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Contain your AI agents before they reach production.

Cinch is an MCP server + cross-harness skill set for building and operating AI agents safely. 117 enforceable controls, each mapped to a threat, a verification step, and a framework — queryable by any MCP-compatible agent at runtime.

View on GitHub
pip cinch-ai-security Python 3.10+ MCP 2.0 CC BY 4.0 NIST AI RMF · OWASP · CUSTODY · LASM

What Cinch gives you

The agent never trusts a prompt to enforce a boundary. It asks Cinch for the control, the verification step, and the framework mapping — and the harness enforces the answer.

117 enforceable controls

Six checklists — agent containment, harness engineering, system hardening, red team, supply chain, agent environment. Each control names a threat, a required control, and how to verify it.

Framework crosswalks

Every control maps to NIST AI RMF, OWASP LLM/Agentic, CUSTODY, LASM, and MITRE ATLAS — so a passing assessment is directly evidence for the obligations you already track.

MCP tools at runtime

Seven tools your agent queries live — checklist_run, threat_search, mapping_lookup, protocol_get, checklist_diff. The harness enforces; Cinch answers. The server is read-only by design.

Evidence export

Every answer becomes evidence. Export a PDF report, framework crosswalk appendix, signed JSON for CI gating, and a CSV gap register — re-assessment diffs are machine-readable.

CUSTODY — seven pillars of containment

CUSTODY addresses capability accretion: the gap between what an agent is granted and what it can actually do. Seven pillars define what an agent may access, change, invoke, or delegate.

Install in one command

Cinch runs as a local MCP server over stdio. Any MCP-compatible harness — Claude Code, Cursor, custom orchestrators — connects the same way.

Install & run

pip install cinch-ai-security
cinch serveCopy

MCP config

{
  "mcpServers": {
    "cinch": {
      "command": "cinch",
      "args": ["serve"]
    }
  }
}Copy

How it works

Cinch is the control plane sitting between the agent that wants to act and the harness that decides whether to let it.

1

Agent asks

Before a high-impact action, the agent queries Cinch: threat_search("prompt injection") or checklist_run("agent-containment").

2

Cinch answers

Cinch returns the required control, the verification step, and the framework mapping — read-only, validated, path-traversal-hardened. No agent can write to it.

3

Harness enforces

The harness checks the answer and gates the action. Evidence is logged. Re-assessment diffs against the last run for CI gating.

See it on a real deployment

The interactive console below is a live assessment against the agent-containment checklist — mark controls enforced, log evidence, and watch the containment score and framework coverage update in real time.

🛡️ Cinch — CC BY 4.0 Docs ↗ GitHub ↗ PyPI ↗ MCP server + cross-harness skills for operating AI agents safely.