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Open Source Security

Apr 15
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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.

Apr 03
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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.

Apr 02
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From AIxCC to OpenSSF: Welcoming OSS-CRS to Advance AI Driven Open Source Security

By Blog

By Jeff Diecks

Artificial intelligence is changing how we approach software security. Open source is at the center of that shift.

Over the past year, DARPA’s Artificial Intelligence Cyber Challenge (AIxCC) showed that cyber reasoning systems (CRS) can go beyond finding vulnerabilities. These systems can analyze code, confirm issues, and generate patches. This brings us closer to a future where security is more automated and scalable.

When the competition ended, one question remained. How do we take these breakthroughs and make them usable in the real world?

Today, we are taking an important step forward.

The Open Source Security Foundation (OpenSSF) is welcoming OSS-CRS as a new open source project under the AI / ML Security Working Group.

OSS-CRS emerged from AIxCC and is a standard orchestration framework for building and running LLM-based autonomous bug-finding and bug-fixing systems.

The open framework is designed to make CRS practical outside of the AIxCC environment. During the competition, teams built powerful systems that were released as open source. However, many of them depended on the competition infrastructure, which made them difficult to reuse or extend. OSS-CRS addresses that gap.

OSS-CRS Features include:

  • Standard CRS Interface: OSS-CRS defines a unified interface for CRS development. Build your CRS once following the development guide, and run it across different environments (local, Azure, …) without any modification.
  • Effortless Targeting: Run any CRS against projects in OSS-Fuzz format. If your project is compatible with OSS-Fuzz, OSS-CRS can orchestrate CRSs against it out of the box.
  • Ensemble Multiple CRSs: Compose and run multiple CRSs together in a single campaign to combine their strengths and maximize bug-finding and bug-fixing coverage.
  • Resource Control: Manage CPU limits and LLM budgets per CRS to keep costs and resources in check.

Read the OSS-CRS research paper: https://doi.org/10.48550/arXiv.2603.08566

From Competition to Community

The move of OSS-CRS into OpenSSF marks a clear transition from research and competition to open collaboration and long term development.

OpenSSF provides a neutral home where projects like OSS-CRS can grow. Contributors can work together to improve the tools, validate results, and support adoption across the ecosystem.

OSS-CRS is already producing real results. 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.

OpenSSF will continue to support this important work by providing 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.

Recent research from the OSS-CRS team validates the necessity of having a human in the loop. The team manually reviewed a set of 630 AI-generated patches and found 20-40% of the patches to be semantically incorrect. The incorrect patches pass all automated validation but are actually wrong — a dangerous failure mode only catchable by manual review.

A key benefit of the OSS-CRS project is its Ensemble feature. The Ensemble feature enhances accuracy and reliability by combining patches from multiple CRS approaches and using a selection process to pick the one most likely to be correct. The research showed this approach consistently matches or outperforms the best single component in improving semantic correctness, which is hard to eliminate at the single-agent level. This collaboration of systems helps produce more robust results for open source defenders.

Get Involved

With projects like OSS-CRS, OpenSSF will continue to support AI-driven security work to help turn innovation into practical outcomes for open source.

We offer several options to get involved including:

Author Bio

Jeff Diecks is a Senior Technical Program Manager at The Linux Foundation. He has more than two decades of experience in technology and communications with a diverse background in operations, project management and executive leadership. A participant in open source since 1999, he’s delivered digital products and applications for universities, sports leagues, state governments, global media companies and non-profits.

Mar 24
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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.

Mar 23
Love0

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.

Mar 23
Love0

OpenSSF Celebrates New Members, No-Cost Tooling, and Project Milestones

By Blog, Press Release

Foundation welcomes Helvethink, Spectro Cloud, Quantrexion as members, offers Kusari Inspector for free to projects, and celebrates increased investment in AI security 

AMSTERDAM – Open Source SecurityCon Europe – March 23, 2026 – The Open Source Security Foundation (OpenSSF), a cross-industry initiative of the Linux Foundation that focuses on sustainably securing open source software (OSS), today announced new members and key project momentum during Open Source SecurityCon Europe. 

New OpenSSF members include Helvethink, Spectro Cloud, and Quantrexion, who join the Foundation as General Members. As members, these companies will engage with working groups, contribute to technical initiatives, and help guide the strategic direction of the OpenSSF. Together, members support open, transparent, and community-driven security innovation, and the long-term sustainability of the Foundation.

“Open source security continues to evolve significantly in the face of new, automated threats,” said Steve Fernandez, General Manager of OpenSSF. “Our member organizations are seeding a more secure future, built with longevity in mind, by working with the OpenSSF. This network of projects, maintainers, and thousands of contributors is key to reinforcing reliable, sustainable open source software for all.”

Foundation Updates and Milestones

In the past quarter, OpenSSF has furthered its mission to secure open source software with the following achievements:

  • A new partnership with Kusari to offer Kusari Inspector at no cost to OpenSSF projects – this offering provides maintainers with deeper visibility into their software supply chains and enables proactive security checks at the pull request level.
  • The SLSA (Supply-chain Levels for Software Artifacts) project achieved Graduated status – this recognition advances SLSA’s stability, maturity, and broad adoption as a critical framework for supply chain integrity.
  • The release of the Gemara Project’s inaugural white paper – the findings outline a new framework for integrating security-as-code principles directly into the software development lifecycle.
  • The launch of new Special Interest Groups focused on Model Lifecycle Provenance and GPU-Based Model Integrity – these groups, under the AI/ML Security Working Group, expand the Foundation’s focus on securing the rapidly evolving field of AI/ML software security.
  • OpenSSF is approved as a CEN / CENELEC Liaison Organization for cybersecurity – this designation, through the Linux Foundation Europe, strengthens OpenSSF’s position in global standards development and policy influence.
  • The official launch of the OpenSSF Ambassador Program – applications are now open for the initial cohort.
  • Over 7,300 learners enrolled in OpenSSF’s free course, “Understanding the EU Cyber Resilience Act (LFEL1001)” – the Foundation has had over 75,000 enrollments in OpenSSF training programs to date.

OpenSSF growth follows the announcement of $12.5 million in grant funding awarded to OpenSSF and Alpha-Omega from leading AI providers. Funding from these leaders underscores broad industry support for more sustainable AI security assistance that empowers maintainers. Learn more about how OpenSSF and Alpha-Omega are using this grant to build long-term, sustainable security solutions, here. 

Supporting Quotes

“At Helvethink, we work at the intersection of cloud architecture, platform engineering, and DevSecOps. Open source components are foundational to modern infrastructure from Kubernetes and IaC tooling to CI/CD pipelines and security automation. Strengthening this ecosystem requires measurable standards, robust software supply chain security practices, and active collaboration across the community. By joining OpenSSF, we are actively participating in several working groups to contribute to initiatives focused on supply chain integrity, secure-by-design principles, and the continuous improvement of cloud-native security practices.”

– José Goncalves, co-founder, Helvethink

“Quantrexion is proud to join OpenSSF and support its mission to strengthen the security, resilience, and trustworthiness of open source software. As a company focused on governance and human risk management, we see secure open ecosystems as a critical part of long-term digital resilience.”

– Dionysis Karamitopoulos, CEO, Quantrexion

“Open source is the foundation of modern infrastructure — and its security is a shared responsibility. By joining the OpenSSF, Spectro Cloud is investing directly in the community work that raises the bar for everyone. Just as importantly, it strengthens the standards and practices behind the software we ship, so our customers can deploy Kubernetes with confidence in the integrity of every component. We’re proud to support the OpenSSF mission and to keep translating that momentum into real product capabilities that make secure software a default, not a bolt-on.”

– Saad Malik, CTO and co-founder, Spectro Cloud

Events and Gatherings

OpenSSF members are gathering this week in Amsterdam at Open Source SecurityCon Europe. To get involved with the OpenSSF community, join us at the following upcoming events:

Additional Resources

About the OpenSSF

The Open Source Security Foundation (OpenSSF) is a cross-industry organization at 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
Grace Lucier
The Linux Foundation

pr@linuxfoundation.org  

Mar 17
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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.

Mar 17
Love0

Linux Foundation Announces $12.5 Million in Grant Funding from Leading Organizations to Advance Open Source Security 

By Blog, Press Release

Anthropic, Amazon Web Services (AWS), GitHub, Google, Google DeepMind, Microsoft, and OpenAI Join Forces with the Foundation to Invest in Sustainable Security Solutions for the Open Source Ecosystem

SAN FRANCISCO – March 17, 2026 – The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced $12.5 million in total grants from Anthropic, AWS, GitHub, Google, Google DeepMind, Microsoft, and OpenAI to strengthen the security of the open source software ecosystem. The funding will be managed by Alpha-Omega and the Open Source Security Foundation (OpenSSF), trusted security initiatives within the Linux Foundation, to develop long-term, sustainable security solutions that support open source communities worldwide.

As the security landscape grows more complex, advances in AI are dramatically increasing the speed and scale of vulnerability discovery in open source software. Maintainers are now facing an unprecedented influx of security findings, many of which are generated by automated systems, without the resources or tooling needed to triage and remediate them effectively. Through this investment, Alpha-Omega and OpenSSF will work directly with maintainers and their communities to make emerging security capabilities accessible, practical, and aligned with existing project workflows. The effort will support sustainable strategies that help maintainers manage growing security demands while improving the overall resilience of the open source ecosystem.

“Alpha-Omega was built on the idea that open source security should be both normal and achievable. By funding audits and embedding security experts directly into the ecosystem, we’ve proven that targeted investment works,” said Michael Winser, Co-Founder of Alpha-Omega. “Now, we’re scaling that expertise. We are excited to bring maintainer-centric AI security assistance to the hundreds of thousands of projects that power our world.”

“Grant funding alone is not going to help solve the problem that AI tools are causing today on open source security teams,” said Greg Kroah-Hartman of the Linux kernel project. “OpenSSF has the active resources needed to support numerous projects that will help these overworked maintainers with the triage and processing of the increased AI-generated security reports they are currently receiving.”

“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. 

Supporting Quotes

“The open source ecosystem underpins nearly every software system in the world, and its security can’t be taken for granted. This investment reflects our belief that the best way to improve security outcomes is to work directly with maintainers and give them the resources and tooling to address threats at scale. Ensuring the world safely navigates the transition to transformative AI means investing in the foundations it runs on.” 

– Vitaly Gudanets, CISO, Anthropic

“Over the past four years, our work with Alpha-Omega has proven it can deliver real results for the open source ecosystem at scale—from helping the Rust Foundation deploy Trusted Publishing to enabling critical vulnerability fixes across Node.js and PyPI. We are excited to increase our investment in Alpha-Omega and to work with our collaborators and directly with maintainers to provide not just funding, but the right tools and expertise that projects actually need to handle AI-generated security reports at scale.” 

— Mark Ryland, Director, AWS Security 

“Building on our initial commitment alongside Google and Microsoft four years ago, we’re now confronting new security challenges as AI transforms vulnerability discovery. That’s why AWS is investing an additional $2.5 million in Alpha-Omega. We believe the same advanced models creating these challenges can also solve them through better tooling and automation, but only through collaboration between industry leaders and the open source security community.” 

— Stormy Peters, Head of Open Source Strategy and Marketing, Amazon Web Services  

“As the home for open source, GitHub knows that code is only as strong as the community behind it. Supporting the Linux Foundation’s Alpha-Omega initiative extends our longstanding commitment to securing the global software supply chain. Through funding, training, and AI-powered tools, we’re empowering maintainers to identify risks faster and prevent burnout.”


— Kyle Daigle, COO, GitHub

“Securing the open source ecosystem is a shared responsibility that requires more than just capital, it also requires giving maintainers the right tools to stay ahead of evolving threats. By combining AI-driven innovation with the proven frameworks of Alpha-Omega and OpenSSF, we are empowering the community to not just react to threats, but build systemic resilience.” 


— Evan Kotsovinos, Vice President of Privacy, Safety and Security, Google

“Securing open source is a shared responsibility, and we have to move as fast as the technology does. We’re focused on turning AI’s ability to find and patch vulnerabilities into a massive defensive advantage. Supporting Alpha-Omega and OpenSSF is an important step for us, right alongside our work on OSS-Fuzz, Big Sleep and CodeMender. We’re going to keep building on this to put these capabilities into the hands of maintainers, leveraging AI to help scale society’s collective resistance to cyber attacks.” 

— Four Flynn, VP, Security and Privacy, Google DeepMind

“Open source software is a critical part of the modern technology landscape. As AI accelerates both software development and the discovery of vulnerabilities, the industry must step up to protect this shared infrastructure. This collaboration represents an important step in democratizing AI-powered defenses, and we’re proud to support Alpha-Omega and the OpenSSF in delivering scalable, maintainer-first solutions that secure the code powering our digital society.” 


— Mark Russinovich, CTO, Deputy CISO and Technical Fellow, Microsoft Azure

“This is a critical moment for global cybersecurity that requires unprecedented levels of collaboration across the industry, and sustained commitment. For artificial intelligence to benefit us all, we need to listen closely to maintainers and strengthen the open source foundations we all depend on. Maintainers make an extraordinary contribution, and this program is an important step in providing them the support they need.”

— Dane Stuckey, CISO, OpenAI

About Alpha-Omega

Alpha-Omega protects society by funding and catalyzing sustainable security across open source software. With over 70 grants totalling over $20M across major ecosystems, package registries, and individual projects, Alpha-Omega has an established track record of “turning money into security.” Backed by Anthropic, AWS, Citi, GitHub, Google, Google DeepMind, Microsoft, and OpenAI, Alpha-Omega partners with maintainers, security experts, and communities to invest where it can have the greatest impact. For more information, visit us at alpha-omega.dev.

About the OpenSSF

The Open Source Security Foundation (OpenSSF) is a cross-industry organization at 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. 

About the Linux Foundation

The Linux Foundation is the world’s leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects, including Linux, Kubernetes, Model Context Protocol (MCP), OpenChain, OpenSearch, OpenSSF, OpenStack, PyTorch, Ray, RISC-V, SPDX and Zephyr, provide the foundation for global infrastructure. The Linux Foundation is focused on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org. 

Media Contact
Grace Lucier
The Linux Foundation

pr@linuxfoundation.org

Mar 10
Love0

What’s in the SOSS? Podcast #55 – S3E7 The Gemara Project: GRC Engineering Model for Automated Risk Assessment

By Podcast

Summary

Hannah Braswell and Jenn Power, Security Engineers from Red Hat and contributors to the OpenSSF, join host Sally Cooper to discuss the Gemara project. Gemara, an acronym for GRC Engineering Model for Automated Risk Assessment, is a seven-layer logical model that aims to solve the problem of incompatibility in the GRC (Governance, Risk, and Compliance) stack. By outlining a separation of concerns, the project seeks to enable engineers to build secure and compliant systems without needing to be compliance experts. The speakers explain how Gemara grew organically to seven layers and is leveraged by other open source initiatives like the OpenSSF Security Baseline and Finos Common Cloud Controls. They also touch on the ecosystem of tools being built, including Queue schemas and a Go SDK, and how new people can get involved.

Conversation Highlights

00:00 Welcome music + promo clip
00:22 Introductions
02:17 What is Gemara and what problem does it address?
03:58 Why do we need a model for GRC engineering?
05:50 The seven-layer structure of Gemara
07:40 How Gemara connects to other open source projects
10:14 Tools available to help with Gemara model adoption
11:39 How to get involved in the Gemara projects
13:59 Rapid Fire
16:03 Closing thoughts and call to action

Transcript

Sally Cooper (00:22)
Hello, hello, and welcome to What’s in the SOSS, where we talk to amazing people that make up the open source ecosystem. These are developers, security engineers, maintainers, researchers, and all manner of contributors that help make open source secure. I’m Sally, and today I have the pleasure of being joined by two fantastic security engineers from Red Hat. We have Hannah and Jenn.

Thank you both so much for joining me today and to get us started, can you tell us a little bit about yourselves and the work that you do at Red Hat? I’ll start with Jenn.

Jenn Power (00:58)
Sure. I am Jenn Power. I’m a principal product security engineer at Red Hat. My whole life is compliance automation, let’s say that. And outside of Red Hat, I participate in the OpenSSF Orbit Working Group, and I’m also a maintainer of the Gemara project.

Sally Cooper (01:18)
Amazing. Thank you, Jenn and Hannah. How about you? Hi.

Hannah Braswell (01:21)
Hey, Sally. Thanks for the nice introduction. I’m Hannah Braswell, and I’m an associate product security engineer at Red Hat. And I work with Jenn on the same team. And I primarily focus on compliance automation and enablement for compliance analysts to actually take advantage of that automation. Then within the OpenSSF, I’m involved in the Gemara project. I’m the community manager there. And then

I’m kind of a fly on the wall at a lot of the community meetings, whether it be the Gemara meeting or the orbit working group. I like to go to a lot of them.

Sally Cooper (02:01)
we love to hear that. I heard Orbit working group from both of you. That’s exciting. And I also really want to dive in to the project Gemara. So before we do dive into those details, let’s make sure that everyone’s starting from the same place. So for listeners who are hearing about Gemara for the first time, what is Gemara and what problem is it designed to address?

Jenn Power (02:23)
Sure, can start there. It’s actually secretly an acronym. So it stands for GRC Engineering Model for Automated Risk Assessment. So that’s kind of a mouthful, so we just shorten it to Gemara. And the official description I’ll give, and then I can go into it like a little bit more of a relatable example, is that it provides a logical model for describing categories of compliance activities, how they interact,

And it has schemas to enable automated interoperability between them. So like, what does that mean? I think a good, if we anchor this in an analogy, we could call Gemara like the OSI model for the GRC stack. In fact, that was one of the kind of primary inspirations for the categorical layers of Gemara. And Gemara also happens to have seven categorical layers, just like the OSI model.

So if you think about it in networking, if I want to send an email, I don’t have to understand like packet routing. I can just send my email. So in GRC, we can’t really do that today. We have security engineers that also have to be compliance experts to be successful. And so with Gemara, we want to outline the separation of concerns within the GRC stack to make sure that like each specialist can contain their complexity in their own layer while allowing them to exchange information with different specialists completing activities in different layers.

So like if I could give one takeaway, we want to make it so engineers can build secure and compliant systems without having to understand the nuance of every single compliance framework out

Sally Cooper (04:14)
I love that. So we have a baseline now. Let’s talk about the problem and get a little bit deeper into that. So Gemara is responding to a problem that you touched upon. Why do we need a model for GRC engineering and what incompatibility issue are you trying to solve? If you could go a little deeper.

Jenn Power (04:34)
Sure. So I think sharing resources in GRC is just really hard today. Sharing content, sharing tools, none of those tools and content, it doesn’t work together today, if I could say that. engineers are typically having to reinterpret security controls. They’re having to create a lot of glue code to make sure that a tool like a GRC system and a vulnerability scanner can actually talk to each other.

So we’re trying to solve that incompatibility issue on the technical side. But this is also a human problem. And I think that’s kind of the sneakiest part about it. A lot of times, we’re not even saying the same things when we use the same terms. And so that’s another thing that we’re trying to solve within the Gemara project.

This one comes up all the time. Take the word policy. If you say that to an engineer, you’re thinking immediately, policy as code, like a rego file or something you’re going to use with your policy engine. But if you’re talking to someone in the compliance space, they’re thinking like, this is like my 40 page document that outlines my organizational objectives. So we created definitions within the Gemara project to go along with the model to solve the human problem while we’re also trying to solve the technical problem.

Sally Cooper (06:05)
That’s interesting. Okay, I heard you say something about a seven-layer structure. Can you tell me why you chose a seven-layer structure for Gemara?

Jenn Power (06:17)
So this actually stemmed from an initiative under the CNCF called the Automated Governance Maturity Model. And that started as four concepts actually, policy, evaluation, enforcement, and audit. And that established the initial kind of lexicon that the project had been using.

And it initially got some adoption in the ecosystem, specifically in projects under the Linux Foundation, like FINOS Common Cloud Controls (CCC) and the Open Source Project Security Baseline (OSPS Baseline). And through the application of that lexicon, we found that there needed to be more granularity within that policy layer. So it expanded to two new layers called guidance and controls.

And I didn’t mention that we were creating a white paper yet, but we do have a white paper. And through the creation of that white paper, which Eddie Knight did so much work to create that initial draft there, we actually found that we were missing a layer. We had a seventh layer, and it was something that we had called sensitive activities. And it’s something kind of sandwiched in the middle of the Gemara layer. And so with that, we kind of organically grew to seven layers. So that I think is the kind of origin story on how the layers got to seven.

Sally Cooper (07:54)
I love that. And you’re really talking about how Gemara is not built in isolation, that you’re working with other open source projects. For example, you mentioned Baseline and the FINOS Common Cloud Controls. Can you tell me how Gemara connects to those projects?

Hannah Braswell (08:09)
Yeah. So in terms of Gemara connecting to the other open source projects, the first thing that comes to mind is really the CRA because of how prominent it is right now and just the future of its impact. And I really think that Gemara is going to be a catalyst for open source projects in general that are in need of some kind of mechanism to, you know, implement security controls and align with potential compliance requirements.

And the good thing about Gemara is that you don’t have to be a compliance expert to make sure that your open source project is secure. And so I would say that the OSPS Baseline is a great example of Gemara’s layer two, because it provides a set of security controls that engineers can actually implement. So in that case, other projects can reuse the baseline controls and then fit them to their needs.

And I think it also goes to say that, anyone that is actually building a tool they want to sell or distribute in the European Union market that’s using the open source components, they’re gonna have to think about what’s in scope and having something like the OSPS Baseline to understand how to effectively assess your open source components and their risks is really, really valuable and just gonna be super useful. And then in terms of the FINOS Common Cloud Controls, I think that’s

Also another great example, just in terms of the use case and implementation of Gemara, because they have their core catalog, which has its own definitions of threats and controls that’s then imported to their technology specific catalogs. And yeah, so that’s a great implementation within the financial sector.

And then where we’re trying to expand the ecosystem for Gemara, as in the Cloud Native Security Controls catalog refresh. And that’s actually an initiative that Jenn is leading. I’ve done a few contributions to it, but it’s essentially an effort to take the controls catalog that currently exists as a spreadsheet and make it available as a Gemara layer one machine readable guidance document. So Gemara is really connecting to projects that are all great to have on your radar, especially with the CRA coming up.

Sally Cooper (10:26)
Wow, that sounds great. But I’m just thinking about our listeners. They’re probably wondering, like, what does this look like in practice? And I’m curious if there are any tools available to help with the Gemara model adoption.

Jenn Power (10:39)
So we’re actually working on an ecosystem of tools. So we want to bridge that theory that we’re creating within the Gemara white paper to things that are actually implementable just to make sure that you don’t have to start from scratch if you’re trying to implement the Gemara model.

So we have a couple tools within the ecosystem. One would be our implementation of the model. We’re using queue schemas to allow users to create the models like in YAML, for instance, if you wanted to create your layer two, you would create YAML, you could use our queue schemas to validate that your document is in fact a Gemara compliant document. And then we’re also building SDKs. Right now we have a Go SDK, so you can build tooling around the programmatic access and manipulation of Gemara documents. A tool in the ecosystem that’s using this currently is a tool called Privateer that automates the layer five evaluations.

Sally Cooper (11:47)
Wow, that’s great. And of course, none of this works without the people. So I know you mentioned a few of them. How can new people get involved in the Gemara project?

Hannah Braswell (11:58)
So anyone that’s new and interested in getting involved in the Gemara project, my first piece of advice would just be to jump in a community meeting and listen in on what’s happening. I know I started out just by joining those meetings and I, you know, I didn’t necessarily have much to say, but I appreciated the culture and the open discussion, just like bouncing ideas back and forth off of one another.

And there’s also plenty of times when I joined a community meeting and still trying to understand the project if there was some kind of group opinion trying to be formed. Like I think it’s perfectly fine to say, you know, I don’t have the information right now. I don’t have an opinion. I’m still trying to learn about the project. But if something piques your interests and you want to contribute, then volunteer for it or show you’re interested because people are not going to forget about your willingness to step up and be part of the community.

But I started joining those meetings before we were rolling out the white paper. So that kind of brings me back to my first piece of advice. So I’d really suggest reading the white paper first, because it describes the problem and the trajectory of the project so well, and in a really clear way that I think is super important context for anyone that wants to start contributing. And I mean, from there, I mean, I’m the community manager, but I started with small contributions.

that ended up supporting the community in terms of documentation and some other aspects of the project I was excited about and that I could contribute to. So I really think the contributions are dependent on what you’re interested in. And even if there’s some difference in opinion and perspective or background, all of that can make a huge difference for the community and anything from documentation to code or discussion and collaboration will count as valid contribution and effort. So I’d say to anyone that’s wanting to join the Gemara community and start contributing, I think you should just find an area that truly interests you and makes you excited and get involved.

Sally Cooper (14:02)
Oh, that’s great. Well, thanks so much. And before we wrap, we’re going to do rapid, rapid fire. So I hope you’re ready because this is the fun part. No overthinking, no explanations, just the first instinct, okay, that comes to you. And I’m going to bounce. Yes, exactly. I’m going to bounce back and forth and ask you both some questions. Ready?

Jenn Power (14:17)
I’ll close my eyes then.

Sally Cooper (14:25)
Okay, Hannah, you’re up first. Star Wars or Star Trek?

Hannah Braswell (14:29)
Star Wars.

Sally Cooper (14:30)
Nice, I love it.
And Jenn, same question, Star Wars or Star Trek?

Jenn Power (14:335)
Star Wars.

Sally Cooper (14:36)
Okay, we’re all friends here.
Okay, back to Hannah, coffee or tea?

Hannah Braswell (14:42)
Definitely coffee.

Sally Cooper (14:43)
Yay, cheers. That’s solid.
Jenn, morning person or night owl?

Jenn Power (14:49)
Night Owl.

Sally Cooper (14:50)
Ohh that tracks. Hannah, beach vacation or mountains?

Hannah Braswell (14:56)
Hmm beach vacation.

Sally Cooper (14:58)
Nice choice. Jenn, books or movies?

Jenn Power (15:02)
Movies.

Sally Cooper (15:03)
Nice. All right, last round. Hannah, favorite open source mascot?

Hannah Braswell (15:08)
Oh…Zarf. I think that looks like an axolotl. I used to be obsessed with axolotls. And I mean, ever since I saw that, I was like, that’s the mascot.

Sally Cooper (15:18)
I love Zarf too. Cool. Okay. That’s a really strong pick.
Jenn, I’m going to give you the same question. Favorite open source mascot?

Jenn Power (15:26)
actually love the OpenSSF goose. I think it’s so cute.

Sally Cooper (15:30)
Teehee, Honk, he’s the best. Okay, let’s bring it home, Hannah, sweet or savory.

Hannah Braswell (15:38)
Savory.

Sally Cooper (15:39)
interesting. Okay, and Jenn? Spicy or mild?

Jenn Power (15:46)
mild. I can’t handle any spice. I’m a baby.

Sally Cooper (15:51)
love it. That’s amazing. Well, thank you both so much for playing along. And as we wind things down, do you have any other calls to action for our audience if someone’s listening, and they want to learn more or get involved? What is like the best next step for them?

Jenn Power (16:05)
I would say read the white paper. We are looking for feedback on it and that is really a way to understand the philosophy and the architectural goals of Gemara. And if you’re looking to just like, hey I want to learn GRC, that’s a good first step. So I think that’s what I would say.

Sally Cooper (16:28)
Fantastic. Hannah, Jenn, thank you so much for your time today and for the work you’re doing for the open source security community. We appreciate you both. And to everyone listening, happy open sourcing and that’s a wrap.