Open source increases risk because modern applications pull in many direct and transitive dependencies that change over time. Without clear visibility, teams may ship builds containing outdated or vulnerable packages, or miss the impact of a newly disclosed CVE. The more dependencies a codebase consumes, the harder it becomes to maintain safe release decisions.
Why open source changes the supply chain problem for in-house applications
Open source does not just mean “free code,” it means inheriting a dependency graph that you did not write and cannot fully control. In in-house applications, that graph can include direct libraries, transitive packages, build plugins, and tooling that all change on different schedules. The security issue is not open source itself, but the operational reality that trust, provenance, and update discipline become harder to maintain as the dependency chain grows.
That matters because a vulnerable package can enter your build long after the application team last touched the related code. A release may be blocked by an outdated subdependency, or worse, quietly ship with an exposed flaw because no one has a complete inventory of what the application actually consumes. For this reason, supply chain risk is really a visibility and control problem as much as a code quality problem.
Open source also widens the blast radius of compromise. A single package maintainer account, compromised release process, or malicious update can affect many downstream applications at once, especially when teams rely on popular components without independent verification. OpenSSF is a useful reference point for the broader set of controls that exist around dependency security, provenance, and ecosystem hardening.
Where the risk shows up in practice
In-house teams usually feel this risk in a few recurring ways. First, dependency sprawl makes it difficult to answer a simple question: “What exactly is in this build?” Second, transitive dependencies can introduce exposed code paths that no one on the team selected directly. Third, package ecosystems move quickly, so a component that was acceptable at release time may become unsafe after a new advisory, compromise, or malicious version publication.
The problem is amplified when open source is consumed through package managers, CI/CD pipelines, build tools, or developer workstations that can fetch and cache artifacts automatically. That means the attack surface extends beyond the source repository itself and into the mechanisms used to assemble, test, sign, and deploy software. A package compromise can therefore become a release compromise if build trust is assumed rather than verified. NIST’s Secure Software Development Framework is relevant here because it treats secure composition, integrity, and disciplined development workflows as core software assurance concerns.
Where provenance matters most, teams should also look at whether they can verify what was built, from what source, and by which process. Artifact integrity controls help reduce the chance that an upstream package event silently becomes a downstream application event. SLSA is one of the clearest models for thinking about build provenance and tamper resistance across the software lifecycle.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
CIS Controls v8, NIST CSF 2.0 and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| CIS Controls v8 | 15 — Service Provider Management | Covers third-party software and dependency risk across the supply chain. |
| 16 — Application Software Security | Directly applies to secure handling of application code and dependencies. | |
| Recommendation — Assess supplier and upstream package risk before approving dependencies. Scan, update, and validate application dependencies before release. | ||
| NIST CSF 2.0 | PR.DS — Data Security | Supports integrity and protection of software artifacts and build outputs. |
| ID.SC — Supply Chain Risk Management | Directly addresses upstream dependency and supplier risk in software delivery. | |
| PR.IP — Information Protection Processes and Procedures | Fits dependency review, patching, and release hygiene for software composition. | |
| Recommendation — Protect build artifacts and verify integrity before deployment. Track supplier and component risk across the software supply chain. Maintain dependency review and patch management procedures for releases. | ||
| NIST SP 800-63 | Digital Identity Guidelines | Supports trust in issuing and validating identities used in build and release systems. |
| Recommendation — Use strong authentication for repository and release-system access. | ||
Practitioner Guidance
What to prioritise: Start with dependency inventory and release visibility. If you cannot enumerate direct and transitive dependencies, you cannot reliably assess whether a disclosed CVE or compromised package affects a given in-house application.
What to verify: Confirm that your build process can answer three questions for every release: which packages were used, which versions were resolved, and whether the final artifact can be tied back to a trusted source and repeatable pipeline. If those answers depend on manual reconstruction, the control is too weak for high-confidence release decisions.
Common mistake: Treating open source risk as a one-time licensing or approval review. The real risk changes as dependencies update, maintainers change, and transitive packages shift underneath the application, so the governance model has to be continuous rather than gate-based.
Practitioner takeaway: Open source increases supply chain risk when teams consume more software than they can observe and verify, so the control objective is to narrow the gap between dependency usage, artifact provenance, and release approval.
Related resources from NHI Mgmt Group
- Why do malicious packages create more supply chain risk than ordinary CVEs in open-source software?
- Why do AI generated code and open source models increase supply chain risk for application security teams?
- Why do build systems increase supply chain risk in software teams?
- How do security teams reduce supply-chain risk in open-source release processes?
