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Oct 07
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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

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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.

Aug 26
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What’s in the SOSS? Podcast #38 – S2E15 Securing AI: A Conversation with Sarah Evans on OpenSSF’s AI/ML Initiatives

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Summary

In this episode of “What’s in the SOSS,” we welcome back Sarah Evans, Distinguished Engineer at Dell Technologies and a key figure in the OpenSSF’s AI/ML Security working group. Sarah discusses the critical work being done to extend secure software development practices to the rapidly evolving field of AI. She dives into the AI Model Signing project, the groundbreaking MLOps whitepaper developed in partnership with Ericsson, and the crucial work of identifying and addressing new personas in AI/ML operations. Tune in to learn how OpenSSF is shaping the future of AI security and what challenges and opportunities lie ahead.

Conversation Highlights

0:00 Welcome and Introduction to Sarah Evans
0:48 Sarah Evans: Role at Dell Technologies and Involvement in OpenSSF
1:38 The OpenSSF AI/ML Working Group: Genesis and Goals
3:37 Deep Dive: The AI Model Signing Project with Sigstore
4:28 AI Model Signing: Benefits for Developers
5:20 Transition to the MLSeCOps White Paper
5:49 The Mission of the MLSecOps White Paper: Addressing Industry Gaps
7:00 Collaboration with Ericsson on the MLEC Ops White Paper
8:15 Identifying and Addressing New Personas in AI/ML Ops
10:04 The Power of Open Source in Extending Previous Work
10:15 Future Directions for OpenSSF’s AI/ML Strategy
11:21 OpenSSF’s Broader AI Security Focus
12:08 Sneak Peek: New Companion Video Podcast on AI Security
12:31 Sarah’s Personal Focus: The Year of the Agents (2025)
13:00 Security Concerns: Bringing Together Data Models and Code in AI Applications
14:00 Conclusion and Thanks

Transcript

0:00 Intro Music & Promo Clip: We have so much experience in applying secure software development to CI/CD and software, we can extend what we’ve learned to the data teams and to those AI/ML engineering teams because ultimately, I don’t think that we want a world where we have to do separate security governance across AI apps.

CRob:

0:20: Welcome, welcome, welcome to What’s in the SOSS, where we talk to interesting characters from around the open source security ecosystem, maintainers, engineers, thought leaders, contributors, and I just get to talk to a lot of really great people along the way.  Today we have a friend of the show we’ve already had discussions with her in the past. I am so pleased and proud to introduce my friend Sarah Evans. Sarah, for our audience, could you maybe just tell them, remind them, you know who you are and what do you do and what you’ve been up to since our last talk.

Sarah Evans:

0:57: Well, thanks for having me here. I’m a distinguished engineer at Dell Technologies, and I have two roles. One is I do security applied research for my company looking at the future of security in our products and what innovation that we need to explore to improve the security by design. My second role is to activate my company to participate in OpenSSF, which I have thoroughly enjoyed getting to work with friends such as yourselves. I am very active and engaged in the AI/ML working group and trying to advocate for AI security.

CRob:

1:37: Awesome, yeah. And that actually brings us to our talk today. Our friends within your working group, the AI/ML working group, you’ve had a flurry of activity lately. I would love to talk about, you know, first off, let’s give the audience some context. Let’s talk about what is this group, and what’s kind of your some of your goals.

Sarah Evans:

1:58: Yeah, so the AI/ML working group really kind of came into fruition about a year and a half ago, I think, and we needed a space where we could talk about how the work that software developers were doing would change as they started to build applications that had AI in it. So were there things that we were doing today that could apply to the way the technology was changing?

One of the initial concerns is software secure software development we know a lot about that, but we may know less about AI. So is is a home for AI and OpenSSF appropriate? Should we be deeply partnering with some of the other foundations that are creating these data sets, creating the tools and models, and so we started the working group where our commitment to the tech was that we would deeply engage with the other groups around the ecosystem which we have. Done, but then we’ve also been looking for where are those places that are uniquely in the OpenSSF wheelhouse or swim lane of expertise on extending software security to AI applications, and I think that we’ve done a really good job of kind of exploring some of those places.

One of them has been with a white paper that we are partnering with another member in Ericsson to deliver, and that is something that we’re very proud of sharing with the community.

CRob:

3:28: Great, I’m really excited to talk about these projects because I for one welcome our robot overlords. Let’s first off start off – we had a big, you guys had a big announcement that really seems to have captured the imagination of the community. Let’s talk about the AI model signing project.

Sarah Evans:

3:47: Yes, so the model signing project, we worked that as a special interest group within our working group. We were approached by, some folks who are working in partnership with SigS store and. The idea was that if you can use Sigstore to sign code, could you extend Sigstore to sign a model and fill and close a gap that didn’t exist in the industry, and as you know, we were able to do that. There was a team of people that came together in the open source fashion to extend a tool to a new use case. And that’s just been very exciting to watch that evolve.

CRob

4:27: That’s awesome.

Sarah Evans:

4:28: So thinking about it from the developer perspective, I’m a developer working in the AI, how does this help me?

CRob

4:36: Right?

Sarah Evans:

4:36: So right now if you are pulling a model off of hugging face as an example, you don’t have any cryptographic digital signature on that model that that verifies it. The way you would with code. And so if that model has been signed with the SIS store components, then now you have the information that you would use to validate code. You can also follow some of those similar processes to validate a signed model.

CRob

5:07: Pretty cool.

Sara Evans

5:08: Yeah, it’s a really good use case for the supply chain security. And extending what we know about software to models and data that are part of our AI applications.

CRob

5:20: This seems to be kind of a theme for you taking classic ASA and applying it to the newer technologies. So let’s move on to the white paper. You and I have collaborated around some graphics for this, and then you’ve got a couple of folks you’re working with on the white paper. You’re shepherding through review and publication, and you should be able to read that now. So you know why do you think this talk, let’s talk about the white paper, you know, what’s it about? What’s it kind of the mission of it?

Sarah Evans

5:49: When the AI/ML working group first kicked off, I knew that we had seen this evolution of developing on open source software and processes called DevOps and then those evolved to DevSecOps over time. And so with the disruptive technology around AI/ML, I wanted to know what were the processes that a data scientist or an AI/ML engineer used and did they have the security governance they needed in their operational processes.

So I started to look at what is DataOps, what is MLops, what is LLMOps, like all the alphabet soup of ops all the ops. And I couldn’t find a lot of information online. And so I thought this is an industry gap that we have and we have so much experience in applying secure software development to CICD and software.

We can extend what we’ve learned to the data teams and to those AI/ML engineering teams because ultimately I don’t think that we want a world where we have to do separate security governance across AI apps that have these different operational pieces in them.

I was doing my research and I found a white paper by Ericsson on MLSecOps in the telco environment. Ericsson being a fellow member of OpenSSF, I, you know, worked through their OSPO and through some of the connections that we have in OpenSSF said, Hey, can you introduce me to those authors? I would love to see if we could up level that as a general resource to the community as an OpenSSF whitepaper. We were able to do that. They have been a fantastic partner in collaboration.

And so now we have for the industry an MLSecOps white paper reference architecture and some documentation about extending in two ways:

  1. One is if you’re a software developer now and you’re being asked to build an AI app, you have more information about what goes on in that MLOps environment.
  2. And if you are a person who’s creating an MLOps app and you haven’t had secure development training before, you now have a resource so it really serves kind of an existing member of our community and a new member of potential members of our community.

CRob

8:14: That’s really awesome. Congrats on that. Another area that we’ve collaborated on, the OpenSSF has a series of personas. We have 5 personas and that kind of organizes and drives our work. We have a Maintainer developer persona and OSPO persona and executive persona and so forth but one thing that you came to me that you realized early on as you were developing this white paper is there was a, there’s some gaps. Could you maybe talk about those gaps and what we’ve done to address them?

Sarah Evans

8:46: Yeah, where we found the gaps were in sub-personas so those main core personas that OpenSSF has been working with were, were just solid. We still have developers and maintainers, we still have security engineers…we still have folks working in our open source program offices, but the sub-personas were very software developer focused.

They really didn’t include some of the personas that we were seeing related to curating data sets, putting together end to end architectures, or, kind of putting together a pipeline for machine learning as a data engineer. So we, I worked based off of the language in that original Ericsson white paper that we have up leveled to an OSSF white paper to take those personas that work in that MLE op space and add them as sub personas within OpenSSF. So now we can all start to have the same language and understanding around who might be developing software applications, new members of our community that we want to be inclusive of and have language to understand how to reach them and partner with them.

CRob

10:04: I just love the power of open source where you find some previous work, you get value out of it, and then you expand it.  Thank you so much for contributing that back.

Sarah Evans

10:13: Absolutely.

CRob

10:15: And where are you going from here? Where are the next steps around the white paper?

Sarah

10:19: I think we want to spend some time championing and then you know, meeting with our community we’ve discovered that potentially OpenSSF would like to have a broader AI/ML strategy or program and so really understanding how those strategic efforts will evolve and making sure that we can plug into those and provide resources that that strategically move OpenSSF forward into this new space those could include an MLSecOps document or maybe even a converged enterprise view of multiple ops but we’re also open to just looking at. Maybe some of the other areas that have been identified such as dealing with potentially AI slop or other concerns related to AI/ML.

I think there’s a really great opportunity for OpenSSF to look through our stack of tools and processes and understand how we can extend those to AI/ML use cases and applications.

I know that there is an opportunity to have a strategic program around AI and securing AI applications, and I’m really excited and looking forward to what the future of OpenSSF tools, processes, procedures, best practices look like so we can really support our software developers as they’re developing secure AI applications.

CRob

11:12: That’s awesome. I’m really looking forward to collaborating with you all and kind of championing and showcasing the work going forward. So thank you very much.

Let’s move along. We will be creating a new companion video podcast focused on this amazing community of AI security experts we have here within OpenSSF and within the broader community, and we’ll be talking about AI security news and topics. And I’m going to give this, take this opportunity to give the listeners a sneak peek of what we might be discussing very soon. So from your perspective, Sarah, you know, beyond these cool projects that you’re working on, what are you personally keeping an eye on in this fast moving AI space?

Sarah Evans

12:42: Well, I’ll tell you, 2025 is the year of the agents, and understanding the accelerated rate that agents that impact they will have on AI applications has been something I’ve been spending a lot of time on.

CRob

12:56: Pretty cool. I’m looking forward to learning more with everyone together. And from your perspective again, what’s keeping you up at night in regards to this crazy AI/ML, LLM, GenAI agentic, blah blah blah, machine space? What what are you concerned about from a security perspective?

Sarah Evans

I think for me from a security perspective bringing together data models and deploying it with code really puts an end to end AI application. It puts a lot of pressure on teams that may not have had to tightly work together before to begin to tightly work together. And so that’s why the personas and the and the converged operations and thinking about how do we apply what security we know to new areas is so important because we don’t have a moment to lose.

There’s such accelerated excitement around leveraging AI and leveraging agents that’s going to be very important for us to have a common way to talk to each other and to begin to solve problems and challenges so that we can innovate with this technology.

CRob

13:59: Excellent. Well, Sarah, I really appreciate your time come and talk to us about these amazing going on and kind of giving us a sneak peek into the future. And you know, I, I want to thank you again from behalf of the foundation, our community, and you know all the maintainers and enterprises that we serve. So thanks for showing up today.

Sarah Evans

14:17: Yeah, thanks, CRob.

CRob

14:18: Yeah, and that’s a wrap today. Thank you for listening to what’s in the SOSS. Have a great day and happy open sourcing.

Outro

14:29: Like what you’re hearing, be sure to subscribe to what’s in the SOSS on Spotify, Apple Podcasts, Antennapod, Pocketcast, or wherever you get your podcasts. There’s a lot going on with the OpenSSF and many ways to stay on top of it all. Check out the newsletter for open source news, upcoming events, and other happenings. Go to OpenSSF.org/newsletter to subscribe. Connect with us on LinkedIn for the most up-to-date OpenSSF news and insight, and be a part of the OpenSSF community at OpenSSF.org/getinvolved. Thanks for listening and we’ll talk to you next time on What’s in the SOSS.

Aug 06
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OpenSSF at DEF CON 33: AI Cyber Challenge (AIxCC), MLSecOps, and Securing Critical Infrastructure

By Blog

By Jeff Diecks

The OpenSSF team will be attending DEF CON 33, where the winners of the AI Cyber Challenge (AIxCC) will be announced. We will also host a panel discussion at the AIxCC village to introduce the concept of MLSecOps.

AIxCC, led by DARPA and ARPA-H, is a two-year competition focused on developing AI-enabled software to automatically identify and patch vulnerabilities in source code, particularly in open source software underpinning critical infrastructure.

OpenSSF is supporting AIxCC as a challenge advisor, guiding the competition to ensure its solutions benefit the open source community. We are actively working with DARPA and ARPA-H to open source the winning systems, infrastructure, and data from the competition, and are designing a program to facilitate their successful adoption and use by open source projects. At least four of the competitors’ Cyber Resilience Systems will be open sourced on Friday, August 8 at DEF CON. The remaining CRSs will also be open sourced soon after the event.

Join Our Panel: Applying DevSecOps Lessons to MLSecOps

We will be hosting a panel talk at the AIxCC Village, “Applying DevSecOps Lessons to MLSecOps.” This presentation will delve into the evolving landscape of security with the advent of AI/ML applications.

The panelists for this discussion will be:

  • Christopher “CRob” Robinson – Chief Security Architect, OpenSSF
  • Sarah Evans – Security Applied Research Program Lead, Dell Technologies
  • Eoin Wickens – Director of Threat Intelligence, HiddenLayer

Just as DevSecOps integrated security practices into the Software Development Life Cycle (SDLC) to address critical software security gaps, Machine Learning Operations (MLOps) now needs to transition into MLSecOps. MLSecOps emphasizes integrating security practices throughout the ML development lifecycle, establishing security as a shared responsibility among ML developers, security practitioners, and operations teams. When thinking about securing MLOps using lessons learned from DevSecOps, the conversation includes open source tools from OpenSSF and other initiatives, such as Supply-Chain Levels for Software Artifacts (SLSA) and Sigstore, that can be extended to MLSecOps. This talk will explore some of those tools, as well as talk about potential tooling gaps the community can partner to close. Embracing this methodology enables early identification and mitigation of security risks, facilitating the development of secure and trustworthy ML models.  Embracing MLSecOps methodology enables early identification and mitigation of security risks, facilitating the development of secure and trustworthy ML models.

We invite you to join us on Saturday, August 9, from 10:30-11:15 a.m. at the AIxCC Village Stage to learn more about how the lessons from DevSecOps can be applied to the unique challenges of securing AI/ML systems and to understand the importance of adopting an MLSecOps approach for a more secure future in open source software.

About the Author

JeffJeff Diecks is the Technical Program Manager for the AI Cyber Challenge (AIxCC) at the Open Source Security Foundation (OpenSSF). A participant in open source since 1999, he’s delivered digital products and applications for dozens of universities, six professional sports leagues, state governments, global media companies, non-profits, and corporate clients.

Aug 01
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🎉 Celebrating Five Years of OpenSSF: A Journey Through Open Source Security

By Blog

August 2025 marks five years since the official formation of the Open Source Security Foundation (OpenSSF). Born out of a critical need to secure the software supply chains and open source ecosystems powering global technology infrastructure, OpenSSF quickly emerged as a community-driven leader in open source security.

“OpenSSF was founded to unify and strengthen global efforts around securing open source software. In five years, we’ve built a collaborative foundation that reaches across industries, governments, and ecosystems. Together, we’re building a world where open source is not only powerful—but trusted.” — Steve Fernandez, General Manager, OpenSSF

🌱 Beginnings: Answering the Call

OpenSSF was launched on August 3, 2020, consolidating earlier initiatives into a unified, cross-industry effort to protect open source projects. The urgency was clear—high-profile vulnerabilities such as Heartbleed served as stark reminders that collective action was essential to safeguard the digital infrastructure everyone depends on.

“From day one, OpenSSF has been about action—empowering the community to build and adopt real-world security solutions. Five years in, we’ve moved from ideas to impact. The work isn’t done, but the momentum is real, and the future is wide open.” — Christopher “CRob” Robinson, Chief Architect, OpenSSF

🚀 Milestones & Major Initiatives

Over the past five years, OpenSSF has spearheaded critical initiatives that shaped the landscape of open source security:

2021 – Secure Software Development Fundamentals:
Launching free educational courses on edX, OpenSSF equipped developers globally with foundational security practices.

“When we launched our first free training course in secure software development, we had one goal: make security knowledge available to every software developer. Today, that same mission powers all of OpenSSF—equipping developers, maintainers, and communities with the tools they need to make open source software more secure for everyone.” — David A. Wheeler, Director, Open Source Supply Chain Security, Linux Foundation

2021 – Sigstore: Open Source Signing for Everyone:
Sigstore was launched to make cryptographic signing accessible to all open source developers, providing a free and automated way to verify the integrity and provenance of software artifacts and metadata.

“Being part of the OpenSSF has been crucial for the Sigstore project. It has allowed us to not only foster community growth, neutral governance, and engagement with the broader OSS ecosystem, but also given us the ability to coordinate with a myriad of in-house initiatives — like the securing software repos working group — to further our mission of software signing for everybody. As Sigstore continues to grow and become a core technology for software supply chain security, we believe that the OpenSSF is a great place to provide a stable, reliable, and mature service for the public benefit.”
— Santiago Torres-Arias, Assistant Professor at Purdue University and Sigstore TSC Chair Member 

2021-2022 – Security with OpenSSF Scorecard & Criticality Score:
Innovative tools were introduced to automate and simplify assessing open source project security risks.

“The OpenSSF has been instrumental in transforming how the industry approaches open source security, particularly through initiatives like the Security Scorecard and Sigstore, which have improved software supply chain security for millions of developers. As we look ahead, AWS is committed to supporting OpenSSF’s mission of making open source software more secure by default, and we’re excited to help developers all over the world drive security innovation in their applications.” — Mark Ryland, Director, Amazon Security at AWS

2022 – Launch of Alpha-Omega:

Alpha-Omega (AO), an associated project of the OpenSSF launched in February 2022, is funded by Microsoft, Google, Amazon, and Citi. Its mission is to enhance the security of critical open source software by enabling sustainable improvements and ensuring vulnerabilities are identified and resolved quickly. Since its inception, the Alpha-Omega Fund has invested $14 million in open source security, supporting a range of projects including LLVM, Java, PHP, Jenkins, Airflow, OpenSSL, AI libraries, Homebrew, FreeBSD, Node.js, jQuery, RubyGems, and the Linux Kernel. It has also provided funding to key foundations and ecosystems such as the Apache Software Foundation (ASF), Eclipse Foundation, OpenJS Foundation, Python Foundation, and Rust Foundation.

2023 – SLSA v1.0 (Supply-chain Levels for Software Artifacts):
Setting clear and actionable standards for build integrity and provenance, SLSA was a turning point for software supply chain security and became essential in reducing vulnerabilities.
At the same time, community-driven tools like GUAC (Graph for Understanding Artifact Composition) built on SLSA’s principles, unlocking deep visibility into software metadata, making it more usable, actionable and connecting the dots across provenance, SBOMs and in-toto security attestations.

“Projects like GUAC demonstrate how open source innovation can make software security both scalable and practical. Kusari is proud to have played a role in these milestones, helping to strengthen the resiliency of the open source software ecosystem.”

— Michael Lieberman, CTO and Co-founder at Kusari and Governing Board member

2024 – Principles for Package Repository Security:

Offering a voluntary, community-driven security maturity model to strengthen the resilience of software ecosystems.

“Developers around the world rely daily on package repositories for secure distribution of open source software. It’s critical that we listen to the maintainers of these systems and provide support in a way that works for them. We were happy to work with these maintainers to develop the Principles for Package Repository Security, to help them put together security roadmaps and provide a reference in funding requests.” — Zach Steindler, co-chair of Securing Software Repositories Working Group, Principal Engineer, GitHub

2025

OSPS Baseline:
This initiative brought tiered security requirements into the AI space, quickly adopted by groundbreaking projects such as GUAC, OpenTelemetry, and bomctl.

“The Open Source Project Security Baseline was born from real use cases, with projects needing robust standardized guidance around how to best secure their development processes. OpenSSF has not only been the best topical location for contributors from around the world to gather — the foundation has gone above and beyond by providing project support to extend the content, promote the concept, and elevate Baseline from a simple control catalog into a robust community and ecosystem.” — Eddie Knight, OSPO Lead, Sonatype

AI/ML Security Working Group: 

The MLSecOps White Paper from the AI/ML Security Working Group marks a major step in securing machine learning pipelines and guiding the future of trustworthy AI.

“The AI/ML working group tackles problems at the confluence of security and AI. While the AI world is moving at a breakneck pace, the security problems that we are tackling in the traditional software world are also relevant. Given that AI can increase the impact of a security vulnerability, we need to handle them with determination. The working group has worked on securing LLM generating code, model signing and a new white paper for MLSecOps, among many other interesting things.” — Mihai Maruseac, co-chair of AI/ML Security Working Group, Staff Software Engineer, Google

🌐 Growing Community & Policy Impact

OpenSSF’s role rapidly expanded beyond tooling, becoming influential in global policy dialogues, including advising the White House on software security and contributing to critical policy conversations such as the EU’s Cyber Resilience Act (CRA).

OpenSSF also continues to invest in community-building and education initiatives. This year, the Foundation launched its inaugural Summer Mentorship Program, welcoming its first cohort of mentees working directly with technical project leads to gain hands-on experience in open source security.

The Foundation also supported the publication of the Compiler Options Hardening Guide for C and C++, originally contributed by Ericsson, to help developers and toolchains apply secure-by-default compilation practices—especially critical in memory-unsafe languages.

In addition, OpenSSF has contributed to improving vulnerability disclosure practices across the ecosystem, offering guidance and tools that support maintainers in navigating CVEs, responsible disclosure, and downstream communication.

“The OpenSSF is uniquely positioned to advise on considerations, technical elements, and community impact public policy decisions have not only on open source, but also on the complex reality of implementing cybersecurity to a diverse and global technical sector. In the past 5 years, OpenSSF has been building a community of well-informed open source security experts that can advise regulations but also challenge and adapt security frameworks, law, and regulation to support open source projects in raising their security posture through transparency and open collaboration; hallmarks of open source culture.” — Emily Fox, Portfolio Security Architect, Red Hat

✨ Voices from Our Community: Reflections & Hopes

Key community members, from long-standing contributors to new voices, have shaped OpenSSF’s journey:

OG Voices:

“Microsoft joined OpenSSF as a founding member, committed to advancing secure open source development. Over the past five years, OpenSSF has driven industry collaboration on security through initiatives like Alpha-Omega, SLSA, Scorecard, Secure Software Development training, and global policy efforts such as the Cyber Resilience Act. Together, we’ve improved memory safety, supply chain integrity, and secure-by-design practices, demonstrating that collaboration is key to security. We look forward to many more security advancements as we continue our partnership.” — Mark Russinovich, CTO, Deputy CISO, and Technical Fellow, Microsoft Azure

OpenSSF Leadership Perspective: 

“OpenSSF’s strength comes from the people behind it—builders, advocates, and champions from around the world working toward a safer open source future. This milestone isn’t just a celebration of what we’ve accomplished, but of the community we’ve built together.” — Adrianne Marcum, Chief of Staff, OpenSSF

Community Perspectives:

“After 5 years of hard work, the OpenSSF stands as a global force for securing the critical open-source that we all use. Here’s to five years of uniting communities, hardening the software supply chain, and driving a safer digital future.” Tracy Ragan, CEO, DeployHub

“I found OpenSSF through my own curiosity, not by invitation, and I stayed because of the warmth, support, and shared mission I discovered. From contributing to the BEAR Working Group to receiving real backing for opportunities, the community consistently shows up for its members. It’s more than a project; it’s a space where people are supported, valued, and empowered to grow.” Ijeoma Onwuka, Independent Contributor

🔮 Looking Forward

As we celebrate our fifth anniversary, OpenSSF is preparing for a future increasingly influenced by AI-driven tools and global collaboration. Community members across the globe envision greater adoption of secure AI practices, expanded policy influence, and deeper, inclusive international partnerships.

“As we celebrate OpenSSF’s 5th Anniversary, I’m energized by how our vision has grown into a thriving global movement of developers, maintainers, security researchers, and organizations all united by our shared mission. Looking ahead we’re hoping to cultivate our community’s knowledge and empower growth through stronger collaboration and more inclusive pathways for contributors.” – Stacey Potter, Community Manager, OpenSSF

📣 Join the Celebration

We invite you to share your memories, contribute your voice, and become part of the next chapter in securing open source software.

Here’s to many more years ahead! 🎉

Jun 25
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An Introduction to the OpenSSF Model Signing (OMS) Specification: Model Signing for Secure and Trusted AI Supply Chains

By Blog, Guest Blog

By Mihai Maruseac (Google), Eoin Wickens (HiddenLayer), Daniel Major (NVIDIA), Martin Sablotny (NVIDIA)

As AI adoption continues to accelerate, so does the need to secure the AI supply chain. Organizations want to be able to verify that the models they build, deploy, or consume are authentic, untampered, and compliant with internal policies and external regulations. From tampered models to poisoned datasets, the risks facing production AI systems are growing — and the industry is responding.

In collaboration with industry partners, the Open Source Security Foundation (OpenSSF)’s AI/ML Working Group recently delivered a model signing solution. Today, we are formalizing the signature format as OpenSSF Model Signing (OMS): a flexible and implementation-agnostic standard for model signing, purpose-built for the unique requirements of AI workflows.

What is Model Signing

Model signing is a cryptographic process that creates a verifiable record of the origin and integrity of machine learning models.  Recipients can verify that a model was published by the expected source, and has not subsequently been tampered with.  

Signing AI artifacts is an essential step in building trust and accountability across the AI supply chain.  For projects that depend on open source foundational models, project teams can verify the models they are building upon are the ones they trust.  Organizations can trace the integrity of models — whether models are developed in-house, shared between teams, or deployed into production.  

Key stakeholders that benefit from model signing:

  • End users gain confidence that the models they are running are legitimate and unmodified.
  • Compliance and governance teams benefit from traceable metadata that supports audits and regulatory reporting.
  • Developers and MLOps teams are equipped to trace issues, improve incident response, and ensure reproducibility across experiments and deployments.

How does Model Signing Work

Model signing uses cryptographic keys to ensure the integrity and authenticity of an AI model. A signing program uses a private key to generate a digital signature for the model. This signature can then be verified by anyone using the corresponding public key. These keys can be generated a-priori, obtained from signing certificates, or generated transparently during the Sigstore signing flow.If verification succeeds, the model is confirmed as untampered and authentic; if it fails, the model may have been altered or is untrusted.

Figure 1:  Model Signing Diagram

How Does OMS Work

OMS Signature Format

OMS is designed to handle the complexity of modern AI systems, supporting any type of model format and models of any size. Instead of treating each file independently, OMS uses a detached OMS Signature Format that can represent multiple related artifacts—such as model weights, configuration files, tokenizers, and datasets—in a single, verifiable unit.

The OMS Signature Format includes: 

  • A list of all files in the bundle, each referenced by its cryptographic hash (e.g., SHA256)
  • An optional annotations section for custom, domain-specific fields (future support coming)
  • A digital signature that covers the entire manifest, ensuring tamper-evidence

The OMS Signature File follows the Sigstore Bundle Format, ensuring maximum compatibility with existing Sigstore (a graduated OpenSSF project) ecosystem tooling.  This detached format allows verification without modifying or repackaging the original content, making it easier to integrate into existing workflows and distribution systems.

OMS is PKI-agnostic, supporting a wide range of signing options, including:

  • Private or enterprise PKI systems
  • Self-signed certificates
  • Bare keys
  • Keyless signing with public or private Sigstore instances 

This flexibility enables organizations to adopt OMS without changing their existing key management or trust models.

Figure 1. OMS Signature Format

Signing and Verifying with OMS

As reference implementations to speed adoption, OMS offers both a command-line interface (CLI) for lightweight operational use and a Python library for deep integration into CI/CD pipelines, automated publishing flows, and model hubs. Other library integrations are planned.

Signing and Verifying with Sigstore

Shell
# install model-signing package
$ pip install model-signing

# signing the model with Sigstore
$ model_signing sign <MODEL_PATH>

# verification if the model is signed with Sigstore
$ model_signing verify \
  <MODEL_PATH> \
  --signature <OMS_SIG_FILE> \
  --identity "<IDENTITY>" \
  --identity_provider "<OIDC_PROVIDER>"

 

Signing and Verifying with PKI Certificates

Shell
# install model-signing package
$ pip install model-signing

# signing the model with a PKI certificate
$ model_signing sign  \
  --certificate_chain  \
  --private_key 

# verification if the model is signed with a PKI certificate
$ model_signing verify \
 <MODEL_PATH> \
  --signature <OMS_SIG_FILE> \
  --certificate_chain <ROOT_CERT>


 

Other examples, including signing using PKCS#11, can be found in the model-signing documentation.

This design enables better interoperability across tools and vendors, reduces manual steps in model validation, and helps establish a consistent trust foundation across the AI lifecycle.

Looking Ahead

The release of OMS marks a major step forward in securing the AI supply chain. By enabling organizations to verify the integrity, provenance, and trustworthiness of machine learning artifacts, OMS lays the foundation for safer, more transparent AI development and deployment.

Backed by broad industry collaboration and designed with real-world workflows in mind, OMS is ready for adoption today. Whether integrating model signing into CI/CD pipelines, enforcing provenance policies, or distributing models at scale, OMS provides the tools and flexibility to meet enterprise needs.

This is just the first step towards a future of secure AI supply chains. The OpenSSF AI/ML Working Group is engaging with the Coalition for Secure AI to incorporate other AI metadata into the OMS Signature Format, such as embedding rich metadata such as training data sources, model version, hardware used, and compliance attributes.  

To get started, explore the OMS specification, try the CLI and library, and join the OpenSSF AI/ML Working Group to help shape the future of trusted AI.

Special thanks to the contributors driving this effort forward, including Laurent Simon, Rich Harang, and the many others at Google, HiddenLayer, NVIDIA, Red Hat, Intel, Meta, IBM, Microsoft, and in the Sigstore, Coalition for Secure AI, and OpenSSF communities.

Mihai Maruseac is a member of the Google Open Source Security Team (GOSST), working on Supply Chain Security for ML. He is a co-lead on a Secure AI Framework (SAIF) workstream from Google. Under OpenSSF, Mihai chairs the AI/ML working group and the model signing project. Mihai is also a GUAC maintainer. Before joining GOSST, Mihai created the TensorFlow Security team and prior to Google, he worked on adding Differential Privacy to Machine Learning algorithms. Mihai has a PhD in Differential Privacy from UMass Boston.

Eoin Wickens, Director of Threat Intelligence at HiddenLayer, specializes in AI security, threat research, and malware reverse engineering. He has authored numerous articles on AI security, co-authored a book on cyber threat intelligence, and spoken at conferences such as SANS AI Cybersecurity Summit, BSides SF, LABSCON, and 44CON, and delivered the 2024 ACM SCORED opening keynote.

Daniel Major is a Distinguished Security Architect at NVIDIA, where he provides security leadership in areas such as code signing, device PKI, ML deployments and mobile operating systems. Previously, as Principal Security Architect at BlackBerry, he played a key role in leading the mobile phone division’s transition from BlackBerry 10 OS to Android. When not working, Daniel can be found planning his next travel adventure.

Martin Sablotny is a security architect for AI/ML at NVIDIA working on identifying existing gaps in AI security and researching solutions. He received his Ph.D. in computing science from the University of Glasgow in 2023. Before joining NVIDIA, he worked as a security researcher in the German military and conducted research in using AI for security at Google.

Mar 11
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OpenSSF Hosts 2025 Policy Summit in Washington, D.C. to Tackle Open Source Security Challenges

By Blog, Global Cyber Policy, Press Release

WASHINGTON, D.C. – March 11, 2025 – The Open Source Security Foundation (OpenSSF) successfully hosted its 2025 Policy Summit in Washington, D.C., on Tuesday, March 4. The summit brought together industry leaders and open source security experts to address key challenges in securing the software supply chain, with a focus on fostering harmonization for open source software (OSS) development and consumption in critical infrastructure sectors.

The event featured keynotes from OpenSSF leadership and industry experts, along with panel discussions and breakout sessions covering the latest policy developments, security frameworks, and industry best practices for open source software security. 

“The OpenSSF is committed to tackling the most pressing security challenges facing the consumption of open source software in critical infrastructure and beyond,” said Steve Fernandez, General Manager, OpenSSF. “Our recent Policy Summit highlighted the shared responsibility, common goals, and interest in strengthening the resilience of the open source ecosystem by bringing together the open source community, government, and industry leaders.” 

Key Themes and Discussions from the Summit

  1. AI, Open Source, and Security
  • AI security remains an emerging challenge: Unlike traditional software, AI has yet to experience a major security crisis akin to Heartbleed, leading to slower regulatory responses.
  • Avoid premature regulation: Experts advised policymakers to allow industry-led security improvements before introducing regulation.
  • Security guidance for AI developers: There is an increasing need for dedicated security frameworks for AI systems, akin to SLSA (Supply Chain Levels for Software Artifacts) in traditional software.
  1. Software Supply Chain Security and OSS Consumption
  • Balancing software repository governance: The summit explored whether package repositories should actively limit the use of outdated or vulnerable software, recognizing both the risks and ethical concerns of software curation.
  • Improving package security transparency: Participants discussed ways to provide better lifecycle risk information to software consumers and whether a standardized framework for package deprecation and security backports should be introduced.
  • Policy recommendations for secure OSS consumption: OpenSSF emphasized the need for cross-sector collaboration to align software security policies with global regulatory frameworks, such as the EU Cyber Resilience Act (CRA) and U.S. federal cybersecurity initiatives.

“The OpenSSF Policy Summit reaffirmed the importance of industry-led security initiatives,” said Jim Zemlin, Executive Director of the Linux Foundation. “By bringing together experts from across industries and open source communities, we are ensuring that open source security remains a collaborative effort, shaping development practices that drive both innovation and security.”

Following the summit, OpenSSF will continue to refine security guidance, best practices, and policy recommendations to enhance the security of open source software globally. The discussions from this event will inform ongoing initiatives, including the OSS Security Baseline, software repository security principles, and AI security frameworks.

For more information on OpenSSF’s policy initiatives and how to get involved, visit openssf.org.

Supporting Quotes

“The 2025 Policy Summit was an amazing day of mind share and collaboration across different teams, from security, to DevOps, and policy makers. By uniting these critical voices, the day resulted in meaningful progress toward a more secure and resilient software supply chain that supports innovation across IT Teams.” – Tracy Ragan, CEO and Co-Founder DeployHub

“I was pleased to join the Linux Foundation OpenSSF Policy Summit “Secure by Design” panel and share insights on improving the open source ecosystem via IBM’s history of creating secure technology solutions for our clients,” said Jamie Thomas, General Manager, Technology Lifecycle Services & IBM Enterprise Security Executive. “Open source has become an essential driver of innovation for artificial intelligence, hybrid cloud and quantum computing technologies, and we are pleased to see more regulators recognizing that the global open source community has become an essential digital public good.” – Jamie Thomas, General Manager, Technology Lifecycle Services & IBM Enterprise Security Executive

“I was delighted to join this year’s OpenSSF Summit on behalf of JFrog as I believe strongly in the critical role public/private partnerships and collaboration plays in securing the future of open source innovation. Building trust in open source software requires a dedicated focus on security and software maturity. Teams must be equipped with tools to understand and vet open source packages, ensuring we address potential vulnerabilities while recognizing the need for ongoing updates. As the value of open source grows, securing proper funding for these efforts becomes essential to mitigate risks effectively.” – Paul Davis, U.S. Field CISO, JFrog

“Great event. I really enjoyed the discussions and the idea exchange between speakers, panelists and the audience.  I especially liked the afternoon breakout discussion on AI, open source, and security.” – Bob Martin, Senior Software and Supply Chain Assurance Principal Engineer at the MITRE Corporation

“The Internet is plagued by chronic security risks, with a majority of companies relying on outdated and unsupported open source software, putting consumer privacy and national security at risk. As explored at the OpenSSF Policy Summit, we are at an inflection point for open source security and sustainability, and it’s time to prioritize and invest in the open source projects that underpin our digital public infrastructure.” – Robin Bender Ginn, Executive Director, OpenJS Foundation

“It is always a privilege to speak at the OpenSSF Policy Summit in D.C. and converse with some of the brightest minds in security, government, and open source. The discussions we had about the evolving threat landscape, software supply chain security, and the policies needed to protect critical infrastructure were timely and essential. As the open source ecosystem expands with skyrocketing open source AI adoption, it’s vital that we work collaboratively across sectors to ensure the tools and frameworks developers rely on are secure and resilient. I look forward to continuing these important conversations and furthering our collective mission of keeping open source safe and secure.” – Brian Fox, CTO and Co-Founder, Sonatype

“The OpenSSF Policy Summit highlighted the critical intersection of policy, technical innovation, and collaborative security efforts needed to protect our software supply chains and address emerging AI security challenges. By bringing together policy makers and technical practitioners, we’re collectively building a more resilient open source ecosystem that benefits everyone, we look forward to future events and opportunities to collaborate with the OpenSSF to help strengthen this ecosystem.” – Jim Miller, Engineering Director of Blockchain and Cryptography, Trail of Bits

***

About the OpenSSF

The Open Source Security Foundation (OpenSSF) is a cross-industry initiative by the Linux Foundation that brings together the industry’s most important open source security initiatives and the individuals and companies that support them. The OpenSSF is committed to collaboration and working both upstream and with existing communities to advance open source security for all. For more information, please visit us at openssf.org.

Media Contact
Noah Lehman
The Linux Foundation
nlehman@linuxfoundation.org