agent-bom
agent-bom is a comprehensive AI supply chain security scanner and runtime enforcement MCP server for discovering, assessing, and remediating vulnerabilities across AI agent infrastructure, MCP servers, and dependencies.
Core Scanning & Discovery
scan– Full AI supply chain scan: auto-discovers MCP configs (Claude Desktop, Cursor, Windsurf, VS Code Copilot, etc.), extracts packages, queries OSV.dev for CVEs, assesses credential exposure, computes blast radius, and returns a structured report. Supports Docker image scanning, policy evaluation, SBOM ingestion, and NVD/EPSS/CISA KEV enrichment.inventory– Fast discovery and package extraction without CVE scanning; quick inventory of MCP configs, servers, packages, and transport types.where– List all MCP client config discovery paths and show which files exist on the current system.check– Check a specific package (npm, PyPI, Go, Cargo, Maven, NuGet) for known CVEs before installing, with severity, CVSS score, and fix version.
Risk Analysis
blast_radius– Map the full attack chain for a CVE: affected packages → MCP servers → agents → exposed credentials and tools.context_graph– Build an agent context graph with lateral movement analysis (BFS paths) to answer "if agent X is compromised, what else is reachable?"runtime_correlate– Cross-reference scan results with proxy runtime audit logs to identify which vulnerable tools were actually called in production.
Policy, Compliance & Remediation
policy_check– Evaluate security policy rules (severity thresholds, CISA KEV, AI risk flags, denied packages) against scan results; returns pass/fail with violations.compliance– Map findings to 47 controls across OWASP LLM Top 10, OWASP MCP Top 10, MITRE ATLAS, and NIST AI RMF with per-control status and an overall score.remediate– Generate actionable fix commands (npm/pip upgrades), credential scope reduction guidance, and flag unfixable vulnerabilities.cis_benchmark– Run CIS Foundations Benchmark checks against AWS (18 checks) or Snowflake (12 checks) with per-check pass/fail results.
Trust & Integrity
skill_trust– Assess SKILL.md/instruction files across 5 trust categories with a benign/suspicious/malicious verdict.verify– Verify package integrity via SHA-256/SRI hashes and SLSA build provenance attestations against npm/PyPI registries.marketplace_check– Pre-install trust check for an MCP server package: download count, CVE status, registry verification, and trust signals.registry_lookup– Query the built-in threat intelligence registry (109+ MCP servers) for risk level, known tools, credential requirements, and verification status.
Advanced Capabilities
generate_sbom– Generate a standards-compliant SBOM in CycloneDX 1.6 or SPDX 3.0 format.diff– Compare a fresh scan against a baseline to identify new/resolved vulnerabilities and package inventory changes.code_scan– Run SAST via Semgrep on source code to detect SQL injection, XSS, command injection, hardcoded credentials, and more.fleet_scan– Batch-scan a list of MCP server names against the security registry for fleet-wide risk assessment.analytics_query– Query vulnerability trends, posture history, and runtime event summaries from ClickHouse.
Additional features: real-time runtime enforcement proxy with behavioral attack pattern detection, MCP config drift watching, SIEM integration (Splunk, Datadog, Elasticsearch), output in JSON/SARIF/HTML/Mermaid formats, and AI-specific scanning for GPU/ML packages and model provenance (HuggingFace, Ollama, MLflow, W&B).
Scans AWS cloud infrastructure and Amazon Q configurations to identify security vulnerabilities and ensure compliance with CIS benchmarks.
Integrates with ClickHouse to provide security scan analytics, visualization, and posture scoring for AI infrastructure.
Performs security scanning of Databricks environments to detect misconfigurations and dependency vulnerabilities.
Scans Docker images and Docker-based MCP servers for security risks, tool poisoning, and dependency vulnerabilities.
Integrates as a CI/CD gate to automate security scans and enforce compliance policies during the development lifecycle.
Supports deployment and fleet-wide security scanning of AI agent infrastructure within Kubernetes using Helm charts.
Discovers and analyzes JetBrains AI configurations to identify potential credential leaks and security risks.
Enables dispatching security alerts and vulnerability findings to Jira for incident management and remediation tracking.
Scans Kubernetes clusters to map vulnerability propagation and assess the security posture of AI agent deployments.
Discovers and scans MLflow platforms to identify security risks and verify the provenance of AI models.
Provides integration with OpenTelemetry for monitoring and tracing the security scan pipeline and execution.
Dispatches real-time security alerts and scan reports to Slack channels via webhooks for immediate notification.
Provides governance and security scanning for Snowflake instances, including compliance checks against CIS Snowflake benchmarks.
Generates standardized Software Bill of Materials (SBOM) reports in the SPDX format for security compliance and transparency.
Analyzes security risks and maps the blast radius for AI agent tools and MCP servers utilizing SQLite databases.
What it is
agent-bom is an open scanner and self-hosted control plane for software,
cloud, identity, AI-agent, and MCP evidence. One Finding + UnifiedGraph model
powers CLI and CI artifacts, fleet and browser investigations, compliance
evidence, and runtime policy.
Use the scanner without an account, or deploy the shared control plane inside your own cloud, VPC, Kubernetes cluster, database, identity, and audit boundary.
Graph provenance remains explicit: collected, inferred, static, and runtime relationships stay distinct, and unavailable evidence is never upgraded to observed.
Related MCP server: agent-audit
Who it is for
Role | Start here | Primary outcome |
Developers |
| Find and explain issues before code leaves the workstation |
AppSec |
| Triage reachable findings and enforce CI gates |
Security engineers |
| Investigate exposure paths, identities, and evidence provenance |
Platform / SRE |
| Centralize estate inventory, jobs, and runtime controls |
GRC / audit |
| Review control mappings and export evidence with explicit gaps |
Leadership / CISO |
| Review posture, coverage, material risk, and change over time |
AI / MCP owners |
| Inventory tools and apply allow, warn, or block decisions |
AppSec and GRC remain separate workflows: findings and reachability are not presented as audit certification. See product boundaries.
The gallery uses deterministic sample data, visibly labeled in the UI. It is product-state proof, not customer or advisory evidence.
Overview | Findings |
Investigation | Remediation |
Cloud and environment lineage | Agent mesh |
Quick start
Run against the repository in your current directory:
pip install agent-bom
agent-bom scan .The console shows inventory, findings, and reachable impact. Save an artifact
with agent-bom scan . -f sarif -o findings.sarif, or follow the
first-run guide for exit codes, formats, and CI use.
Use the curated, explicitly synthetic sample when you only want to inspect the output shape:
agent-bom scan --demo --offlineThe sample intentionally contains blocking findings, so exit status 1 is
expected.
Self-host
Start the loopback control plane:
pip install 'agent-bom[ui]'
agent-bom serveFor a shared deployment, use the documented Docker or Helm path and configure real identity, TLS, PostgreSQL, encryption, and audit keys before exposing it.
Target | Start here |
Docker Compose | |
Helm / Kubernetes |
|
EKS | |
Snowflake SPCS / Native App |
|
Air-gapped |
Examples target this release candidate; confirm release availability before copying an exact pin. Otherwise, use the latest version shown on PyPI.
Deployment overview · Enterprise configuration · Cloud connections
Need | First action | Artifact or next step |
GitHub CI |
| SARIF, PR summary, and a policy exit code |
Cloud evidence |
| Stored connection reference; run scans from the control plane |
Runtime gateway |
| Allow, warn, and block audit events |
Agent interface |
| 77 MCP tools, 6 resources, and 8 workflow prompts |
Agent distribution | Registry-specific installation metadata |
MCP server mode exposes 77 MCP tools, 6 resources, and 8 workflow prompts, all read-first: discovery and analysis never mutate a scanned target.
The CLI, Docker, API, Helm chart, MCP server, gateway, and SDK are distribution surfaces of the same product. The Snowflake SPCS / Native App lane runs inside the customer's Snowflake account; it is a customer-owned deployment target, not an agent-bom-hosted service. Snowflake and Snowpark also remain connector and runtime integrations for the other deployment profiles.
Surface | Get it |
Python package |
|
Container |
|
Kubernetes |
|
GitHub Action | |
MCP server |
|
MCP registries | |
SDKs | Python · TypeScript · Go |
Trust
Read-only discovery by default; runtime write decisions are separate and explicit.
Credentials are write-only where stored, encrypted at rest, and never returned by API responses.
API and control-plane routes are tenant scoped and auth protected outside explicit local mode.
Missing evidence is shown as unavailable or partial, never converted into a factual zero.
Public examples and screenshots use deterministic synthetic identifiers only.
Threat model · Release verification · Security policy · MCP security model
Contributing
Start with CONTRIBUTING.md, AGENTS.md, and the open issues.
Apache-2.0 licensed.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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