Ecosystems.ms: Mapping Open Source's Critical Digital Infrastructure
Explore Ecosystems.ms, a platform tracking 24.5 billion open-source dependencies to secure and sustain critical digital infrastructure, revealing key insights into software usage and funding challenges.
Key Insights
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Insight
A tiny fraction (0.01%) of open-source packages underpins 80% of global usage, maintained by a disproportionately small number of individuals (~15,000 people), many of whom lack consistent funding.
Impact
This highlights critical supply chain fragility and the urgent need for targeted investment in the sustainability of foundational open-source components. Failure to address this could lead to widespread system instability and security vulnerabilities.
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Insight
Traditional "star" metrics are poor indicators of an open-source project's true health; actual dependency usage provides a more accurate and actionable signal for project stability and importance.
Impact
Organizations can re-evaluate their criteria for selecting and trusting open-source dependencies, moving towards data-driven assessments of actual adoption and maintenance, thereby improving software reliability and security.
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Insight
Ecosystems.ms provides a robust, granular, and open dataset of open-source metadata (packages, dependencies, repos) crucial for research, security, and sustainability initiatives across diverse ecosystems.
Impact
This foundational data layer enables deeper analysis for supply chain security, accelerates academic research into software ecosystems, and provides tools for better governance and resource allocation in the open-source community.
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Insight
The proliferation of AI agents and low restrictions in package managers introduce new security vulnerabilities, particularly through prompt injection in metadata, demanding robust mitigation strategies.
Impact
This necessitates the development of new security protocols and validation mechanisms for package metadata, forcing a re-evaluation of trust models within automated software development and deployment pipelines.
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Insight
Maintaining critical open-source data infrastructure is financially challenging, relying on a mixed model of grants, paid data licensing, and a potential future of revenue-sharing for analysis tools.
Impact
This calls for innovative and diversified funding models for critical open-source projects and infrastructure, moving beyond traditional donations to ensure long-term operational viability and continued development.
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Insight
The development of an "OSS Taxonomy" aims to provide a structured, multi-faceted framework for describing open-source projects, addressing current deficiencies in discovery and categorization.
Impact
Implementing a standardized taxonomy will significantly improve the discoverability of relevant open-source tools, facilitate cross-ecosystem understanding, and enable better strategic planning for software development.
Key Quotes
"If we go mining the dependency information out of open source repositories at a large scale, you actually start to get a really good picture of how people really open, like use open source and how they don't use open source."
"It's like 0.01% of packages make up 80% of usage."
"I think the change of uh interest rate across the world had a massive impact. Like you can see the nice thing about open collective is they are especially open source collective is very public. You can see the amounts of donations uh like going in and going out, and there was a big drop around the time that uh like post-COVID hit and changed all of the finances of these things was like, oh, okay, well, open source is no longer like one of the it's an easy line item to drop, right? Because everything is free and it just continues to work for now uh until a security problem comes along and then everyone starts scrambling again."
Summary
Ecosystems.ms: Unveiling the Invisible Infrastructure of Open Source
In the vast and often opaque world of open-source software, understanding its true impact and vulnerabilities has long been a challenge. How do we move beyond superficial metrics to grasp the bedrock dependencies that power our digital lives? Enter ecosystems.ms, Andrew Nesbitt's ambitious project, which offers an unprecedented look into the intricate web of open-source components.
The Journey from Libraries.io to a Data Powerhouse
Andrew Nesbitt's decade-long exploration into open-source metadata began with identifying "good projects" beyond mere popularity. His earlier work, libraries.io, established a crucial insight: actual dependency usage, rather than GitHub stars, is a far more reliable indicator of a project's health and importance. Ecosystems.ms represents a ground-up rebuild, transforming a monolithic search engine into a modular, distributed base layer for open-source metadata. It currently tracks over 12 million packages, 287 million repositories, and a staggering 24.5 billion dependencies, managed by 1.9 million maintainers.
This platform isn't just about raw numbers; it reveals a profound asymmetry in open-source utilization. A minuscule fraction — approximately 0.01% — of packages accounts for 80% of all open-source usage. These critical few, often maintained by a single individual, form the bedrock of countless applications, highlighting a significant point of vulnerability and underscoring the vital role of these unsung heroes.
Unmasking Hidden Risks and Funding Imperatives
Ecosystems.ms serves as a critical resource for researchers analyzing developer behaviors, security vulnerabilities, and license compliance across diverse package managers. Its data is pivotal for Software Bill of Materials (SBOM) enrichment, especially through integrations with tools like GitHub Actions, where systems can automatically fetch comprehensive security and license information for every component in an SBOM.
However, the sustainability of this foundational infrastructure faces significant challenges. Post-COVID economic shifts have impacted open-source funding, with donations "an easy line item to drop." While 25-50% of critical projects have some funding mechanism like GitHub Sponsors or Open Collective, corporate contributions lag behind individual support. Furthermore, the advent of AI agents and increasingly permissive package manager policies introduces new security vectors, such as prompt injection through package metadata, creating a "horrible mess" for maintaining trust and stability.
A Vision for a Sustainable and Empowered Open-Source Future
Nesbitt envisions a future where ecosystems.ms not only provides data but actively empowers maintainers and enhances overall open-source security. This includes developing "pipeline of analysis" capabilities, allowing automated security and capabilities scans on new package versions, and even exploring revenue-sharing models for maintainers of critical analysis tools.
Key to this future is enabling maintainers to understand their downstream users better — not just who depends on their software, but how (e.g., specific versions, breaking changes, successful upgrades). This "inverse CI" approach would allow maintainers to proactively test against major downstream dependencies, fostering greater coordination and reducing post-release breakage. Additionally, the nascent "OSS Taxonomy" project aims to provide a structured framework for describing open-source projects, improving discovery and addressing existing "black holes" in metadata.
Conclusion
Ecosystems.ms is more than just a data repository; it's a foundational layer aiming to bring transparency, security, and sustainability to the open-source world. By leveraging this wealth of data, the goal is to empower maintainers and organizations alike to build a more resilient and collaborative digital future, ensuring the longevity and integrity of the software we all rely upon.
Action Items
Integrate ecosystems.ms API into CI/CD pipelines for automated SBOM enrichment with security, license, and dependency data across multi-ecosystem environments.
Impact: This will significantly enhance the accuracy and comprehensiveness of SBOMs, enabling organizations to proactively identify and mitigate security vulnerabilities and licensing risks throughout their software supply chain.
Organizations heavily reliant on open source should proactively identify and directly fund maintainers of critical, low-attention projects, prioritizing usage metrics over superficial popularity.
Impact: Direct funding will stabilize critical open-source infrastructure, improve project maintenance, and reduce the risk of security vulnerabilities due to under-resourcing, thereby enhancing overall software reliability.
Engage with and contribute to initiatives like the "OSS Taxonomy" to improve open-source project discovery and foster better tooling and data fragmentation reduction.
Impact: Participation will accelerate the development and adoption of standardized classification, leading to more efficient open-source project selection, reduced development costs, and increased innovation.
Implement "inverse CI" workflows that test new open-source releases against critical downstream dependencies to proactively identify and mitigate breaking changes before public deployment.
Impact: This proactive testing approach will drastically reduce downstream disruptions, improve developer productivity, and foster greater trust and collaboration within the open-source ecosystem by ensuring compatibility.
Package managers and developers should collaborate on establishing and enforcing metadata standards that prevent prompt injection vulnerabilities and control automated publishing behaviors.
Impact: This will enhance the security posture of open-source ecosystems against emerging AI-driven threats, safeguarding the integrity of software components and the trust placed in package registries.