- Updated: December 16, 2025
- 6 min read
Introducing AI SBOM: Open‑Source AI Software Bill of Materials Scanner
AI SBOM Tool: Secure Your Machine‑Learning Models with Open‑Source Scanning

The AI SBOM tool from Lab700xOrg is an open‑source scanner that automatically generates a Software Bill of Materials for AI/ML model files, exposing hidden security vulnerabilities and license violations before they reach production.
In a world where AI models are shipped as binary artifacts—.pt, .pkl, .safetensors—traditional SBOM solutions fall short. This tool fills the gap by performing deep binary introspection without executing the model, delivering fast, reliable, and compliance‑ready reports for developers, security engineers, AI product managers, and compliance officers alike.
Feature Overview: What the AI SBOM Tool Actually Does
Built in Python and distributed via PyPI, the scanner combines several sophisticated techniques into a single, easy‑to‑use CLI. Below is a concise breakdown of its core capabilities.
| Feature | Benefit |
|---|---|
| Deep Introspection | Analyzes model internals without loading them into memory, eliminating the risk of accidental code execution. |
| Pickle‑Bomb Detector | Identifies maliciously crafted Pickle payloads that could trigger Remote Code Execution (RCE). |
| License Radar | Flags models with restrictive or incompatible licenses (e.g., CC‑BY‑NC) before they are used commercially. |
| CycloneDX v1.6 Export | Produces standards‑compliant JSON SBOMs that integrate with existing vulnerability‑management tools. |
| Blazing Speed | Scans gigabyte‑scale models in seconds by reading only file headers. |
The tool also ships with a test‑generator that creates synthetic risk scenarios, allowing security teams to validate detection rules without exposing real malicious binaries.
Why Security & Compliance Teams Should Care
AI model supply chains are increasingly targeted by threat actors. A compromised model can execute arbitrary code, exfiltrate data, or embed backdoors that persist for years. Simultaneously, licensing missteps can lead to costly legal disputes. The AI SBOM tool addresses both vectors in a single workflow.
- Early‑Stage Risk Detection: Spot malicious payloads before they are ever loaded into a runtime environment.
- License Compliance Assurance: Automatically generate a clear inventory of model licenses, simplifying audit preparation.
- Integration‑Ready Reports: Exported CycloneDX files feed directly into SIEMs, GRC platforms, and vulnerability scanners.
- Reduced Attack Surface: By avoiding model execution during scanning, the tool eliminates the very attack vector it aims to detect.
- Scalable for CI/CD: Fast, stateless scans make it practical to embed in every pull request and build pipeline.
“We integrated the AI SBOM scanner into our nightly builds and caught a rogue Pickle payload that would have otherwise slipped into production. It saved us weeks of debugging and potential legal exposure.” – Lead Security Engineer, FinTech Startup
Getting Started: Installation & Basic Usage
The tool follows the classic Python packaging workflow, making it familiar to any developer who has used pip. Below is a step‑by‑step guide.
- Install via PyPI:
pip install aisbom - Run a scan on a directory:
aisbom scan ./models/ --output sbom.json - Review the report: Open
sbom.jsonin any JSON viewer or use the built‑in HTML viewer:aisbom view sbom.json - Validate with test data:
aisbom generate‑test‑artifacts --output ./test_artifacts/ - Integrate with CI/CD: Add the scan command to your pipeline (see next section).
For Docker‑centric environments, a ready‑made Dockerfile is provided in the repository, allowing you to spin up an isolated scanner container in seconds.
Seamless CI/CD Integration
Modern development workflows demand automated security checks. The AI SBOM tool’s lightweight CLI and JSON output make it a perfect fit for GitHub Actions, GitLab CI, Azure Pipelines, and Jenkins.
- GitHub Actions Example: Use the
actions/setup-pythonstep, installaisbom, then runaisbom scan. Fail the job if the report contains anyhighseverity findings. - GitLab CI: Add a
scriptblock that runs the scanner and publishes the SBOM as an artifact for downstream security stages. - Jenkins Pipeline: Invoke the scanner inside a
shstep and parse the CycloneDX JSON with thedependency-checkplugin. - Azure DevOps: Use a
PythonScript@0task to execute the scan and push the SBOM to Azure Artifacts.
By treating the SBOM as a “gate” artifact, teams can enforce “no‑malicious‑model” policies, automatically block merges that contain risky binaries, and keep compliance dashboards up‑to‑date.
Take the Next Step with UBOS – Your AI‑First Automation Partner
While the AI SBOM tool gives you granular visibility into model risk, pairing it with a full‑stack AI automation platform accelerates remediation, governance, and continuous delivery. UBOS offers a suite of capabilities that complement the SBOM workflow:
- UBOS platform overview – a unified environment for building, testing, and deploying AI services.
- Enterprise AI platform by UBOS – enterprise‑grade security, role‑based access, and audit trails.
- Workflow automation studio – design zero‑touch pipelines that automatically invoke the AI SBOM scanner on every model commit.
- AI SBOM integration (internal placeholder) – embed the scanner directly into UBOS’s model registry.
- UBOS templates for quick start – jump‑start your compliance pipeline with pre‑built SBOM‑aware templates.
- UBOS pricing plans – flexible pricing that scales from startups to Fortune‑500 enterprises.
- UBOS partner program – collaborate with UBOS to co‑market your security tooling.
- UBOS portfolio examples – see real‑world cases where AI SBOM scanning prevented supply‑chain attacks.
- AI marketing agents – leverage AI‑driven content creation while staying compliant.
- UBOS for startups – fast, cost‑effective compliance for early‑stage teams.
- UBOS solutions for SMBs – secure AI adoption without heavyweight infrastructure.
- Web app editor on UBOS – build custom dashboards to visualize SBOM data in real time.
- About UBOS – learn more about the team behind the platform.
- UBOS homepage – explore the full ecosystem.
Ready to embed AI SBOM scanning into your development lifecycle? Start with the open‑source scanner, then scale with UBOS’s automation suite for continuous, enterprise‑grade protection.
Explore the Source Code on GitHub
The full project, including documentation, Dockerfiles, and CI templates, lives on GitHub. Review the code, contribute improvements, or fork the repository to tailor the scanner to your organization’s needs:
Visit the AI SBOM GitHub repository
Conclusion: Turning Model Transparency into Competitive Advantage
As AI accelerates across every industry, the hidden risks inside model binaries become a strategic liability. The AI SBOM tool offers a pragmatic, open‑source solution that transforms opaque artifacts into transparent, auditable inventories. By integrating this scanner with modern CI/CD pipelines and pairing it with UBOS’s end‑to‑end AI automation platform, organizations can achieve:
- Proactive detection of malicious code before it ever runs.
- Clear visibility into licensing obligations, reducing legal exposure.
- Automated compliance reporting that satisfies auditors and regulators.
- Scalable security that grows with your model portfolio.
In short, the AI SBOM scanner is not just a tool—it’s a foundational component of a secure AI supply chain. Adopt it today, and let your teams focus on building innovative models rather than firefighting hidden threats.