← Back to Blog
Security December 26, 2025 6 min read

Why SBOM Matters for AI Models

Software Bill of Materials isn't just for traditional software. Here's why AI models need SBOMs and how to use them.

When a security vulnerability is discovered in a popular library, organizations scramble to answer one question: "Are we affected?" For traditional software, Software Bill of Materials (SBOM) provides that answer. For AI models, we're just starting to catch up.

What Is an SBOM?

A Software Bill of Materials is a formal, machine-readable inventory of components that make up a software artifact. Think of it like a nutrition label for software: it tells you exactly what ingredients went into the product.

For AI models, an SBOM includes:

  • Framework dependencies (PyTorch, TensorFlow, JAX)
  • Supporting libraries (NumPy, Pillow, tokenizers)
  • Version information for each component
  • License data for compliance tracking
  • Vulnerability mappings to known CVEs

Why Traditional SBOMs Don't Work for AI

AI models present unique challenges:

1. Implicit Dependencies

A traditional application has a requirements.txt or package.json. AI models often have no explicit dependency declaration. Instead, you have to infer dependencies from:

  • Model architecture (transformers need transformers)
  • File formats (SafeTensors, ONNX, GGUF)
  • Pipeline tags (image-classification implies vision libraries)
  • Framework hints in config files

2. Transitive Complexity

Loading a single HuggingFace model might pull in 50+ Python packages through transitive dependencies. Each of those is a potential attack surface.

3. Runtime vs Build Time

Unlike compiled software, Python's dynamic nature means dependencies are resolved at runtime. The same model file might use different library versions depending on the execution environment.

How HuggingHugh Generates SBOMs

We use a multi-source approach to build comprehensive SBOMs:

Step 1: Metadata Analysis

We parse the model's config.json, tokenizer_config.json, and other metadata files to identify the model architecture and explicit requirements.

Step 2: Inference Engine

Our inference engine maps model characteristics to likely dependencies:

model_type: "llama" → transformers, accelerate, safetensors
pipeline_tag: "text-to-image" → diffusers, pillow, torch
has_gguf: true → llama-cpp-python, gguf

Step 3: Version Resolution

We determine appropriate version ranges based on the model's upload date and known compatibility requirements.

Step 4: Vulnerability Scanning

We scan the inferred SBOM against the National Vulnerability Database (NVD) and other sources using Grype.

Step 5: CycloneDX Output

The final SBOM is formatted as CycloneDX 1.5 JSON, a widely-supported standard that integrates with security tools.

Using HuggingHugh SBOMs

Every model report includes a downloadable SBOM. Here's how to use it:

For Security Teams

  1. Download the sbom.cdx.json from the model report
  2. Import into your vulnerability management platform
  3. Cross-reference with your approved component list
  4. Set up alerts for newly discovered CVEs

For Compliance

  1. Extract license information from the SBOM
  2. Verify all components meet your license policy
  3. Document the supply chain for audit purposes

For Developers

  1. Review dependencies before integrating a model
  2. Check for version conflicts with existing projects
  3. Plan for dependency updates and security patches

The Broader SBOM Ecosystem

The push for SBOMs in AI is part of a larger movement:

  • Executive Order 14028 (2021) mandates SBOMs for software sold to the U.S. government
  • NIST SP 800-218 includes SBOM requirements in secure software development
  • EU Cyber Resilience Act will require SBOMs for products sold in Europe
  • OpenSSF promotes SBOM adoption across open source

AI models will inevitably fall under these requirements. Organizations using AI in regulated industries (healthcare, finance, government) are already being asked about model supply chains.

Limitations of AI SBOMs

Current AI SBOMs have limitations:

  1. Inference isn't perfect: We can't know every dependency without running the model
  2. Environment variance: Your runtime might differ from our analysis
  3. Model-level risks: SBOMs cover dependencies, not the model weights themselves
  4. Training data: SBOMs don't capture training data provenance

Best Practices

  1. Generate your own SBOM after installing model dependencies
  2. Pin versions in your deployment environment
  3. Monitor for CVEs in your dependency tree
  4. Update regularly to get security patches
  5. Document exceptions for known acceptable risks

HuggingHugh's Role

We provide a starting point for AI model security:

  • Free SBOM generation for top HuggingFace models
  • Vulnerability scanning against known CVEs
  • Trust scoring that weighs dependency health
  • Daily updates as models and vulnerabilities change

Enterprise security tools charge thousands for similar functionality. We believe this baseline security visibility should be free for the AI community.

Conclusion

SBOMs are becoming essential for software security, and AI models are no exception. As AI systems become more critical to business operations, understanding your model's supply chain is not optional, it's a requirement.

Start by checking your most-used models on HuggingHugh. Download the SBOM, review the dependencies, and integrate that information into your security processes.

Related Articles