MCP Guardian

A safety layer between your AI assistant and the tools it uses.

Version 4.1.8 · Website · npm · Install & troubleshooting · Changelog

What's new in 4.1.8

• Incident policy workflow — Enterprise AI investigation drawer can generate, preview, accept, or reject blocking rules from incidents
• npm publish hardening — ordered publish script with registry dep resolution checks and clean-install verification before server goes live
• Dashboard stability — fix React hooks ordering in Security/Health panels that caused error #310 on load

What's new in 4.1.7

• Active Rules controls — Security → Policy now includes list/search, soft disable/enable, and hard delete operations synced to YAML
• Policy runtime semantics — enabled: false is honored across rule strategies with backward-compatible defaults
• Policy mutation APIs — cloud + local dashboard endpoints for list/toggle/delete with updated README guidance

What's new in 4.1.6

• mcp-guardian start — one command for proxy + web dashboard on port 4000 (local dev defaults)
• mcp-guardian setup — one-shot install for git clones (pnpm install, build, dashboard SPA)
• npm tarball — prebuilt dashboard UI (deploy/dashboard-spa/out/) built at publish time
• Install guide — expanded troubleshooting in README and docs/INSTALL.md

What's new in 4.1.5

• npm install — fixes broken @mcp-guardian/server@4.1.4 registry manifest (workspace: deps). Use 4.1.5+.

What's new in 4.1.4

• mcp-guardian onboard from global npm — resolves the installed package root (not cwd); writes guardian-configs/ under your current directory; ships scripts/guardian-proxy.sh and policy-audit.yaml in the npm tarball

What's new in 4.1.3

npm install fix — registry manifest now matches published tarballs (^4.1.3 semver deps, not workspace:). Use @mcp-guardian/server@4.1.3 or later. Publish via ./scripts/publish-npm-all.sh (server/CLI ship from .tgz so metadata stays correct).

What's new in 4.1.1

npm install hygiene — fixes supply-chain scanner findings from 4.1.0:

• Published tarballs no longer include postinstall or other lifecycle scripts
• workspace: dependencies are rewritten to semver (^4.1.1) at pack time
• Publish all packages via ./scripts/publish-npm-all.sh (core → plugin-sdk → server → cli)

What's new in 4.1.0

Industry roadmap plan compliance — runtime verification and dashboard wiring for all eleven fleet-wide modules (A1–C5, B1–B3):

• guardian roadmap audit — CLI + GET /api/agentic/plan-compliance/audit verify every shipped module; exit 0 when production-ready
• Dashboard Agentic AI panels — PlanCompliance, Reputation, ZeroTrust, FederatedLearning, Observatory mesh sync, SandboxWizard captured-traffic scorecard, ChainGraph (A1)
• Protection home strip — roadmap compliance score on the main Protection tab with link to Agentic AI
• A1 ONNX graph path — optional fleet chain classifier via GUARDIAN_FLEET_GRAPH_ONNX_MODEL
• B3 MPC-lite masking — pairwise-masked federated gradients (GUARDIAN_FEDERATED_MPC)
• B2/B1 mesh relays — observatory and reputation mesh publish/pull; dev stub via GUARDIAN_OBSERVATORY_STUB
• Docs & env — guardian roadmap * commands documented; production env vars in .env.example

Run guardian roadmap audit --json or open Agentic AI → Overview in the dashboard to confirm 100% compliance.

What's new in 4.0.0

Industry-standard MCP protection — Guardian moves from per-call filtering to fleet-wide, cross-agent security:

• MTX v1 — open threat exchange format (@mcp-guardian/mtx) + cloud hub
• Guardian Certified MCP — HMAC attestation, persistent registry, verification API
• Multi-step attack chains — collusion detector + session-chain graph with proxy enforcement
• Capability graph & intent binding — tool/resource graph and session intent allowlists
• Agent reputation ledger — persistent scores with proxy enforcement
• Dynamic sandbox tiers — shadow / redact / allow with RL-ready persistence
• Protocol fuzzer — expanded corpus with real block validation and cert gates
• Policy simulator — /api/policy/simulate + ab_test_policy MCP tool
• Incident playbooks & AI investigator — webhook/isolate executors; Threat Lab–linked investigations
• Compliance evidence runner — live policy + audit wired to SOC2/HIPAA/PCI/FedRAMP/ISO mappings
• guardian-bench — mcp-guardian bench CLI + public leaderboard

See CHANGELOG.md for 3.4.1 production hardening (JWKS refresh, payload limits, SIEM on all block paths, audit retention).

Roadmap (shipped in 4.0): Semantic policy translator with approval flows, config provenance chain, STRIDE/LINDDUN threat modeling, behavioral biometrics, cross-MCP attack chains with SIEM export, digital twin sandbox, zero-trust SPIFFE scoring, decentralized reputation network, ecosystem observatory, insurance risk quantification + PDF export, and federated threat detection — docs/AGENTIC_ROADMAP.md.

Fleet mandate for CISO buyers

Guardian v4 is designed as a fleet-wide control plane, not a single-proxy filter:

• Mandatory policy provenance — every YAML change is hash-chained, signed, and exportable to SIEM/auditors
• Human-in-the-loop policy approval — NL drafts must pass simulation + explicit approval before apply
• Cross-agent attack chain detection — session graphs span servers; alerts export as CEF for Splunk/Datadog
• SPIFFE/mTLS identity — zero-trust composite scores include workload identity from SPIFFE SVIDs
• Cloud observatory + reputation mesh — anonymized fleet telemetry and server reputation consensus via MCP Guardian Cloud
• Insurance-ready risk reports — ALE quantification with underwriter PDF export for cyber insurance workflows

What problem does this solve?

Modern AI assistants (Claude, Cursor, Cline, and others) can connect to tools — read files, run commands, query databases, post to Slack, and more. Those connections often use a standard called MCP (Model Context Protocol).

That power is useful, but risky:

• The AI might read files it should not see.
• It might run shell commands or delete data by mistake or because of a malicious prompt.
• Secrets can leak through tool arguments.
• API costs can spike without you noticing.

MCP Guardian sits in the middle. Every tool request goes through Guardian first. Guardian checks your rules, blocks bad requests, logs what happened, and can show you a live dashboard — before anything reaches your real tools.

Your AI assistant
       │
       ▼
  MCP Guardian  ← reads your rules, blocks bad calls, keeps a log
       │
       ▼
  Your real tools (files, GitHub, database, …)

How it works (step by step)

1. You install Guardian and point it at your existing MCP setup (or run mcp-guardian onboard to do this automatically).
2. Guardian wraps your tool servers so the AI talks to Guardian instead of talking to them directly.
3. When the AI tries to use a tool, Guardian receives the request first.
4. Guardian compares the request to your policy (a simple rules file you control).
5. If the request is allowed, Guardian forwards it to the real tool and returns the result.
6. If the request breaks a rule, Guardian blocks it and tells the AI it was denied — the real tool never runs.
7. Every allow and block is saved to a local database so you can review history and see charts on the dashboard.

You stay in control: Guardian does not silently change your rules unless you approve it (for example when reviewing Threat Lab suggestions).

Architecture

This section shows how MCP Guardian is wired together: what runs where, how a tool call flows through governance, and how optional Pro pipelines connect to the proxy.

In this section: System overview · Tool call path · Transports · Agentic AI · Dashboard · Pro pipelines · Learning loop

System overview

When you run mcp-guardian start or pnpm dashboard:proxy, one Node process typically hosts the policy proxy, the dashboard API, and (optionally) agentic services. All components share the same audit database (MCP_GUARDIAN_DB_PATH, default ~/.mcp-guardian/history.db).

flowchart TB
  subgraph clients [AI clients]
    Cursor[Cursor / Cline / Claude]
  end