Snyk AI-BOM

Product Introduction

1. Snyk AI-BOM is a specialized CLI tool that extends traditional Software Bill of Materials (SBOM) capabilities to map AI-specific components within applications. It creates comprehensive inventories of AI dependencies including models, datasets, and external services powering AI implementations. The tool specifically detects connections established through the Model Context Protocol (MCP) open standard to provide full visibility into AI supply chains. This enables organizations to maintain governance over rapidly evolving AI ecosystems through automated dependency mapping.

2. The core value lies in providing security and engineering leaders with critical governance insights for AI-powered applications through automated component discovery. It transforms opaque AI dependencies into auditable inventories that track foundational models, open-source components, and third-party services. By extending SBOM principles to AI contexts, it addresses unique security challenges posed by LLM integrations and external tool connections. This enables compliance verification and risk assessment for AI systems through standardized CycloneDX outputs.

Main Features

1. AI Component Identification automatically detects foundational models like GPT-4, open-source models like Llama-4, datasets, and AI agents within Python codebases. It surfaces detailed metadata including model cards, licenses, and provenance information when available through dependency analysis. The tool categorizes components into models, agents, tools, and MCP connections for structured inventory management. This enables comprehensive visibility into all AI elements powering applications beyond traditional software packages.

2. MCP Dependency Mapping specifically analyzes Model Context Protocol implementations to visualize connections between AI components and external services. It identifies MCP clients initiating connections, MCP servers providing tools/resources, and catalogs specific functions or data sources being accessed. The feature generates dependency chains showing application → mcp-client → mcp-server → tool relationships. This exposes previously hidden supply chain links to external AI services and data sources.

3. Compliance-Ready Outputs generate AI-BOMs in CycloneDX v1.6 JSON format for integration with existing security tooling. The optional HTML visualization provides interactive diagrams of component relationships and dependency hierarchies. Users can export results directly to files using the --json-file-output flag for audit trails and automated processing. These standardized outputs facilitate compliance documentation and security reviews for AI systems.

Problems Solved

1. The tool addresses critical visibility gaps in AI supply chains where traditional SBOMs fail to track model dependencies, dataset origins, and external service connections. Organizations struggle to inventory rapidly evolving AI components that introduce new attack surfaces and compliance risks. Without specialized tooling, security teams cannot assess risks from third-party AI services or verify license compliance for proprietary models.

2. Primary users include security leaders needing governance over AI implementations and engineering managers responsible for AI-powered application stacks. Compliance officers leverage it for regulatory audits of AI systems, while DevOps teams integrate it into CI/CD pipelines for continuous AI component monitoring. Data scientists benefit from automated dependency tracking in machine learning projects.

3. Typical scenarios include auditing production applications for unauthorized AI model usage, mapping dependencies on external LLM providers like OpenAI, and verifying compliance before product releases. Enterprises use it for vendor risk assessments of AI services and to maintain inventories for GDPR/CPAI-1000 compliance. Development teams integrate it into PR workflows to flag new AI dependencies.

Unique Advantages

1. Unlike generic SBOM tools, it specializes in AI-specific components including MCP connections, model dependencies, and dataset provenance that conventional scanners miss. The tool focuses exclusively on Python-based AI projects with deep analysis of AI frameworks and patterns rather than broad language coverage. It provides contextual insights about AI components rather than just listing package names and versions.

2. MCP protocol analysis represents groundbreaking innovation for mapping connections between AI systems and external tools/services. The HTML visualization capability uniquely illustrates dependency chains between applications, MCP clients, servers, and tools. Experimental feature status enables rapid iteration on emerging AI security challenges before industry standards solidify.

3. Competitive differentiation comes from Snyk's ecosystem integration, allowing AI-BOMs to complement existing vulnerability scans with AI-specific risk context. The CLI-based approach enables seamless adoption in developer workflows without new platforms. Early focus on MCP standardization ensures forward compatibility with evolving AI supply chain protocols.

Frequently Asked Questions (FAQ)

1. What prerequisites are needed to use Snyk AI-BOM? You must have Snyk CLI version 1.1298.3 or later installed and maintain an active internet connection during execution. Your project must be Python-based using package managers supported by Snyk, and you must authenticate using snyk config set org=<ORG_ID> or the --org flag.

2. How does AI-BOM handle Model Context Protocol dependencies? It analyzes source code to identify MCP clients initiating connections and MCP servers providing tools/resources. The tool categorizes these into dependency chains showing application → mcp-client → mcp-server → tool relationships. This exposes connections to external services that traditional dependency scanners would miss.

3. What output formats does the tool support? It generates standards-compliant CycloneDX v1.6 JSON files containing full AI component inventories. You can optionally create interactive HTML visualizations using the --html flag for dependency mapping. All outputs can be saved directly to files using the --json-file-output parameter for integration with other systems.