested $5.8 million in 14 critical open source projects and completed over 60 security audits and engagements.
  • Growing a Global Community: OpenSSF grew to 117 member organizations and was advanced by 267+ active contributors from 112 organizations, working across 10 Working Groups and 32 Technical Initiatives.
  • Driving Technical Impact: The OpenSSF Technical Advisory Council (TAC) awarded over $660,000 in funding across 14 Technical Initiatives, strengthening supply chain integrity, advancing transparency tools like Sigstore, and enabling community-driven security audits.
  • Measurable Security Uplift: Focused security engagements across critical projects resulted in 52 vulnerabilities fixed and 5 fuzzing frameworks implemented.
  • Expanding Education: Nearly 20,000 course enrollments across OpenSSF’s free training programs, with new courses like Security for Software Development Managers and Secure AI/ML-Driven Software Development empowering developers globally.
  • Global Policy Engagement: Launched the Global Cyber Policy Working Group and served as a challenge advisor for the Artificial Intelligence Cyber Challenge (AIxCC), ensuring the open source voice is heard in evolving regulations like the EU Cyber Resilience Act (CRA).

AI: A New Frontier in Security

The security landscape is changing fast. Artificial intelligence (AI) accelerates both software development and the discovery of vulnerabilities, which creates new demands on maintainers and security teams. However, OpenSSF recognizes that grant funding alone is not the sole solution to the problems AI tools are causing today on open source security teams. This moment also offers powerful new opportunities to improve how security work is completed.

This new funding will help the OpenSSF provide the active resources and dedicated projects needed to support overworked maintainers with the triage and processing of the increased AI-generated security reports they are currently receiving. Our response will feature global strategies tailored to the needs of maintainers and their communities.

“Open source software now underpins the majority of modern software systems, which means the security of that ecosystem affects nearly every organization and user worldwide,” said Christopher Robinson, CTO and Chief Security Architect at OpenSSF. “Investments like this allow the community to focus on what matters most: empowering maintainers, strengthening security practices across projects, and raising the overall security bar for the global software supply chain.”

Securing the Open Source Lifecycle

The true measure of success will be execution. Success is not about how much AI we introduce into open source. It is determined by whether maintainers can use it to reduce risk, remediate serious vulnerabilities faster, and strengthen the software supply chain long term. We are grateful to our funding partners for their commitment to this work, and we look forward to continuing it alongside the maintainers and communities that power the world’s digital systems.

“Our commitment remains focused: to sustainably secure the entire lifecycle of open source software,” said Steve Fernandez, General Manager of OpenSSF. “By directly empowering the maintainers, we have an extraordinary opportunity to ensure that those at the front lines of software security have the tools and standards to take preventative measures to stay ahead of issues and build a more resilient ecosystem for everyone.”

To learn more about open source security initiatives at the Linux Foundation, please visit openssf.org and alpha-omega.dev.

What’s in the SOSS? Podcast #41 – S2E18 The Remediation Revolution: How AI Agents Are Transforming Open Source Security with John Amaral of Root.io

By Podcast

Summary

In this episode of What’s in the SOSS, CRob sits down with John Amaral from Root.io to explore the evolving landscape of open source security and vulnerability management. They discuss how AI and LLM technologies are revolutionizing the way we approach security challenges, from the shift away from traditional “scan and triage” methodologies to an emerging “fix first” approach powered by agentic systems. John shares insights on the democratization of coding through AI tools, the unique security challenges of containerized environments versus traditional VMs, and how modern developers can leverage AI as a “pair programmer” and security analyst. The conversation covers the transition from “shift left” to “shift out” security practices and offers practical advice for open source maintainers looking to enhance their security posture using AI tools.

Conversation Highlights

00:25 – Welcome and introductions
01:05 – John’s open source journey and Root.io’s SIM Toolkit project
02:24 – How application development has evolved over 20 years
05:44 – The shift from engineering rigor to accessible coding with AI
08:29 – Balancing AI acceleration with security responsibilities
10:08 – Traditional vs. containerized vulnerability management approaches
13:18 – Leveraging AI and ML for modern vulnerability management
16:58 – The coming “remediation revolution” and fix-first approach
18:24 – Why “shift left” security isn’t working for developers
19:35 – Using AI as a cybernetic programming and analysis partner
20:02 – Call to action: Start using AI tools for security today
22:00 – Closing thoughts and wrap-up

Transcript

Intro Music & Promotional clip (00:00)

CRob (00:25)
Welcome, welcome, welcome to What’s in the SOSS, the OpenSSF’s podcast where I talk to upstream maintainers, industry professionals, educators, academics, and researchers all about the amazing world of upstream open source security and software supply chain security.

Today, we have a real treat. We have John from Root.io with us here, and we’re going to be talking a little bit about some of the new air quotes, “cutting edge” things going on in the space of containers and AI security. But before we jump into it, John, could maybe you share a little bit with the audience, like how you got into open source and what you’re doing upstream?

John (01:05)
First of all, great to be here. Thank you so much for taking the time at Black Hat to have a conversation. I really appreciate it. Open source, really great topic. I love it. Been doing stuff with open source for quite some time. How do I get into it? I’m a builder. I make things. I make software been writing software. Folks can’t see me, but you know, I’m gray and have no hair and all that sort of We’ve been doing this a while. And I think that it’s been a great journey and a pleasure in my life to work with software in a way that democratizes it, gets it out there. I’ve taken a special interest in security for a long time, 20 years of working in cybersecurity. It’s a problem that’s been near and dear to me since the first day I ever had my like first floppy disk, corrupted. I’ve been on a mission to fix that. And my open source journey has been diverse. My company, Root.io, we are the maintainers of an open source project called Slim SIM (or SUM) Toolkit, which is a pretty popular open source project that is about security and containers. And it’s been our goal, myself personally, and as in my latest company to really try to help make open source secure for the masses.

CRob (02:24)
Excellent. That is an excellent kind of vision and direction to take things. So from your perspective, I feel we’re very similar age and kind of came up maybe in semi-related paths. But from your perspective, how have you seen application development kind of transmogrify over the last 20 or so years? What has gotten better? What might’ve gotten a little worse?

John (02:51)
20 years, big time frame talking about modern open source software. I remember when Linux first came out. And I was playing with it. I actually ported it to a single board computer as one of my jobs as an engineer back in the day, which was super fun. Of course, we’ve seen what happened by making software available to folks. It’s become the foundation of everything.

Andreessen said software will eat the world while the teeth were open source. They really made software available and now 95 or more percent of everything we touch and do is open source software. I’ll add that in the grand scheme of things, it’s been tremendously secure, especially projects like Linux. We’re really splitting hairs, but security problems are real. as we’ve seen, proliferation of open source and proliferation of repos with things like GitHub and all that. Then today, proliferation of tooling and the ability to build software and then to build software with AI is just simply exponentiating the rate at which we can do things. Good people who build software for the right reasons can do things. Bad people who do things for the bad reasons can do things. And it’s an arms race.

And I think it’s really both benefiting software development, society, software builders with these tremendously powerful tools to do things that they want. A person in my career arc, today I feel like I have the power to write code at a rate that’s probably better than I ever have. I’ve always been hands on the keyboard, but I feel rejuvenated. I’ve become a business person in my life and built companies.

And I didn’t always have the time or maybe even the moment to do coding at the level I’d like. And today I’m banging out projects like I was 25 or even better. But at the same time that we’re getting all this leverage universally, we also noticed that there’s an impending kind of security risk where, yeah, we can find vulnerabilities and generate them faster than ever. And LLMs aren’t quite good yet at secure coding. I think they will be. But also attackers are using it for exploits and really as soon as a disclosed vulnerability comes out or even minutes later, they’re writing exploits that can target those. I love the fact that the pace and the leverage is high and I think the world’s going to do great things with it, the world of open source folks like us. At the same time, we’ve got to be more diligent and even better at defending.

CRob (05:44)
Right. I heard an interesting statement yesterday where folks were talking about software engineering as a discipline that’s maybe 40 to 60 years old. And engineering was kind of the core noun there. Where these people, these engineers were trained, they had a certain rigor. They might not have always enjoyed security, but they were engineers and there was a certain kind of elegance to the code and that was people much like artists where they took a lot of pride in their work and how the code you could understand what the code is. Today and especially in the last several years with the influx of AI tools especially that it’s a blessing and a curse that anybody can be a developer. Not just people that don’t have time that used to do it and now they get to of scratch that itch. But now anyone can write code and they may not necessarily have that same rigor and discipline that comes from like most of them engineering trades.

John (06:42)
I’m going to guess. I think it’s not walking out too far on limb that you probably coded in systems at some point in your life where you had a very small amount of memory to work with. You knew every line of code in the system. Like literally it was written. There might have been a shim operating system or something small, but I wrote embedded systems early in my career and we knew everything. We knew every line of code and the elegance and the and the efficiency of it and the speed of it. And we were very close to the CPU, very close to the hardware. It was slow building things because you had to handcraft everything, but it was very curated and very beautiful, so to speak. I find beauty in those things. You’re exactly right. I think I started to see this happen around the time when JVM started happening, Java Virtual Machines, where you didn’t have to worry about Java garbage collection. You didn’t have to worry about memory management.

And then progressively, levels of abstraction have changed right to to make coding faster and easier and I give it more you know more power and that’s great and we’ve built a lot more systems bigger systems open source helps. But now literally anyone who can speak cogently and describe what they want and get a system and. And I look at the code my LLM’s produce. I know what good code looks like. Our team is really good at engineering right?

Hmm, how did it think to do it that way? Then go back and we tell it what we want and you can massage it with some words. It’s really dangerous and if you don’t know how to look for security problems, that’s even more dangerous. Exactly, the level of abstraction is so high that people aren’t really curating code the way they might need to to build secure production grade systems.

CRob (08:29)
Especially if you are creating software with the intention of somebody else using it, probably in a business, then you’re not really thinking about all the extra steps you need to take to help protect yourself in your downstream.

John (08:44)
Yeah, yeah. think it’s an evolution, right? And where I think of it like these AI systems we’re working with are maybe second graders. When it comes to professional code authoring, they can produce a lot of good stuff, right? It’s really up to the user to discern what’s usable.

And we can get to prototypes very quickly, which I think is greatly powerful, which lets us iterate and develop. In my company, we use AI coding techniques for everything, but nothing gets into production, into customer hands that isn’t highly vetted and highly reviewed. So, the creation part goes much faster. The review part is still a human.

CRob (09:33)
Well, that’s good. Human on the loop is important.

John (09:35)
It is.

CRob (09:36)
So let’s change the topic slightly. Let’s talk a little bit more about vulnerability management. From your perspective, thinking about traditional brick and mortar organizations, how have you seen, what key differences do you see from someone that is more data center, server, VM focused versus the new generation of cloud native where we have containers and cloud?

What are some of the differences you see in managing your security profile and your vulnerabilities there?

John (10:08)
Yeah, so I’ll start out by a general statement about vulnerability management. In general, the way I observe current methodologies today are pretty traditional.

It’s scan, it’s inventory – What do I have for software? Let’s just focus on software. What do I have? Do I know what it is or not? Do I have a full inventory of it? Then you scan it and you get a laundry list of vulnerabilities, some false positives, false negatives that you’re able to find. And then I’ve got this long list and the typical pattern there is now triage, which are more important than others and which can I explain away. And then there’s a cycle of remediation, hopefully, a lot of times not, that you’re cycling work back to the engineering organization or to whoever is in charge of doing the remediation. And this is a very big loop, mostly starting with and ending with still long lists of vulnerabilities that need to be addressed and risk managed, right? It doesn’t really matter if you’re doing VMs or traditional software or containerized software. That’s the status quo, I would say, for the average company doing vulnerability maintenance. And vulnerability management, the remediation part of that ends up being some fractional work, meaning you just don’t have time to get to it all mostly, and it becomes a big tax on the development team to fix it. Because in software, it’s very difficult for DevSec teams to fix it when it’s actually a coding problem in the end.

In traditional VM world, I’d say that the potential impact and the velocity at which those move compared to containerized environments, where you have

Kubernetes and other kinds of orchestration systems that can literally proliferate containers everywhere in a place where infrastructure as code is the norm. I just say that the risk surface in these containerized environments is much more vast and oftentimes less understood. Whereas traditional VMs still follow a pattern of pretty prescriptive way of deployment. So I think in the end, the more prolific you can be with deploying code, the more likely you’ll have this massive risk surface and containers are so portable and easy to produce that they’re everywhere. You can pull them down from Docker Hub and these things are full of vulnerabilities and they’re sitting on people’s desks.

They’re sitting in staging areas or sitting in production. So proliferation is vast. And I think that in conjunction with really high vulnerability reporting rates, really high code production rates, vast consumption of open source, and then exploits at AI speed, we’re seeing this kind of almost explosive moment in risk from vulnerability management.

CRob (13:18)
So there’s been, over the last several, like machine intelligence, which has now transformed into artificial intelligence. It’s been around for several decades, but it seems like most recently, the last four years, two years, it has been exponentially accelerating. We have this whole spectrum of things, AI, ML, LLM, GenAI, now we have Agentic and MCP servers.

So kind of looking at all these different technologies, what recommendations do you have for organizations that are looking to try to manage their vulnerabilities and potentially leveraging some of this new intelligence, these new capabilities?

John (13:58)
Yeah, it’s amazing at the rate of change of these kinds of things.

CRob (14:02)
It’s crazy.

John (14:03)
I think there’s a massively accelerating, kind of exponentially accelerating feedback loop because once you have LLMs that can do work, they can help you evolve the systems that they manifest faster and faster and faster. It’s a flywheel effect. And that is where we’re going to get all this leverage in LLMs. At Root, we build an agentic platform that does vulnerability patching at scale. We’re trying to achieve sort of an open source scale level of that.

And I only said that because I believe that rapidly, not just us, but from an industry perspective, we’re evolving to have the capabilities through agentic systems based on modern LLMs to be able to really understand and modify code at scale. There’s a lot of investment going in by all the major players, whether it’s Google or Anthropic or OpenAI to make these LLM systems really good at understanding and generating code. At the heart of most vulnerabilities today, it’s a coding problem. You have vulnerable code.

And so, we’ve been able to exploit the coding capabilities to turn it into an expert security engineer and maintainer of any software system. And so I think what we’re on the verge of is this, I’ll call it remediation revolution. I mentioned that the status quo is typically inventory, scan, list, triage, do your best. That’s a scan for us kind of, you know, I’ll call it, it’s a mode where mostly you’re just trying to get a comprehensive list of the vulnerabilities you have. It’s going to get flipped on its head with this kind of technique where it’s going to be just fix everything first. And there’ll be outliers. There’ll be things that are kind of technically impossible to fix for a while. For instance, it could be a disclosure, but you really don’t know how it works. You don’t have CWEs. You don’t have all the things yet. So you can’t really know yet.

That gap will close very quickly once you know what code base it’s in and you understand it maybe through a POC or something like that. But I think we’re gonna enter into the remediation revolution of vulnerability management where at least for third party open source code, most of it will be fixed – a priority.

Now, zero days will start to happen faster, there’ll be all the things and there’ll be a long tail on this and certainly probably things we can’t even imagine yet. But generally, I think vulnerability management as we know it will enter into this phase of fix first. And I think that’s really exciting because in the end it creates a lot of work for teams to manage those lists, to deal with the re-engineering cycle. It’s basically latent rework that you have to do. You don’t really know what’s coming. And I think that can go away, which is exciting because it frees up security practitioners and engineers to focus on, I’d say more meaningful problems, less toil problems. And that’s good for software.

CRob (17:08)
It’s good for the security engineers.

John (17:09)
Correct.

CRob (17:10)
It’s good for the developers.

John (17:11)
It’s really good for developers. I think generally the shift left revolution in software really didn’t work the way people thought. Shifting that work left, it has two major frictions. One is it’s shifting new work to the engineering teams who are already maximally busy.

CRob (17:29)
Correct.

John (17:29)
I didn’t have time to do a lot of other things when I was an engineer. And the second is software engineers aren’t security engineers. They really don’t like the work and maybe aren’t good at the work. And so what we really want is to not have that work land on their plate. I think we’re entering into an age where, and this is a general statement for software, where software as a service and the idea of shift left is really going to be replaced with I call shift out, which is if you can have an agentic system do the work for you, especially if it’s work that is toilsome and difficult, low value, or even just security maintenance, right? Like lot of this work is hard. It’s hard. That patching things is hard, especially for the engineer who doesn’t know the code. If you can make that work go away and make it secure and agents can do that for you, I think there’s higher value work for engineers to be doing.

CRob (18:24)
Well, and especially with the trend with open source, kind of where people are assembling composing apps instead of creating them whole cloth. It’s a very rare engineer indeed that’s going to understand every piece of code that’s in there.

John (18:37)
And they don’t. I don’t think it’s feasible. don’t know one except the folks who write node for instance, Node works internally. They don’t know. And if there’s a vulnerability down there, some of that stuff’s really esoteric. You have to know how that code works to fix it. As I said, luckily, agent existing LLM systems with agents kind of powering them or using or exploiting them are really good at understanding big code bases. They have like almost a perfect memory for how the code fits together. Humans don’t, and it takes a long time to learn this code.

CRob (19:11)
Yeah, absolutely. And I’ve been using leveraging AI in my practice is there are certain specific tasks AI does very well. It’s great at analyzing large pools of data and providing you lists and kind of pointers and hints. Not so great making it up by its own, but generally it’s the expert system. It’s nice to have a buddy there to assist you.

John (19:35)
It’s a pair programmer for me, and it’s a pair of data analysts for you, and that’s how you use it. I think that’s a perfect. We effectively have become cybernetic organisms. Our organic capabilities augmented with this really powerful tool. I think it’s going to keep getting more and more excellent at the tasks that we need offloaded.

CRob (19:54)
That’s great. As we’re wrapping up here, do you have any closing thoughts or a call to action for the audience?

John (20:02)
Call to action for the audience – I think it’s again, passion play for me, vulnerability management, security of open source. A couple of things. same. Again, same cloth – I think again, we’re entering an age where think security, vulnerability management can be disrupted. I think anyone who’s struggling with kind of high effort work and that never ending list helps on the way techniques you can do with open source projects and that can get you started. Just for instance, researching vulnerabilities. If you’re not using LLMs for that, you should start tomorrow. It is an amazing buddy for digging in and understanding how things work and what these exploits are and what these risks are. There are tooling like mine and others out there that you can use to really take a lot of effort away from vulnerability management. I’d say that for any open source maintainers out there, I think you can start using these programming tools as pair programmers and security analysts for you. And they’re pretty good. And if you just learn some prompting techniques, you can probably secure your code at a level that you hadn’t before. It’s pretty good at figuring out where your security weaknesses are and telling you what to do about them. I think just these things can probably enhance security open source tremendously.

CRob (24:40)
That would be amazing to help kind of offload some of that burden from our maintainers and let them work on that excellent…

John (21:46)
Threat modeling, for instance, they’re actually pretty good at it. Yeah. Which is amazing. So start using the tools and make them your friend. And even if you don’t want to use them as a pair of programmer, certainly use them as a adjunct SecOps engineer.

CRob (22:00)
Well, excellent. John from Root.io. I really appreciate you coming in here, sharing your vision and your wisdom with the audience. Thanks for showing up.

John (22:10)
Pleasure was mine. Thank you so much for having me.

CRob (22:12)
And thank you everybody. That is a wrap. Happy open sourcing everybody. We’ll talk to you soon.