ai-bom 0.1.0

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  |  What It Finds  |  Demo  |  Output Formats  |  n8n Scanning  |  Risk Scoring

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_{Scan your entire infrastructure in seconds}

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Why AI-BOM?

Shadow AI is the new Shadow IT. Developers are integrating AI services — LLMs, agents, embeddings, MCP servers — without security review. Organizations face real compliance gaps:

• EU AI Act (Article 53, Aug 2025) — requires a complete AI component inventory
• NIST AI Agent Security (Jan 2026) — mandates agent trust boundaries
• 60%+ of AI usage is undocumented — shadow AI is everywhere
• No existing tool scans n8n workflows for AI — until now

ai-bom is a single CLI that scans source code, Docker configs, cloud infrastructure, network endpoints, and n8n workflows — then produces a standards-compliant AI Bill of Materials.

One command. Complete visibility.

Quick Start

pip install ai-bom

ai-bom scan .

ai-bom scan . --format cyclonedx --output ai-bom.json

What It Finds

Category	Examples	Scanner
LLM Providers	OpenAI, Anthropic, Google AI, Mistral, Cohere, Ollama	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, NVIDIA, ChromaDB	Docker
Cloud AI	AWS Bedrock, SageMaker, Vertex AI, Azure Cognitive	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 connections	Code, n8n

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

Demo

ai-bom demo

Runs a scan on the bundled demo project showcasing all detection capabilities:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  AI-BOM Discovery Scanner by Trusera
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

✓ Running code scanner...       done
✓ Running docker scanner...     done
✓ Running network scanner...    done
✓ Running cloud scanner...      done
✓ Running n8n scanner...        done

Found 40 AI/LLM component(s)

┌──────────────────────┬────────────────────┬──────┬──────────┐
│ Component            │ Type               │ Risk │ Severity │
├──────────────────────┼────────────────────┼──────┼──────────┤
│ OpenAI SDK           │ LLM Provider       │   30 │ CRITICAL │
│ Anthropic SDK        │ LLM Provider       │   25 │ HIGH     │
│ LangChain            │ Agent Framework    │   20 │ HIGH     │
│ gpt-4o               │ Model Reference    │   15 │ MEDIUM   │
│ AI Agent Node        │ n8n AI Node        │   30 │ CRITICAL │
│ MCP Client           │ n8n MCP            │   25 │ HIGH     │
│ Ollama Container     │ AI Container       │   10 │ MEDIUM   │
│ ...                  │                    │      │          │
└──────────────────────┴────────────────────┴──────┴──────────┘

Output Formats

Table (default)

ai-bom scan .

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

CycloneDX 1.6

ai-bom scan . --format cyclonedx --output ai-bom.cdx.json

Industry-standard SBOM format compatible with OWASP Dependency-Track and other SBOM tools. Includes Trusera-specific properties for AI risk metadata.

HTML Dashboard

ai-bom scan . --format html --output report.html

Self-contained dark-mode dashboard with sortable tables, severity charts, and risk breakdowns. Share with stakeholders — no server required.

Markdown

ai-bom scan . --format markdown --output report.md

GitHub-flavored markdown for CI/CD integration, pull request comments, and documentation.

n8n Workflow Scanning — First of Its Kind

ai-bom is the first and only tool that scans n8n workflows for AI components.

n8n is rapidly becoming the backbone of enterprise AI automation, but existing security tools are completely blind to it. ai-bom detects:

• AI Agent nodes and their connected models
• MCP client connections to external servers
• Webhook triggers without authentication
• Agent-to-agent chains via Execute Workflow
• Dangerous tool combinations (Code + HTTP Request)
• Hardcoded credentials in workflow JSON

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

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

n8n Risk Factors

Risk	Score	Description
Hardcoded credentials	+30	API keys in workflow JSON instead of credential store
Code + HTTP tools	+30	Agent can execute code AND make HTTP requests
Webhook no auth	+25	Webhook trigger without authentication
MCP unknown server	+20	MCP client connected to non-localhost server
Agent chain no validation	+20	Agent-to-agent execution without input validation

Risk Scoring

Every component receives a risk score (0–100):

Severity	Score Range	Color
Critical	76–100	Red
High	51–75	Yellow
Medium	26–50	Blue
Low	0–25	Green

Risk Factors

Factor	Points	Description
Hardcoded API key	+30	API key found in source code
Shadow AI	+25	AI dependency not declared in project files
Internet-facing	+20	AI endpoint exposed to internet
Multi-agent no trust	+20	Multi-agent system without trust boundaries
No authentication	+15	AI endpoint without auth
No rate limiting	+10	No rate limiting on AI endpoint
Deprecated model	+10	Using deprecated AI model
Unpinned model	+5	Model version not pinned

Comparison

How does ai-bom compare to existing supply chain 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
SARIF output (GitHub Code Scanning)	Yes	Yes	No	No
Single-file scanning	Yes	Yes	Yes	No
Git URL scanning (auto-clone)	Yes	Yes	No	No
CycloneDX SBOM output	Yes	Yes	Yes	No
Docker AI container detection	Yes	Partial	Partial	No
Cloud AI service detection	Yes	No	No	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.

How It Works

src/ai_bom/
├── cli.py              # Typer CLI entry point
├── config.py           # Detection patterns as data
├── models.py           # Pydantic v2 data models
├── scanners/           # Auto-registered scanner plugins
│   ├── code_scanner    # Source code analysis (21+ SDKs, 7 languages)
│   ├── docker_scanner  # Container image detection
│   ├── network_scanner # Endpoint & API key detection
│   ├── cloud_scanner   # Terraform / CloudFormation
│   └── n8n_scanner     # n8n workflow analysis
├── detectors/          # Pattern registries
│   ├── llm_patterns    # SDK import/usage patterns
│   ├── model_registry  # Known model database
│   └── endpoint_db     # API endpoint patterns
├── reporters/          # Output formatters
│   ├── cli_reporter    # Rich terminal output
│   ├── cyclonedx       # CycloneDX 1.6 JSON
│   ├── sarif           # SARIF 2.1.0 for GitHub Code Scanning
│   ├── html_reporter   # Self-contained dashboard
│   └── markdown        # GFM report
└── utils/
    └── risk_scorer     # Stateless risk engine

Scanners auto-register via __init_subclass__. Adding a new scanner is a single file — no wiring needed.

Development

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

# Run tests (94 passing)
pytest tests/ -v

# Run demo
ai-bom demo

CLI Reference

Usage: ai-bom [OPTIONS] COMMAND [ARGS]...

Commands:
  scan     Scan a directory or repository for AI/LLM components
  demo     Run demo scan on bundled example project
  version  Show AI-BOM version

Scan Options:
  --format, -f     Output format: table | cyclonedx | json | html | markdown | sarif
  --output, -o     Write report to file
  --severity, -s   Minimum severity: critical | high | medium | low
  --include-tests  Include test directories in scan
  --n8n-local      Scan ~/.n8n/ directory for workflows
  --no-color       Disable colored output

Roadmap

• Multi-language AI SDK detection (Python, JS, TS, Java, Go, Rust, Ruby)
• CycloneDX 1.6 SBOM output
• n8n workflow scanning
• MCP server detection
• HTML dashboard reports
• Risk scoring engine
• AST-based scanning for deeper analysis
• Live n8n API integration (scan running instances)
• SARIF output format (GitHub Code Scanning integration)
• GitHub Actions marketplace action (trusera/ai-bom@v1)
• Single-file scanning
• VS Code extension
• CI/CD policy enforcement (fail builds on critical findings)

Contributing

Contributions are welcome! ai-bom is open source and we'd love your help making it better.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-scanner)
3. Write tests for your changes
4. Ensure all tests pass (pytest tests/ -v)
5. Submit a pull request

Whether it's a new scanner, additional detection patterns, bug fixes, or documentation improvements — all contributions are appreciated.

License

Apache License 2.0 — see LICENSE for details.

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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.}