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What counts as undeclared open-source code?
A dependency can be present in software without appearing as a declared package in a manifest or lockfile. Someone may have copied source files into the repository, added a vendored library, embedded code in a larger component, or included code in a generated or packaged artifact. These cases are easy to miss if the audit checks only package-manager metadata.
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There are therefore two related but different questions: what dependencies do the repository’s declarations resolve to, and what third-party code can be found in the files and artifacts themselves? The first is usually answered with manifests, lockfiles, and dependency graphs. The second requires scanning content and investigating evidence such as package metadata, license text, copyright notices, file paths, hashes, and similarity to upstream code.
Finding a likely match is not the same as deciding that a license obligation has been met, that a component is policy-compatible, that a vulnerability applies, or that the code ships to users. Those are separate determinations that may require engineering, security, and legal review.
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Prepare a scan that answers the right question
Choose the repository revision
Start with a clean checkout and record its commit or other revision identifier. Without a stable revision, reviewers cannot tell exactly which files produced the findings or repeat the assessment later. Also record relevant submodules and any generated-source or vendored directories that form part of the product.
Decide whether you are auditing source or shipped software
For a repository inventory, scan the source tree and the directories that contain third-party or generated material. For a question about what customers receive, include the actual distributed artifacts: build outputs, containers, installers, archives, or packaged releases as relevant. A source repository and a release artifact can differ; test fixtures and build-only dependencies may be present in one but not the other.
Write down the scope before scanning. Record which repository revision and artifact versions were examined, which paths were included or excluded, and why. This makes exclusions visible rather than allowing a later reader to mistake an incomplete scan for a complete one.
Keep the scan repeatable
Retain the scanner names and versions, configuration, invocation or workflow, reports, and the date of the run alongside the revision or build identifier. Where scanners allow path exclusions or ecosystem-specific settings, preserve those too. A finding without its file path and evidence is difficult to validate; a report without its configuration may be impossible to reproduce.
Build a declared-dependency baseline
Collect the project’s package manifests and lockfiles, then resolve dependency trees where possible. Include transitive dependencies: a project’s direct package may pull in additional components that do not appear as direct declarations. A dependency graph can provide package versions, manifest paths, dependency paths, and, depending on the platform and ecosystem, license or vulnerability context.
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GitHub Docs describes its dependency graph as automatically parsing dependencies by analyzing repository manifests and lock files. Its documentation also describes dependency data submitted to the graph. The feature is useful for supported ecosystems and available metadata, but its results answer what the dependency records reveal. It cannot establish that no source was copied into the repository outside those records.
Record gaps in the baseline: unsupported ecosystems, missing or stale lockfiles, unresolved dependencies, and directories not represented by package metadata. Those gaps help explain why a source scan is needed and prevent the graph from being treated as a complete code inventory.
Scan source files and relevant artifacts
Use a scanner that inspects file content and package clues, not only dependency declarations. ScanCode Toolkit reports that it can detect licenses, copyrights, package manifests, and direct dependencies in source and binary files, and documents command-line and CI/CD use. This kind of scan can surface candidate components that a manifest-and-lockfile review does not show.
OSS Review Toolkit (ORT) can coordinate several stages of an open-source review. Its Analyzer determines dependencies and metadata; Downloader retrieves source; Scanner invokes configured source-code scanners for license and copyright findings; Evaluator applies policy; and Reporter produces reports or bills of materials. The outcome depends on the ecosystems, scanner configuration, and policy rules used, so document those choices rather than treating an orchestrated workflow as automatically comprehensive.
Look for several kinds of evidence
One clue rarely establishes provenance on its own. Review findings using a combination of evidence, where available:
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- Package clues: manifests, package metadata, version strings, and dependency records.
- Notices: license text, copyright statements, author or project names, and attribution files.
- File context: directory names, file paths, comments, and neighboring files that may identify a library or upstream layout.
- Code evidence: hashes, fingerprints, or similarity to upstream files, including evidence that code may have been changed.
- Build context: whether the candidate appears in a product build, test fixture, generated output, or another non-shipped area.
A notice can be boilerplate copied from a template, and a matching code fragment may come from a modified fork or a common snippet. Conversely, a partial or altered copy may not retain obvious package names or notices. Treat automated output as a set of leads that guides review, not as a final attribution.
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Validate and triage every candidate
For each candidate, create a record that lets another person follow the reasoning from the scanner output to the disposition. Preserve the exact file path or artifact layer, the scanner’s evidence, any relevant notice or code excerpt, the proposed upstream project, confidence, and validation status.
- Inspect the location. Establish whether the files are part of application source, a vendored directory, generated code, a test fixture, or a built artifact. Note the repository revision or artifact layer where the evidence was found.
- Identify a plausible upstream. Use package metadata, notices, history, file layout, or code similarity to develop a candidate origin. Do not infer a project solely from a common license phrase or a familiar filename.
- Compare against upstream evidence. When source or release archives are available, compare the candidate files with them. Look for matching content, version clues, changes, and evidence that could indicate a fork or partial copy. Record what supports the match and what remains uncertain.
- Check product inclusion. Review build context to establish whether the candidate is used or distributed in the product under assessment. A code match in a test fixture may be relevant to repository hygiene but does not by itself show that the component ships.
- Route the right question to the right reviewer. Engineering can help establish origin and build use; security can assess vulnerability relevance; legal or compliance reviewers can interpret license obligations and policy. Keep these conclusions distinct in the record.
Use a clear status such as confirmed, probable, unresolved, or rejected, with a short evidence-based explanation. A scanner’s label alone is not a legal conclusion or proof of vulnerability exposure. If evidence is ambiguous, preserve the uncertainty and ask for review instead of converting a tentative match into a definite attribution.
Choose tools by coverage and workflow
There is no single tool category that answers every part of the undeclared-code question. Compare tools on the coverage and evidence they provide, the effort required to review findings, and whether the outputs fit the organization’s workflow. The sources described here do not establish a head-to-head accuracy benchmark, so a single claimed accuracy figure would not be a sound basis for selection.
| Approach | Useful for | Important limitation | Evaluation questions |
|---|---|---|---|
| Manifest and lockfile dependency graph | Declared direct and transitive packages, versions, dependency paths, and some license or security context. | May miss copied or vendored code absent from package metadata; results depend on ecosystem support and available records. | Which ecosystems are supported? Are lockfiles resolved faithfully? Can reviewers trace transitive paths and manifest locations? Does it integrate with the repository workflow? |
| Source and binary scanner | Package, license, copyright, and code clues found by inspecting files and artifacts. | Findings need triage; modified, partial, generated, or common code can make matching difficult. | Which file and artifact types are inspected? How much evidence is included? Can it suggest provenance or version? How are false positives reviewed, and where is scan data processed? |
| Orchestrated SCA or compliance workflow | Combines dependency analysis, source retrieval or scanning, policy evaluation, reporting, and SBOM generation. | Setup, ecosystem support, scanner configuration, and policy quality affect the result. | Can the workflow be repeated in CI? Are policies understandable and maintainable? Which outputs and deployment options are available? What does integration and ongoing operation require? |
These approaches complement one another: GitHub documents dependency-graph analysis of manifests and lockfiles; ScanCode Toolkit describes source and binary scanning; ORT documents combining dependency analysis, configured scanning, policy evaluation, and reporting. Select on the evidence and operational fit needed for the audit, not on a presumed guarantee that one scanner finds everything.
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Generate and retain an SBOM
After validating findings, create an inventory that reflects the assessed revision or build and retain it with the scan report and configuration. An SBOM can make the component inventory easier to exchange and process, but it should not be mistaken for independent proof that every component was discovered: its completeness depends on scan scope, tool capabilities, and review.
ORT documents SPDX and CycloneDX SBOM generation as well as custom FOSS attribution documentation. GitHub documents repository SBOM export in SPDX JSON. Its documentation says the synchronous export operation is scheduled to close after November 13, 2026, and directs users to asynchronous generation and fetch endpoints. Because that date is upcoming as of October 13, 2026, teams using the synchronous operation should plan against the documented transition and verify the current endpoint guidance.
Keep a link between each inventory and the context that produced it: repository revision or build identifier, artifact scope, scanner and configuration versions, review decisions, and date. Where an SBOM format cannot carry all of the review evidence, keep the detailed report and decision record alongside it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make detection part of the development and release process
A one-time scan describes one revision and one scope. To catch new copied code or newly introduced dependencies, run the inventory process when relevant changes are made and before releases. Review dependency changes in pull requests, and define who resolves policy findings and how unresolved issues are recorded or escalated.
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Use different checks for different change types
- Manifest or lockfile changes: inspect newly added or changed direct and transitive dependencies, their paths, and available package metadata.
- Source changes: scan or review newly introduced files and changes in vendored or third-party directories, including code that has no package declaration.
- Release changes: scan the actual artifacts being distributed and compare the resulting inventory with the intended release contents.
- Policy findings: assign an owner, record the decision and rationale, and retain unresolved questions for the appropriate reviewer rather than silently suppressing them.
AWS guidance also recommends software composition analysis to validate third-party components and describes scanning inventories such as SBOMs and dependency manifest files. Use that as part of a broader process: an SBOM or manifest scan contributes useful evidence, while code-content scanning addresses copies that may not be declared.
Common blind spots and how to address them
Relying on manifests alone
A dependency graph can be an excellent view of declared packages and still omit code placed directly into the repository. Pair it with content scanning and review of likely vendored or embedded areas.
Treating every scanner hit as a confirmed component
License text may be generic, fingerprints may match modified code, and a component may appear only in tests. Preserve the evidence, inspect context, compare to a plausible upstream, and record confidence and status.
Scanning the repository when the question concerns a release
Repository contents do not necessarily match shipped contents. Include the release artifacts for a release-focused assessment, and retain the artifact identity so the inventory can be tied to what was actually evaluated.
Ignoring configuration and exclusions
A report without scope, tool version, and configuration leaves unanswered what was scanned. Keep exclusions and settings with the results, and explain why excluded paths were outside the audit.
Conflating provenance, license, and security decisions
Identifying an upstream match helps answer where code may have come from. It does not, by itself, decide whether the applicable license obligations have been met or whether a vulnerability affects the product. Record and route each determination separately.
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