A Breakdown of Open Source SBOM Analysis Tools: Measuring SBOM Quality

One of the most common questions I hear from teams generating SBOMs is simple:

“How do I know if my SBOM is any good?”

At this point, most teams can generate an SBOM. There are plenty of tools that will output CycloneDX or SPDX in a nicely formatted JSON file. But generating an SBOM and generating a high-quality SBOM are two very different things.

Automated SBOM analysis tools can be used to check SBOM quality, including tools that validate, score, or analyze the SBOM itself.

This post breaks down several categories of open source SBOM analysis tools and what they’re typically used for.

Types of Open Source SBOM Analysis Tools

Open Source SBOM Analysis Tools at a Glance

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1. SBOM Quality Scoring Tools

There is a growing category of tools that focus on assigning a quality score to an SBOM. A well-known open source example is SBOMqs. It analyzes an SBOM and produces a quality score based on things like:

• Percentage of components with license information
• Presence of CPE identifiers
• Version completeness
• Required metadata fields
• Regulatory alignment (FDA, automotive, etc.)

Some commercial vendors now offer similar scoring systems that are helpful for benchmarking and reporting. SBOM Scorecard (from OpenSSF) also analyzes and scores SBOMs based on completeness, accuracy, and other quality metrics.

These tools are especially useful in CI/CD pipelines. If you’re generating an SBOM for every build or release, you can track quality over time. If your score suddenly drops from 90% to 65%, something has changed that is worth investigating.

Quality scoring tools are commonly used for:

• Automated pipeline integration
• Measuring metadata completeness
• Comparing SBOMs across releases
• Supporting regulatory reporting
• Establishing internal quality gates

They provide a quantifiable way to measure SBOM consistency and completeness at a metadata level.

2. Visualization Tools

Another category of SBOM analysis tools focuses on visualization.

Tools like Sunshine provide graphical representations of:

• Dependency relationships
• License distribution
• Component structure

Visualization tools are helpful during proof-of-concept phases, internal reviews, or architecture discussions. A dependency tree or license heatmap can make patterns obvious that aren’t immediately clear in raw JSON.

These tools are typically used for:

• Manual inspection
• License review workflows
• Understanding dependency structure
• Communicating SBOM data to non-technical stakeholders

They improve readability and interpretation of SBOM content.

3. Schema Validators and Format Checkers

At the most foundational level, you have schema validators.

Examples include:

• CycloneDX Validator / Web Tool
• NIST and NTIA-related validation tools
• SPDX online validation tools
• SBOMAudit

These tools verify whether an SBOM conforms to the required schema or standard.

They check for:

• Required fields
• Proper JSON structure
• Specification compliance
• Data type correctness

If you’re submitting an SBOM to a prime contractor, government agency, or regulated customer, this step matters. A malformed SBOM may be rejected outright if it fails schema validation.

Schema validators are commonly used to:

• Ensure standards compliance
• Prevent downstream tool failures
• Support regulatory submissions
• Enforce formatting requirements before release

4. Dependency and Vulnerability Analyzers

Another important category includes open source tools that take an existing SBOM and correlate it with vulnerability databases.

Examples include:

• Grype
• Dependency-Track

These tools ingest SBOM data and match listed components against known vulnerabilities in databases such as the National Vulnerability Database (NVD).

They are commonly used to:

• Identify known CVEs in shipped software
• Prioritize remediation efforts
• Support vulnerability management workflows
• Feed security dashboards and reporting systems

Dependency and vulnerability analyzers are often where SBOMs start delivering operational value. Instead of just maintaining an inventory, teams can connect that inventory to security findings.

Why Automated SBOM Analysis Is Helpful

Automated SBOM quality tools absolutely have a place.

They are effective for:

• Sanity checking releases
• Ensuring metadata completeness
• Monitoring SBOM trends over time
• Supporting regulatory submissions
• Comparing outputs from different SBOM generators

If you’re evaluating SBOM generators, running their output through a scoring tool or validator provides a baseline comparison. It helps ensure your SBOM meets formatting requirements and includes expected metadata.

For many organizations, especially those operating in regulated environments, these tools are an important part of the workflow.

Where SBOM Quality Breaks Down

Automated SBOM analysis tools are useful because they help catch real problems. But to understand their value and their limits,  it’s important to look at the kinds of quality issues teams actually encounter.

As someone who works primarily in compiled and embedded environments, I see the same patterns repeatedly.

Missing or Incomplete Component Identifiers

Component identifiers such as CPE (Common Platform Enumeration) and PURL (Package URL) are what allow SBOMs to be correlated with vulnerability databases like the National Vulnerability Database (NVD).

If those identifiers are missing, your vulnerability analyzer can’t reliably match components to CVEs. At that point, you technically have an inventory, but you can’t operationalize it.

Quality scoring tools are helpful here because they flag missing identifiers. But they can only flag what’s present in the SBOM. They don’t verify whether all relevant components were included in the first place.

Inaccurate Version and Dependency Data

Version mismatches are more common than most teams expect. A tool might report version 2.3.1 when the binary actually includes 2.3.0. That minor version difference could determine whether you’re exposed to a vulnerability.

For embedded systems with long deployment lifecycles, this becomes more dangerous over time. A small inaccuracy today becomes a significant blind spot five or ten years down the road.

Validators won’t catch this. Scoring tools won’t catch this. The SBOM can look structurally perfect and still contain inaccurate component data.

Schema and Format Violations

SBOMs that violate schema rules can break CI/CD pipelines or get rejected by customers and regulators.

Common issues include:

• Missing required fields
• Incorrect data types
  Invalid structure

This is where schema validators are extremely valuable. They enforce conformance and prevent downstream failures.

But passing schema validation only tells you the SBOM is well-formed—not that it’s complete or correct.

Lack of Provenance and Supplier Information

Provenance refers to where a component originated and how it entered your build.

Regulatory frameworks like Executive Order 14028 and FDA guidance increasingly require provenance and supplier data. Without it, you may meet technical SBOM requirements but still fall short of compliance expectations.

Quality scoring tools can detect missing supplier fields. They cannot determine whether the listed supplier information is accurate or whether all third-party code was properly captured.

Building Confidence in Your SBOM

When you step back, you can see how each category of tool contributes to SBOM quality:

• Validators ensure structural correctness
• Scoring tools measure metadata completeness
• Visualization tools help interpret relationships and dependencies
• Vulnerability analyzers connect SBOM contents to known risk

Each plays a valuable role, but none fully answers the most important question:

Does this SBOM accurately reflect what is actually inside the compiled software?

In embedded and C/C++ environments especially, the hardest challenges are not about formatting or schema compliance. They are about completeness and accuracy, ensuring that every relevant component is captured and correctly represented.

Automated tools can highlight gaps in metadata and structure, but they cannot independently verify whether something was included in the build and omitted from the SBOM. For teams trying to move beyond surface-level validation, a more deliberate evaluation approach is needed. 

One place to get started is this interactive SBOM Accuracy Checklist for C/C++ Projects, which can help guide that process. Its purpose is to force the kinds of questions automated tools don’t ask, and surface the gaps they can’t see.

Ultimately, measuring SBOM quality is a combination of verifying completeness, compliance with industry standards and best practices, and accuracy to what’s compiled in your software.

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