agent-bom

Open security scanner and self-hosted control plane for AI, MCP, and cloud infrastructure.

Start with the demo, then choose the entrypoint that matches your first job: repo scan, image scan, cloud posture, fix plan, dashboard, MCP tools, or runtime review.

better-sqlite3@9.0.0  (npm package)
  |── OSV/GHSA finding  (critical · advisory-backed)
  |── sqlite-mcp  (MCP Server · unverified · root)
       |── Cursor IDE  (Agent · 4 servers · 12 tools)
       |── ANTHROPIC_KEY, DB_URL, AWS_SECRET  (Credential env names visible)
       |── query_db, read_file, write_file, run_shell  (Reachable tools)

 Fix: upgrade better-sqlite3 → 11.7.0

Blast radius is the core idea: package -> vulnerability finding -> MCP server (tools + credential env names) -> connected agents. This schematic explains the model; emitted findings are backed by the configured advisory sources.

Scan local agent configs, MCP servers, instruction files, lockfiles, containers, cloud estate, AI models/datasets, non-human identities, LLM cost, GPU surfaces, and runtime evidence.

Try the built-in demo first:

agent-bom scan --demo --offline

The demo uses a curated sample so the output stays reproducible across releases. For real scans, run agent-bom scan, or add -p . to fold project manifests and lockfiles into the same result.

If you want an inspectable sample before scanning your own repo:

agent-bom samples first-run
agent-bom scan --inventory agent-bom-first-run/inventory.json -p agent-bom-first-run --enrich

The bundled first-run stack includes agent inventory, MCP server definitions, placeholder credential environment variable names, Python/npm manifests, and a prompt file. See docs/FIRST_RUN.md in the repository for the guided flow.

See the terminal demo

agent-bom demo

Recommended starting points

pip install agent-bom

agent-bom quickstart --dry-run --offline          # Scan, sample-data, and API/UI next steps
agent-bom scan -p .                            # Repo + MCP + package blast radius
agent-bom samples first-run                      # Inspectable sample AI stack
agent-bom check flask@2.2.0 --ecosystem pypi     # Pre-install package verdict
agent-bom image nginx:latest                     # Container image scan
agent-bom scan -p . --remediate remediation.md # Fix-first remediation plan
pip install 'agent-bom[ui]'                      # once, if you want the dashboard
agent-bom serve                                  # API + dashboard + graph explorer

The base wheel is the scanner/CLI path. Install optional surfaces explicitly: pip install 'agent-bom[mcp-server]' for MCP server mode and pip install 'agent-bom[ui]' for the local API/dashboard process. Use pip install 'agent-bom[all]' for supported first-run extras; MLflow remains separate until its upstream CVE backlog is fixed. If an extra is missing, the command exits with the matching install hint.

Self-hosted pilot:

curl -fsSL https://raw.githubusercontent.com/msaad00/agent-bom/main/deploy/docker-compose.pilot.yml -o docker-compose.pilot.yml
docker compose -f docker-compose.pilot.yml up -d
# Dashboard -> http://localhost:3000

Production chart from a checked-out repo:

helm upgrade --install agent-bom deploy/helm/agent-bom \
  --namespace agent-bom --create-namespace \
  -f deploy/helm/agent-bom/examples/eks-production-values.yaml

Product views

Dashboard

agent-bom dashboard overview

Agent mesh graph

agent-bom agent mesh graph

What it scans

  • Agents + MCP — MCP clients, servers, tools, transports, trust posture
  • Skills + instructions — CLAUDE.md, AGENTS.md, .cursorrules, .windsurfrules, skills/*
  • Package risk — supply-chain scanning across 15 package ecosystems (including mix.lock / pubspec.lock for Hex and Pub) with OSV/GHSA enrichment and blast radius
  • AI models + datasets — malicious-model detection via safe pickle-opcode disassembly (no execution), model/dataset cards, and target-scoped PII/PHI dataset-file scanning
  • Container images + IaC — native OCI parsing plus Dockerfile, Terraform, CloudFormation, Helm, and Kubernetes coverage
  • Cloud estate — read-only, gated asset inventory across AWS/Azure/GCP plus AI/GPU posture and CIS benchmarks
  • Identity (NHI) — non-human identity discovery (Okta/Entra), credential-expiry posture, and access-review campaigns
  • LLM cost — spend forecasting, budget runway, chargeback/allocation, and seasonal-aware spend-anomaly detection
  • Secrets + runtime — MCP proxy/gateway, inline firewall enforcement, A2A/MCP auth posture, Shield SDK, secrets, and redaction surfaces
  • Compliance + evidence — mapped governance plus ZIP evidence bundles for auditors

Key features

  • Blast radius + attack-path fusion — multi-hop exposure paths over one UnifiedGraph, from package → finding → MCP server → credentials → connected agents
  • CWE-aware impact — RCE shows credential exposure, DoS does not; symbol-level CVE reachability joins CWE/CVE/CPE advisory context when OSV/GHSA carry affected symbols (Python, npm, Go)
  • Portable outputs — SARIF, CycloneDX, SPDX, OCSF, HTML, graph, JSON, ZIP evidence bundles, and more
  • MCP server mode — 84 MCP tools, 6 resources, and 8 workflow prompts exposed to MCP clients like Claude, Cursor, Windsurf, and Cortex CoCo / Cortex Code
  • Skill bundle identity — stable bundle hashes for skill and instruction file review
  • Dependency confusion detection — flags internal naming patterns
  • VEX generation — auto-triage with CWE/CVE/CPE-aware reachability (package + function-level when advisory symbols exist)

Discovery and static/cloud scanning are read-only and agentless. Proxy, gateway, Shield, and control-plane write tools are separate, explicit runtime or authenticated operations. No secrets leave your machine unless you explicitly enable an outbound integration.

How the data moves

How agent-bom works

Blast radius

Blast radius

Links

Project links

Data verified by PyPI on Aug 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for agent-bom from gravatar.com agent-bom

Credits

PyPI data
Data sourced directly from PyPI's database.

Author: Wagdy Saad

GitHub Statistics

Data verified by PyPI on Aug 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.11

Provides Extra

api otel ui aws azure gcp coreweave databricks snowflake nebius huggingface wandb openai ai-enrich graph pdf postgres watch runtime visual mcp-server dashboard lake snyk interactive oidc saml cloud all docs dev dev-all

Tags

ai-bom sbom mcp mcp-server security ai-agents vulnerability supply-chain owasp mitre-atlas nist-ai-rmf blast-radius cve llm-security remediation mcp-introspection openclaw ai-enrichment credential-exposure config-security ai-supply-chain ai-infrastructure gpu-security cuda pytorch openssf-scorecard malicious-package-detection runtime-monitoring model-provenance

Classifiers

Report project as malware

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