ai-bom 3.1.0

AI Bill of Materials — discover and inventory all AI/LLM agents, models, and API integrations across your infrastructure.

AI-BOM

Discover every AI agent, model, and API hiding in your infrastructure

Quick Start  |  n8n Node  |  What It Finds  |  Comparison  |  Architecture  |  Output Formats  |  CI/CD  |  Scan Levels  |  Dashboard

_{Scan all your n8n AI workflows for security risks — directly inside n8n}

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Why AI-BOM?

• EU AI Act (Article 53, Aug 2025) requires a complete AI component inventory. No existing SBOM tool covers AI.
• 60%+ of AI usage is undocumented — shadow AI is the new shadow IT. Developers ship LLM integrations, agent frameworks, and MCP servers without security review.
• First tool to scan n8n workflows for AI — n8n is the backbone of enterprise AI automation, but completely invisible to Trivy, Syft, and Grype.

One command. 13 scanners. 9 output formats. Standards-compliant AI Bill of Materials.

Quick Start

pipx install ai-bom
ai-bom scan .

That's it. Scans your project and prints a risk-scored inventory of every AI component found.

# CycloneDX SBOM for compliance
ai-bom scan . -f cyclonedx -o ai-bom.cdx.json

# SARIF for GitHub Code Scanning
ai-bom scan . -f sarif -o results.sarif

# Fail CI on critical findings
ai-bom scan . --fail-on critical --quiet

Alternative: Install in a virtual environment

python3 -m venv .venv && source .venv/bin/activate
pip install ai-bom
ai-bom scan .

Troubleshooting: PEP 668 / "externally-managed-environment" error

Modern Linux distros (Ubuntu 24.04+) and macOS 14+ block pip install at the system level. Use pipx (recommended) or a venv as shown above.

sudo apt install pipx   # Debian/Ubuntu
brew install pipx        # macOS
pipx install ai-bom

Alternative: Run with Docker

docker run --rm -v $(pwd):/scan ghcr.io/trusera/ai-bom scan /scan

# CycloneDX output
docker run --rm -v $(pwd):/scan ghcr.io/trusera/ai-bom scan /scan -f cyclonedx -o /scan/ai-bom.cdx.json

# JSON output piped to jq
docker run --rm -v $(pwd):/scan ghcr.io/trusera/ai-bom scan /scan --json | jq '.components[] | select(.properties[]? | select(.name == "trusera:risk_score" and (.value | tonumber) > 7))'

The image is published to ghcr.io/trusera/ai-bom on every tagged release.

n8n Community Node

Scan all your n8n workflows for AI security risks — directly inside n8n. One node, full dashboard.

Install: Settings > Community Nodes > n8n-nodes-trusera

Setup (1 minute)

1. Add the Trusera Webhook node to a workflow
2. Add your n8n API credential (Settings > n8n API > Create API Key)
3. Activate the workflow
4. Visit http://your-n8n-url/webhook/trusera

That's it. The node fetches all workflows, scans them, and serves an interactive HTML dashboard.

Included Nodes

Node	Purpose
Trusera Webhook	One-node dashboard at /webhook/trusera (recommended)
Trusera Dashboard	Chain with built-in Webhook for custom setups
Trusera Scan	Programmatic scanning — returns JSON for CI/CD pipelines
Trusera Policy	Security gates — pass/fail against configurable policies
Trusera Report	Markdown/JSON reports for Slack, email, or docs

Dashboard features

• Severity distribution charts, component type breakdown, and OWASP LLM Top 10 mapping
• Scanned workflows table with trigger type, component count, and risk severity
• Sortable findings table with search, severity/type/workflow filters
• Per-finding remediation cards with actionable fix steps
• CSV and JSON export
• Light/dark theme toggle
• Optional password protection (AES-256-GCM encrypted, client-side decryption)

What It Finds

Category	Examples	Scanner
LLM Providers	OpenAI, Anthropic, Google AI, Mistral, Cohere, Ollama, DeepSeek	Code
Agent Frameworks	LangChain, CrewAI, AutoGen, LlamaIndex, LangGraph	Code
Model References	gpt-4o, claude-3-5-sonnet, gemini-1.5-pro, llama-3	Code
API Keys	OpenAI (sk-*), Anthropic (sk-ant-*), HuggingFace (hf_*)	Code, Network
AI Containers	Ollama, vLLM, HuggingFace TGI, NVIDIA Triton, ChromaDB	Docker
Cloud AI	AWS Bedrock/SageMaker | Azure OpenAI/ML | Google Vertex AI	Cloud
AI Endpoints	api.openai.com, api.anthropic.com, localhost:11434	Network
n8n AI Nodes	AI Agents, LLM Chat, MCP Client, Tools, Embeddings	n8n
MCP Servers	Model Context Protocol server configurations	Code, MCP Config
A2A Protocol	Google Agent-to-Agent protocol	Code
CrewAI Flows	@crew, @agent, @task, @flow decorators	Code, AST
Jupyter Notebooks	AI imports and model usage in .ipynb files	Jupyter
GitHub Actions	AI-related actions and model deployments	GitHub Actions
Model Files	.gguf, .safetensors, .onnx, .pt binary model files	Model File

25+ AI SDKs detected across Python, JavaScript, TypeScript, Java, Go, Rust, and Ruby.

Comparison

How does ai-bom compare to existing supply chain security tools?

Feature	ai-bom	Trivy	Syft	Grype
AI/LLM SDK detection	Yes	No	No	No
AI model references	Yes	No	No	No
Agent framework detection	Yes	No	No	No
n8n workflow scanning	Yes	No	No	No
MCP server detection	Yes	No	No	No
AI-specific risk scoring	Yes	No	No	No
Cloud AI service detection	Yes	No	No	No
Jupyter notebook scanning	Yes	No	No	No
CycloneDX SBOM output	Yes	Yes	Yes	No
SARIF output (GitHub)	Yes	Yes	No	No
Docker AI container detection	Yes	Partial	Partial	No
CVE vulnerability scanning	No	Yes	No	Yes
OS package scanning	No	Yes	Yes	Yes

ai-bom doesn't replace Trivy or Syft — it fills the AI-shaped gap they leave behind.

Architecture

graph LR
    subgraph Input
        A[Source Code] --> S
        B[Docker/K8s] --> S
        C[Network/Env] --> S
        D[Cloud IaC] --> S
        E[n8n Workflows] --> S
        F[Jupyter/.ipynb] --> S
        G[MCP Configs] --> S
        H[GitHub Actions] --> S
        I[Model Files] --> S
    end

    S[Scanner Engine<br/>13 Auto-Registered Scanners] --> M[Pydantic Models<br/>AIComponent + ScanResult]
    M --> R[Risk Scorer<br/>0-100 Score + Severity]
    R --> C2[Compliance Modules<br/>EU AI Act, OWASP, Licenses]

    subgraph Output
        C2 --> O1[CycloneDX 1.6]
        C2 --> O2[SARIF 2.1.0]
        C2 --> O3[SPDX 3.0]
        C2 --> O4[HTML Dashboard]
        C2 --> O5[Markdown / CSV / JUnit]
        C2 --> O6[Rich Terminal Table]
    end

Key design decisions:

• Scanners auto-register via __init_subclass__ — add a new scanner in one file, zero wiring
• Regex-based detection (not AST by default) for speed and cross-language support
• CycloneDX 1.6 JSON generated directly from dicts — no heavy dependencies
• Risk scoring is a pure stateless function
• Parallel scanner execution via thread pool

Output Formats

Table (default)

ai-bom scan .

Rich terminal output with color-coded severity, risk scores, and component grouping.

CycloneDX 1.6

ai-bom scan . -f cyclonedx -o ai-bom.cdx.json

Industry-standard SBOM format. Compatible with OWASP Dependency-Track. Includes Trusera AI risk properties.

Example output snippet

{
  "bomFormat": "CycloneDX",
  "specVersion": "1.6",
  "components": [
    {
      "type": "library",
      "name": "openai",
      "version": "1.x",
      "properties": [
        { "name": "trusera:ai-bom:risk-score", "value": "45" },
        { "name": "trusera:ai-bom:severity", "value": "medium" }
      ]
    }
  ]
}

SARIF 2.1.0

ai-bom scan . -f sarif -o results.sarif

Upload to GitHub Code Scanning for inline annotations on AI components.

Other formats

Format	Flag	Use case
HTML	-f html	Shareable dashboard — no server required
Markdown	-f markdown	PR comments, documentation
SPDX 3.0	-f spdx3	SPDX-compatible with AI extensions
CSV	-f csv	Spreadsheet analysis
JUnit	-f junit	CI/CD test reporting
JSON	-f json	Alias for CycloneDX

CI/CD Integration

GitHub Actions (recommended)

Use the official AI-BOM GitHub Action for one-line CI/CD integration:

name: AI-BOM Scan
on: [push, pull_request]
permissions:
  security-events: write
  contents: read

jobs:
  ai-bom:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Scan for AI components
        uses: trusera/ai-bom@main
        with:
          format: sarif
          output: ai-bom-results.sarif
          fail-on: critical
          scan-level: deep

The action handles Python setup, ai-bom installation, and automatic SARIF upload to GitHub Code Scanning.

See .github/workflows/ai-bom-example.yml for more examples (CycloneDX SBOM, policy gates, artifact uploads).

Manual setup (without the action)

name: AI-BOM Scan
on: [push, pull_request]

jobs:
  ai-bom:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install AI-BOM
        run: pipx install ai-bom

      - name: Scan for AI components
        run: ai-bom scan . --fail-on critical --quiet -f sarif -o results.sarif

      - name: Upload SARIF
        uses: github/codeql-action/upload-sarif@v3
        with:
          sarif_file: results.sarif
        if: always()

Policy enforcement

# Fail CI if any critical findings
ai-bom scan . --fail-on critical --quiet

# Use a YAML policy file for fine-grained control
ai-bom scan . --policy .ai-bom-policy.yml --quiet

# .ai-bom-policy.yml
max_critical: 0
max_high: 5
max_risk_score: 75
block_providers: []
block_flags:
  - hardcoded_api_key
  - hardcoded_credentials

Scan Levels

ai-bom's detection depth depends on the access available at scan time:

Level	Access Required	What It Finds	Scanner
L1 — File System	Read-only file access	Source code imports, configs, IaC, n8n JSON, notebooks	Code, Cloud, n8n, Jupyter, MCP Config
L2 — Docker	+ Docker socket	Running AI containers, GPU allocations	Docker
L3 — Network	+ Env files	API endpoints, hardcoded keys, .env secrets	Network
L4 — Cloud IaC	+ Terraform/CFN files	60+ AWS/Azure/GCP AI resource types	Cloud
L5 — Live Cloud	+ Cloud credentials	Managed AI services via cloud APIs	AWS/GCP/Azure Live

# L1 (default) — works out of the box
ai-bom scan .

# L5 — live cloud scanning
pip install ai-bom[aws]
ai-bom scan-cloud aws

Deep scanning (AST mode)

ai-bom scan . --deep

Enables Python AST analysis for decorator patterns (@agent, @tool, @crew, @flow), function calls to AI APIs, and string literals containing model names.

Dashboard

pip install ai-bom[dashboard]

ai-bom scan . --save-dashboard   # Save scan results
ai-bom dashboard                  # Launch at http://127.0.0.1:8000

The web dashboard provides:

• Scan history with timestamps, targets, and component counts
• Drill-down into individual scans with sortable component tables
• Severity distribution charts and risk score visualizations
• Side-by-side scan comparison (diff view)

n8n workflow scanning

# Scan workflow JSON files
ai-bom scan ./workflows/

# Scan local n8n installation
ai-bom scan . --n8n-local

# Scan running n8n instance via API
ai-bom scan . --n8n-url http://localhost:5678 --n8n-api-key YOUR_KEY

Detects AI Agent nodes, MCP client connections, webhook triggers without auth, dangerous tool combinations, and hardcoded credentials in workflow JSON.

Contributing

See CONTRIBUTING.md for development setup and guidelines.

git clone https://github.com/trusera/ai-bom.git && cd ai-bom
pip install -e ".[dev]"
pytest tests/ -v

Quality gates enforced:

• ruff (E,F,I,W,S,B,C4,UP,SIM,N,RUF) — zero lint errors
• mypy strict (disallow_untyped_defs = true) — zero type errors
• pytest — 651 tests, 80%+ coverage required

License

Apache License 2.0 — see LICENSE for details.

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Star History

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Built by Trusera — Securing the Agentic Service Mesh
_{ai-bom is the open-source foundation of the Trusera platform for AI agent security.}