Securing the AI software supply chain: Security results a…

Learn how The GitHub Secure Open Source Fund helped 67 critical AI‑stack projects accelerate fixes, strengthen ecosystems, and advance open source resilience. The post Securing the AI software supply chain: Security results across 67 open source projects appe…

Learn how The GitHub Secure Open Source Fund helped 67 critical AI‑stack projects accelerate fixes, strengthen ecosystems, and advance open source resilience. Modern software is built on open source projects. In fact, you can trace almost any production system today, including AI, mobile, cloud, and embedded workloads, back to open source components. These components are the invisible infrastructure of software: the download that always works, the library you never question, the build step you haven’t thought about in years, if ever.

When these projects are secure, teams can adopt automation, AI‑enhanced tooling, and faster release cycles without adding risk or slow down development. When they aren’t, the blast radius crosses project boundaries, propagating through registries, clouds, transitive dependencies, and production systems, including AI systems, that react far faster than traditional workflows. Securing this layer is not only about preventing incidents; it’s about giving developers confidence that the systems they depend on—whether for model training, CI/CD, or core runtime behavior—are operating on hardened, trustworthy foundations.

Open source is shared industrial infrastructure that deserves real investment and measurable outcomes. That is the mission of the GitHub Secure Open Source Fund: to secure open source projects that underpin the digital supply chain, catalyze innovation, and are critical to the modern AI stack.  We do this by directly linking funding to verified security outcomes and by giving maintainers resources, hands‑on security training, and a security community where they can raise their highest‑risk concerns and get expert feedback.  A single production service can depend on hundreds or even thousands of transitive dependencies.

As Log4Shell demonstrated, when one widely used project is compromised, the impact is rarely confined to a single application or company. This security work benefits everyone who writes, ships, or operates code, even if they never interact directly with the projects involved. That gap is exactly what the GitHub Secure Open Source Fund was built to close. In Session 1 & 2, 71 projects made significant security improvements.

In Session 3, 67 open source projects delivered concrete security improvements to reduce systemic risk across the software supply chain. Each session is a three-week sprint and engagement for a total of 12 months. Funding and participation are tied directly to outcome‑driven goals and verified security improvements. The sprint is designed and curated by the GitHub Security Lab, and delivered by security experts from GitHub and our partners.

The training is structured into different focus areas per week.  Throughout this program, each project receives $10,000 USD via GitHub Sponsors (which breaks down to $6,000 USD during the sprint and $2,000 USD at 6- and 12-month security check-ins). Projects are invited to a new security-focused community and office hours with the GitHub Security Lab, which they can take advantage of during the full 12 months.

They also receive security resources to immediately implement in their project and Azure credits for cloud infrastructure. Session 3 focused on improving security across the systems developers rely on every day. The projects below are grouped by the role they play in the software ecosystem. These projects define how software is written and executed. Improvements here flow downstream to entire ecosystems.

This group includes CPython, Node.js, LLVM, Rustls, and related tooling that shapes compilation, execution, and cryptography at scale. For example, improvements to CPython directly benefit millions of developers who rely on Python for application development, automation, and AI workloads. LLVM maintainers identified security improvements that complement existing investments and reduce risk across toolchains used throughout the industry.

When language runtimes improve their security posture, everything built on top of them inherits that resilience. These projects form the connective tissue of the internet. They handle HTTP, TLS, APIs, and network communication that nearly every application depends on. This group includes curl, urllib3, Netty, Apache APISIX, quic-go, and related libraries that sit on the hot path of modern software.

Compromising build tooling compromises the entire supply chain. These projects influence how software is built, tested, packaged, and shipped. Session 3 included projects such as Jenkins, Apache Airflow, GoReleaser, PyPI Warehouse, webpack, and related automation and release infrastructure. Maintainers in this category focused on securing workflows that often run with elevated privileges and broad access.

Improvements here help prevent tampering before software ever reaches users. ACI.dev • ArviZ • CocoIndex • OpenBB Platform • OpenMetadata • OpenSearch • pandas • PyMC • SciPy • TraceRoot These projects sit at the core of modern data analysis, research, and AI development. They are increasingly embedded in production systems as well as research pipelines. Projects such as pandas, SciPy, PyMC, ArviZ, and OpenSearch participated in Session 3.

Maintainers expanded security coverage across large and complex codebases, often moving from limited scanning to continuous checks on every commit and release. Many of these projects also engaged deeply with AI-related security topics, reflecting their growing role in AI workflows. AssertJ • ArduPilot • AsyncAPI Initiative • Bevy • calibre • DIGIT • fabric.js • ImageMagick • jQuery • jsoup • Mastodon • Mermaid • Mockoon • p5.js • python-benedict • React Starter Kit • Selenium • Sphinx• Spyder • ssh_config• Thunderbird for Android • Two.js • xyflow • Yii framework These projects shape the day-to-day experience of writing, testing, and maintaining software.

The group includes tools such as Selenium, Sphinx, ImageMagick, calibre, Spyder, and other widely used utilities that appear throughout development and testing environments. Improving security here reduces the risk that developer tooling becomes an unexpected attack vector, especially in automated or shared environments. external-secrets • Helmet.js • Keycloak • Keyshade • Oauth2 (Ruby) • varlock • WebAuthn (Go) These projects form the backbone of authentication, authorization, secrets management, and secure configuration.

Session 3 participants included projects such as Keycloak, external-secrets, oauth2 libraries, WebAuthn tooling, and related security frameworks. Maintainers in this group often reported shifting from reactive fixes to systematic threat modeling and long-term security planning, improving trust for every system that depends on them. Across all categories, one signal stood out. AI-related security modules produced the largest self-reported increase in security understanding of any topic in Session 3.

While AI security is not solved, it is being actively shaped by the open source community in the open. Maintainers moved security from a stretch goal to a core requirement. They shifted from reactive patching to proactive design, and from isolated work to shared practice. Many are now publishing playbooks, sharing incident response exercises, and passing lessons on to their contributor communities.

Securing open source is basic maintenance for the internet. By giving 67 heavily used projects real funding, three focused weeks, and direct help, we watched maintainers ship fixes that now protect millions of builds a day. This training, taught by the GitHub Security Lab and top cybersecurity experts, allows us to go beyond one-on-one education and enable one-to-many impact.  For example, many maintainers are working to make their playbooks public.

The incident-response plans they rehearsed are forkable. The signed releases they now ship flow downstream to every package manager and CI pipeline that depends on them. We couldn’t do this without our incredible network of partners. Together, we are helping secure the open source ecosystem for everyone!  Funding Partners: Alfred P. Sloan Foundation, American Express, Chainguard, Datadog, Herodevs, Kraken, Mayfield, Microsoft, Shopify, Stripe, Superbloom, Vercel, Zerodha, 1Password Ecosystem Partners: Atlantic Council, Ecosyste.ms, CURIOSS, Digital Data Design Institute Lab for Innovation Science, Digital Infrastructure Insights Fund, Microsoft for Startups, Mozilla, OpenForum Europe, Open Source Collective, OpenUK, Open Technology Fund, OpenSSF, Open Source Initiative, OpenJS Foundation, University of California, OWASP, Santa Cruz OSPO, Sovereign Tech Agency, SustainOSS Learn how the GitHub Secure Open Source Fund helped 71 open source projects significantly improve their security posture through direct funding, expert guidance, and actionable playbooks.

Let’s dig into the 2025’s open source data on GitHub to see what we can learn about the future. Open source is hitting an “Eternal September.” As contribution friction drops, maintainers are adapting with new trust signals, triage approaches, and community-led solutions. Learn how we are using the newly released GitHub Security Lab Taskflow Agent to triage categories of vulnerabilities in GitHub Actions and JavaScript projects.

Build what’s next on GitHub, the place for anyone from anywhere to build anything. Catch up on the GitHub podcast, a show dedicated to the topics, trends, stories and culture in and around the open source developer community on GitHub. Discover tips, technical guides, and best practices in our biweekly newsletter just for devs.

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