ai-agentbom 0.5.1

Offline AI agent bill of materials and attack surface scanner

AgentBOM

Offline bill of materials and attack surface scanner for AI agent repositories.

AgentBOM helps reviewers answer a practical question: what AI providers, models, frameworks, prompts, MCP configuration, and sensitive capabilities exist in this agent repository, and which of those capabilities appear reachable from an AI actor?

It does not execute scanned code, import scanned modules, read secret values, or require network access.

Quickstart

Install from PyPI:

pip install ai-agentbom

Scan a repository:

agentbom scan . --pretty

Generate the shareable reports most teams want in review:

agentbom scan . --output-dir agentbom-report --html --mermaid --sarif --pretty

Typical output:

Wrote agentbom-report/agentbom.json
Wrote agentbom-report/agentbom.md
Wrote agentbom-report/agentbom.html
Wrote agentbom-report/agentbom.mmd
Wrote agentbom-report/agentbom.sarif
Risk: high (70/100)

Why AgentBOM

AI agents combine model output with software capabilities. A dependency list or general static analyzer can tell you that code imports a package or calls a risky API, but it usually does not explain whether an agent framework, model, prompt, or MCP configuration appears connected to that capability.

AgentBOM makes that review repeatable:

• maps AI-specific components: providers, models, frameworks, prompts, and MCP configuration
• connects agent actors to reachable capabilities such as shell, code execution, network, database, cloud, and MCP tool invocation
• records source paths, confidence, and rationale for review
• produces deterministic JSON plus human-readable Markdown and HTML
• exports Mermaid for architecture review and SARIF for GitHub code scanning
• runs offline with zero telemetry and no runtime dependencies

Findings are review signals, not exploit claims.

AgentBOM vs. Traditional SAST

AgentBOM is not a replacement for SAST. It is a focused companion for AI agent review.

Question	Traditional SAST	AgentBOM
Is there a risky API call?	Yes	Yes, at a coarse deterministic level
Which AI provider or model is present?	Usually no	Yes
Which agent framework may route tool calls?	Usually no	Yes
Are prompt or MCP surfaces present?	Usually no	Yes
Can an AI actor appear to reach a capability?	Usually no	Yes
Does it work offline without executing code?	Depends on tool	Yes
Is output designed for AI governance evidence?	Usually no	Yes

Use SAST for language-specific vulnerability analysis. Use AgentBOM to explain the AI agent bill of materials and attack surface.

Demo Repositories

AgentBOM includes realistic static demos under examples/:

• examples/customer-support-agent: a controlled support agent with documented human approval and policy controls
• examples/research-agent: an intentionally riskier research agent with reachable shell/network behavior and missing policy documentation

Run both demos:

agentbom scan examples/customer-support-agent --output-dir agentbom-report/support --html --mermaid --sarif --pretty
agentbom scan examples/research-agent --output-dir agentbom-report/research --html --mermaid --sarif --pretty

See the demo workflow for a repeatable walkthrough.

What It Finds

Area	Examples
Providers	OpenAI, Anthropic, Gemini
Models	gpt-4o, gpt-4.1, claude-3-sonnet, gemini-2.0-flash
Frameworks	LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel
MCP	mcp.json, claude_desktop_config.json
Prompts	AGENTS.md, CLAUDE.md, prompts/*.md, prompt YAML
Capabilities	shell, code execution, network, database, cloud, MCP tool invocation
Policy gaps	prompt files, MCP config, shell/cloud access without policy documentation
Secret references	credential names such as OPENAI_API_KEY, never values

Reports

AgentBOM always writes:

• agentbom.json: machine-readable findings
• agentbom.md: human-readable review report

Optional reports:

Flag	Output	Use
--html	agentbom.html	self-contained offline report for humans
--mermaid	agentbom.mmd	GitHub-native attack surface graph
--sarif	agentbom.sarif	GitHub code scanning and SARIF consumers
--cyclonedx	agentbom.cdx.json	package ecosystem inventory workflows

The report guide explains how to read the findings: docs/report-guide.md.

Architecture

AgentBOM uses a deterministic static-analysis pipeline:

flowchart LR
  files[Repository files] --> filter[Text and size filter]
  filter --> detectors[Pattern detectors]
  detectors --> reachability[Reachability inference]
  reachability --> risk[Risk and policy scoring]
  risk --> reports[JSON, Markdown, HTML, Mermaid, SARIF]

Core concepts:

• Providers: AI service vendors or runtime providers.
• Models: concrete model identifiers found in code or configuration.
• Frameworks: agent and orchestration libraries.
• Capabilities: static evidence of sensitive actions.
• Reachable capabilities: actor-to-capability relationships with risk and confidence.
• Policy findings: missing controls or custom policy violations.

See ARCHITECTURE.md for implementation details.

GitHub Action

Use the bundled action to run AgentBOM in pull requests, upload SARIF, and keep the HTML/JSON/Markdown reports as workflow artifacts.

name: AgentBOM

on:
  pull_request:
  push:
    branches: [main]

permissions:
  contents: read
  security-events: write

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

      - name: Run AgentBOM
        uses: vlcak27/agentbom@v0.5.0
        with:
          path: .
          fail-on: high
          sarif-upload: true
          html: true
          output-dir: agentbom-report

      - name: Upload AgentBOM reports
        uses: actions/upload-artifact@v4
        with:
          name: agentbom-report
          path: agentbom-report/

More details: docs/github-action.md.

CLI Reference

agentbom --help
agentbom scan --help

Common commands:

agentbom scan /path/to/agent-repo --pretty
agentbom scan /path/to/agent-repo --output-dir agentbom-report --html
agentbom scan /path/to/agent-repo --output-dir agentbom-report --mermaid
agentbom scan /path/to/agent-repo --output-dir agentbom-report --sarif
agentbom scan /path/to/agent-repo --policy agentbom-policy.yaml --sarif --pretty

Example policy:

deny_capabilities:
  - shell_execution
  - autonomous_execution

require:
  sandboxing: true
  human_approval: true

Output Example

Simplified JSON:

{
  "schema_version": "0.1.0",
  "repository": "examples/research-agent",
  "providers": [
    {"name": "anthropic", "path": "agent.py", "confidence": "high"}
  ],
  "frameworks": [
    {"name": "crewai", "path": "agent.py", "confidence": "high"}
  ],
  "capabilities": [
    {"name": "shell", "path": "agent.py", "confidence": "high"}
  ],
  "reachable_capabilities": [
    {
      "capability": "code_execution",
      "reachable_from": "crewai",
      "source_file": "agent.py",
      "risk": "high",
      "confidence": "high",
      "confidence_score": 100,
      "paths": ["shell_execution"]
    }
  ],
  "repository_risk": {
    "score": 100,
    "severity": "critical",
    "rationale": [
      "high-risk reachable capability detected: autonomous_execution, code_execution",
      "autonomous execution is present or reachable",
      "shell or code execution is present or reachable"
    ]
  }
}

Secret values are not stored or printed. Secret findings record names such as OPENAI_API_KEY so reviewers can see which credentials are referenced without exposing the values.

Security Model

AgentBOM is designed for safe repository review:

• does not execute scanned code
• does not import scanned modules
• does not evaluate project plugins or dynamic configuration
• skips files larger than 1 MB
• skips binary-looking files
• does not follow symlink loops
• records secret names only, never secret values
• works offline
• emits deterministic output for the same input repository

Static analysis is intentionally conservative. Results should be reviewed by a human before being treated as a security decision.

Development

Contributor and security docs:

• CONTRIBUTING.md
• SECURITY.md
• CHANGELOG.md

Install in editable mode:

pip install -e ".[dev]"

Run tests and linting:

ruff check .
pytest

Or use:

make check
make demo

Scan the demo repository:

agentbom scan examples/research-agent --output-dir agentbom-report --html --mermaid --sarif --pretty

Repository Structure

.
|-- src/agentbom/              # CLI, scanner, detectors, reports, exports
|-- tests/                     # pytest coverage for scanner and outputs
|-- docs/                      # report guide, demo workflow, schemas, assets
|-- examples/                  # demo repositories for scans
|-- .github/                   # workflows, issue templates, release templates
|-- action.yml                 # reusable GitHub Action definition
|-- ARCHITECTURE.md            # scanner design notes
|-- ROADMAP.md                 # planned improvements
|-- SPEC.md                    # project specification
`-- pyproject.toml             # package metadata and dev tooling

Roadmap

Near-term improvements focus on better package/config parsing, more detector coverage, deeper MCP analysis, and clearer policy validation while preserving offline operation, deterministic output, zero telemetry, and minimal dependencies.

See ROADMAP.md.