agent-bom

AI 与智能体

by msaad00

面向 AI 供应链安全的扫描器,支持 CVE 扫描、blast radius 分析、策略执行与 SBOM 生成。

什么是 agent-bom

面向 AI 供应链安全的扫描器,支持 CVE 扫描、blast radius 分析、策略执行与 SBOM 生成。

README

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/logo-dark.svg"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/logo-light.svg" alt="agent-bom" width="360" /> </picture> </p> <p align="center"> <a href="https://github.com/msaad00/agent-bom/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/msaad00/agent-bom/ci.yml?branch=main&style=flat&label=Build" alt="Build"></a> <a href="https://pypi.org/project/agent-bom/"><img src="https://img.shields.io/pypi/v/agent-bom?style=flat&label=Latest%20version&cacheSeconds=300" alt="PyPI"></a> <a href="https://hub.docker.com/r/agentbom/agent-bom"><img src="https://img.shields.io/docker/pulls/agentbom/agent-bom?style=flat&label=Docker%20pulls" alt="Docker"></a> <a href="https://github.com/msaad00/agent-bom/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache%202.0-blue?style=flat" alt="License"></a> <a href="https://securityscorecards.dev/viewer/?uri=github.com/msaad00/agent-bom"><img src="https://img.shields.io/ossf-scorecard/github.com/msaad00/agent-bom?style=flat&label=OpenSSF%20scorecard" alt="OpenSSF Scorecard"></a> </p> <!-- mcp-name: io.github.msaad00/agent-bom --> <p align="center"><b>Open security scanner and self-hosted control plane for AI/MCP infrastructure.</b></p> <p align="center">Headless agent primitives and human cockpit surfaces over the same evidence model.</p> <p align="center"> <a href="https://msaad00.github.io/agent-bom/">Docs</a> · <a href="docs/FIRST_RUN.md">First Run</a> · <a href="site-docs/deployment/overview.md">Self-host</a> · <a href="https://github.com/marketplace/actions/agent-bom">GitHub Action</a> · <a href="https://hub.docker.com/r/agentbom/agent-bom">Docker</a> · <a href="https://github.com/msaad00/agent-bom/releases">Changelog</a> </p>

agent-bom scans local and fleet AI infrastructure, builds an AI BOM across agents, MCP servers, tools, packages, credential environment names, cloud, runtime, and skills, then turns that inventory into findings, compliance evidence, and graph-backed exposure paths.

The same evidence is available through CLI/CI, REST API, MCP tools, and a self-hosted dashboard. Runtime proxy/gateway controls are optional and scoped to environments where enforcement is worth the operational cost.

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/blast-radius-dark.svg"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/blast-radius-light.svg" alt="agent-bom blast-radius drilldown — package to finding to MCP server to agent" width="900" /> </picture> </p>
text
package
  -> vulnerability finding
  -> MCP server
  -> tools + credential refs
  -> agent

Blast radius is the core idea. A vulnerable package is not just a CVE row; it is linked to the MCP server that loads it, the tools exposed by that server, the credential environment names in reach, and the agents that can call it.

<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/control-loop-dark.svg"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/control-loop-light.svg" alt="agent-bom control loop from discovery to graph evidence to gateway policy and runtime enforcement" width="900" /> </picture> </p>

First Run

bash
pip install agent-bom
agent-bom quickstart --dry-run --offline   # print the onboarding plan
agent-bom quickstart --run --offline        # write sample, scan, seed gateway policy, populate the cockpit
agent-bom agents --demo --offline

The demo uses real OSV/GHSA advisories against intentionally vulnerable sample packages and produces graph-ready inventory without touching your source tree. For a real local scan:

bash
agent-bom agents -p . -f html -o agent-bom-report.html

Want an inspectable sample stack first?

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

See docs/FIRST_RUN.md for the guided path from CLI output to the dashboard.

To reproduce the dashboard screenshots from a clean local control-plane store:

bash
make build-ui
uv run agent-bom serve --persist /tmp/agent-bom-demo.db --allow-insecure-no-auth
uv run agent-bom agents --demo --offline --no-auto-update-db -f json -o /tmp/agent-bom-demo.json
curl -sS -H 'content-type: application/json' --data-binary @/tmp/agent-bom-demo.json \
  http://127.0.0.1:8422/v1/results/push
<p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/demo-latest.gif" alt="agent-bom terminal demo" width="820" /> </p>

Product Proof

The dashboard screenshots below are captured from the packaged UI with bundled demo scan data and seeded control-plane records, not static mockups. The data is synthetic where needed, but the routes are the real scan, graph, fleet, identity, audit, and gateway surfaces. The README keeps the first screen focused; expand the gallery when you want to inspect the control-plane surfaces.

<details open> <summary><b>Evidence cockpit and agent mesh</b></summary> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/dashboard-live.png" alt="agent-bom risk overview dashboard with posture score, findings, and attack path summary" width="900" /> </p> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/mesh-live.png" alt="agent-bom agent mesh graph showing agent, MCP server, package, tool, credential reference, and finding path" width="900" /> </p> </details> <details open> <summary><b>Graph views beyond the agent mesh</b></summary>

The graph proof set is intentionally split across modes: fix-first exposure paths, root-centered lineage, lateral context, and package risk distribution. That keeps each view readable instead of forcing every relationship into one sprawling canvas.

<p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/security-graph-live.png" alt="agent-bom security graph with attack-path queue, graph evidence export, and remediation handoff" width="900" /> </p> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/lineage-graph-live.png" alt="agent-bom lineage graph centered on an agent with bounded paths, filters, and graph evidence export" width="900" /> </p> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/context-map-live.png" alt="agent-bom context map showing agent-to-server reachability and lateral movement context" width="900" /> </p> </details> <details open> <summary><b>Environment state and identity lifecycle</b></summary>

Fleet and identity views use the same control-plane APIs that operators use for customer-owned deployments. The sample below seeds environment, owner, lifecycle state, and agent identity events so the screenshots show how local scan evidence connects to reviewable governance records.

<p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/fleet-state-live.png" alt="agent-bom fleet state dashboard showing lifecycle distribution, approved and discovered agents, owner metadata, environment labels, and discovery state" width="900" /> </p> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/identity-audit-live.png" alt="agent-bom audit log filtered to identity lifecycle events with HMAC integrity counters and issue, rotate, revoke rows" width="900" /> </p> </details> <details> <summary><b>Dependency and remediation views</b></summary> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/dependency-map-live.png" alt="agent-bom dependency map with scan pipeline counts, supply-chain treemap, blast-radius chart, and EPSS by CVSS risk map" width="900" /> </p> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/remediation-live.png" alt="agent-bom remediation dashboard with prioritized package fixes and compliance context" width="900" /> </p> </details> <details> <summary><b>Runtime policy and audit posture</b></summary> <p align="center"> <img src="https://raw.githubusercontent.com/msaad00/agent-bom/main/docs/images/gateway-policies-live.png" alt="agent-bom gateway policy dashboard showing advisory runtime posture, enabled policy count, rule counts, and bound agents" width="900" /> </p> </details>

Screenshot capture rules and the full manifest live in docs/CAPTURE.md and docs/images/product-screenshots.json.

Start Here

GoalCommandArtifact
Local agent and MCP inventoryagent-bom agentsfindings, AI BOM, graph-ready JSON
Guided local onboardingagent-bom quickstart --dry-run --offlinescan, sample-data, and local API/UI next steps
One-command onboardingagent-bom quickstart --run --offlinewrites sample, runs a graph-persisting scan, seeds a baseline gateway policy
Repo and lockfile scanagent-bom agents -p .package findings, SARIF/SBOM/HTML when requested
Pre-install guardagent-bom check flask@2.0.0 --ecosystem pypideterministic allow/warn/block result
Container image scanagent-bom image nginx:latestimage findings and remediation
IaC scanagent-bom iac Dockerfile k8s/ infra/main.tfIaC findings and policy context
Cloud posture checkagent-bom cis-benchmark --provider awsruntime CIS posture evidence
CI gateuses: msaad00/agent-bom@v0.88.6SARIF, PR summary, optional code-scanning upload
MCP toolspip install 'agent-bom[mcp-server]' && agent-bom mcp serverstrict-args security tools for MCP clients
Local API/UIpip install 'agent-bom[ui]' && agent-bom serveAPI plus bundled dashboard
First-run extraspip install 'agent-bom[all]'supported onboarding extras; MLflow remains separately installed
Self-hosted pilotdocker compose -f docker-compose.pilot.yml up -dAPI and dashboard in your environment

The base wheel is the scanner and CLI path. Optional runtime surfaces fail fast with install hints when their extras are missing.

MCP registry publishing is tracked through the committed Smithery manifest and other registry metadata; install and liveness checks stay in the linked integration docs instead of this front door.

Shipped Surfaces

SurfacePrimary userCurrent boundary
CLI / CIdevelopers and release gateslocal scans, SARIF/SBOM/HTML/JSON, deterministic exit codes
REST APIcontrol-plane integrationsscans, bulk findings, dataset versions, evaluation runs, graph evidence, audit, runtime summaries
MCP toolsagents and assistantsstrict arguments, read-mostly security queries, exposure paths, deploy decisions, audited Shield actions
Dashboardsecurity teams and operatorsinventory, findings, graph cockpit, compliance, evidence, runtime posture
Runtime proxy/gatewayruntime operatorsscoped MCP traffic inspection, policy decisions, redacted audit evidence
Python clientservices, notebooks, and automationtyped helper for stable REST endpoints in the packaged wheel
TypeScript clientservices and agent runtimestyped helper for stable REST endpoints

MCP server mode advertises 63 MCP tools, 6 resources, and 6 workflow prompts. Most tools are read-only. The three Shield write actions fail closed unless the caller supplies operator_role=admin, operator_scopes=shield:write, and an audit reason.

CLI scan commands run local scan pipelines today. They share lower scanner and discovery libraries with the API, but they are not API wrappers yet.

Runtime enforcement is explicit. Proxy mode either wraps a target MCP server for audit and policy decisions, or runs that server through Docker/Podman isolation when a sandbox image is supplied:

bash
agent-bom proxy --no-isolate --policy policy.json --detect-credentials --block-undeclared -- npx @mcp/server-github
agent-bom proxy --sandbox-image ghcr.io/acme/mcp-runtime@sha256:<digest> \
  --sandbox-image-pin-policy enforce --block-undeclared -- npx @mcp/server-postgres

Deploy In Your Boundary

agent-bom is designed for customer-controlled deployment: local CLI, Docker, GitHub Action, Helm, EKS, Postgres, and optional runtime proxy/gateway.

bash
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 self-hosting starts with the deployment chooser:

There is no managed cloud offering in this repository today. Product lane boundaries are documented in docs/PRODUCT_BOUNDARIES.md.

Trust Model

  • Read-only discovery by default for cloud and local inventory.
  • No mandatory telemetry.
  • Credential values are redacted; credential environment names are preserved as evidence so exposure paths stay explainable.
  • Findings can export as JSON, SARIF, CycloneDX, SPDX, Markdown, HTML, and compliance evidence bundles.
  • API and runtime paths are designed for tenant scope, auth boundaries, and audit evidence.
  • OpenAPI artifacts are committed for SDK and client contract checks.

Security and release references:

Product Views

The docs site carries the deployment-oriented walkthroughs behind those screenshots:

Contributing

Contributions are welcome. Start with:

License: Apache-2.0.

常见问题

agent-bom 是什么?

面向 AI 供应链安全的扫描器,支持 CVE 扫描、blast radius 分析、策略执行与 SBOM 生成。

相关 Skills

Claude接口

by anthropics

Universal
热门

面向接入 Claude API、Anthropic SDK 或 Agent SDK 的开发场景,自动识别项目语言并给出对应示例与默认配置,快速搭建 LLM 应用。

想把Claude能力接进应用或智能体,用claude-api上手快、兼容Anthropic与Agent SDK,集成路径清晰又省心

AI 与智能体
未扫描149.6k

RAG架构师

by alirezarezvani

Universal
热门

聚焦生产级RAG系统设计与优化,覆盖文档切块、检索链路、索引构建、召回评估等关键环节,适合搭建可扩展、高准确率的知识库问答与检索增强应用。

面向RAG落地,把知识库、向量检索和生成链路系统串联起来,做架构设计时更清晰,也更少踩坑。

AI 与智能体
未扫描17.9k

多智能体架构

by alirezarezvani

Universal
热门

聚焦多智能体系统架构设计,梳理 Supervisor、Swarm、分层和 Pipeline 等模式,覆盖角色定义、通信协作与性能评估,适合规划稳健可扩展的 AI agent 编排方案。

帮你系统解决多智能体应用的架构设计与协同编排难题,适合构建复杂 AI 工作流,成熟度高、社区认可也很亮眼。

AI 与智能体
未扫描17.9k

相关 MCP Server

知识图谱记忆

编辑精选

by Anthropic

热门

Memory 是一个基于本地知识图谱的持久化记忆系统,让 AI 记住长期上下文。

帮 AI 和智能体补上“记不住”的短板,用本地知识图谱沉淀长期上下文,连续对话更聪明,数据也更可控。

AI 与智能体
87.1k

顺序思维

编辑精选

by Anthropic

热门

Sequential Thinking 是让 AI 通过动态思维链解决复杂问题的参考服务器。

这个服务器展示了如何让 Claude 像人类一样逐步推理,适合开发者学习 MCP 的思维链实现。但注意它只是个参考示例,别指望直接用在生产环境里。

AI 与智能体
87.1k

PraisonAI

编辑精选

by mervinpraison

热门

PraisonAI 是一个支持自反思和多 LLM 的低代码 AI 智能体框架。

如果你需要快速搭建一个能 24/7 运行的 AI 智能体团队来处理复杂任务(比如自动研究或代码生成),PraisonAI 的低代码设计和多平台集成(如 Telegram)让它上手极快。但作为非官方项目,它的生态成熟度可能不如 LangChain 等主流框架,适合愿意尝鲜的开发者。

AI 与智能体
8.1k

评论