Why do highly connected open-source dependency networks create outsized supply chain risk?

Why connected dependency graphs create outsized blast radius

Open-source ecosystems are not simple chains, they are graphs with hubs, shared transitive dependencies, and repeated reuse of the same packages across many projects. That structure means a compromise at one upstream point can affect many downstream consumers at once. The risk is not just direct infection of a single package, but amplification through dependency reuse, build tooling, and automated update paths.

Highly connected graphs also create concentration. When many packages depend on the same maintainer, token, build pipeline, or release process, the ecosystem inherits that shared failure point. A small malicious change, or even an accidental bad release, can spread faster than teams can manually detect or review it.

open source supply chain are therefore exposed to propagation risk: the more central a package becomes, the larger the number of projects that may trust it implicitly. That trust is efficient for development, but it turns upstream integrity into a systemic dependency for everyone downstream, as seen in incidents like PyPI Breach and LiteLLM PyPI package breach.

Why decentralized dependency choices make the graph uneven

Decentralized ecosystems do not optimize dependencies globally. Maintainers choose what seems useful for their own project, so the network naturally develops uneven structure: a few popular packages become highly connected, while many others remain lightly used. That unevenness is what creates outsized risk, because central nodes become single points of failure in practice even when no one intended them to be.

This also makes dependency hygiene inconsistent. One project may pin versions tightly, another may auto-update, and a third may accept transitive packages without much scrutiny. The result is a patchwork of exposure, where the same upstream dependency can be relatively well controlled in one place and blindly trusted in another. Attackers look for exactly that inconsistency.

The technical problem is compounded by maintainer workflows. Package publication, CI/CD credentials, and repository access often sit close to the release path, so a compromised account or token can reach many downstream consumers quickly. The cascading effect is visible in Nx Package Attack , 2,300+ Credentials Leaked and SpotBugs Token GitHub Supply Chain Attack.

What makes a minor upstream event become an ecosystem-wide incident

A dependency network becomes risky when downstream projects inherit upstream trust without adding sufficient verification. If a malicious package update, compromised maintainer account, or poisoned build artifact enters a widely reused dependency, the event can spread through normal automation rather than through a novel exploit chain. In other words, the network itself becomes the delivery mechanism.

Propagation is especially severe when downstream systems do not distinguish between “package is available” and “package is trustworthy.” Build systems, lockfile updates, and dependency refresh jobs can amplify the spread by pulling the compromised version into many environments at once. Once that happens, the impact may include source disclosure, token theft, malicious code execution, or unauthorized access to adjacent systems.

That is why the same upstream compromise can have very different consequences depending on graph position. A low-use leaf package may be noisy but contained, while a package that sits near the center of a dense dependency cluster can affect many repositories, environments, and teams before detection catches up.

Risk and Threat Considerations

The main risk is concentration of trust: when many projects depend on a small number of packages or maintainers, a single compromise can create broad downstream exposure. Threat actors understand this leverage and target publishing accounts, package registries, and build pipelines because they offer scalable reach rather than one-off access.

Failure mechanism: An attacker compromises an upstream maintainer account, package release path, or CI/CD secret, then distributes malicious or altered code through normal dependency updates. Because downstream consumers trust the package graph, the malicious change can propagate before review or detection blocks it.

Impact: The compromise can cascade across many repositories, leak secrets, alter builds, introduce persistence, or create follow-on access into development and production environments. The larger and more centralized the dependency hub, the greater the blast radius.

Practitioner Guidance

What to prioritise: Identify which dependencies are graph hubs, which maintainers control them, and which build or publishing credentials can move changes into many downstream projects. Those are the points where compromise changes from local risk to ecosystem risk.

What to verify: Do not trust dependency popularity alone. Verify that critical packages have version pinning, provenance checks, maintainer protections, and release review strong enough to withstand account compromise or malicious updates.

What good looks like: The most connected dependencies are the most tightly controlled, and teams can explain why they trust each one, how they would detect tampering, and how quickly they could revoke or replace it if upstream integrity fails.

Practitioner takeaway: In dependency networks, the real risk is not just that one package fails, but that its failure is multiplied by every place that blindly inherits it.

Related resources from NHI Mgmt Group

• Why do supply chain attacks on open source dependencies create outsized risk in modern development environments?
• Why do maintainer accounts create supply chain risk in open source?
• Why do orphaned or stewarded open source packages create hidden supply chain risk in Linux environments?
• Why do open-source dependencies create more supply chain risk than the code your team writes?