agentperms

AgentPerms

Least-privilege permissions for AI agents and MCP tools. Record what your agent does, auto-generate a safe policy, and block dangerous tool calls before they happen.

Your AI agent has sudo. AgentPerms takes it away.

MCP is becoming the standard way AI apps connect to tools and data — which means agents are getting access to your filesystem, your repos, your email, and your database faster than anyone can govern them. Scanners tell you something looks risky. Firewalls make you hand-write YAML. AgentPerms does the missing thing:

record → infer → lock → replay → enforce

Run your agent in dev, record every tool call, infer the minimum permissions it actually needed, generate a policy, prove it blocks attacks, and enforce it in CI and at runtime.

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Install

pip install agentperms

Requires Python 3.10+. This installs the agentperms CLI.

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Quick start (30 seconds, no setup)

AgentPerms ships with a deliberately over-privileged demo MCP server, so you can see a real policy decision without wiring up a client or touching the network:

# 1. Scan the demo config — flags a ~/.ssh mount and an unpinned npx server
agentperms scan --path examples/vulnerable-mcp-demo

# 2. Replay a pack of canned attacks against the example policy
agentperms replay --policy examples/policies/example.mcp.policy.yaml

The replay prints a table and a verdict:

8/8 attacks blocked.

Every attack in the pack — SSH-key exfiltration, .env reads, rm -rf /, unapproved email, force-push, repo deletion, destructive SQL — is denied or routed to human approval before it would ever reach a server.

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The full flow on your own agent

agentperms scan                      # find MCP configs, flag risky tools & exposures
agentperms lock                      # pin every tool's identity (detect tool poisoning)
agentperms record --client cursor    # route the client through the recording proxy
#   ... use your agent normally for a while ...
agentperms infer                     # traces  -> mcp.policy.yaml (least privilege)
agentperms replay                    # prove the policy blocks a pack of attacks
agentperms enforce --client cursor   # route the client through the blocking proxy
agentperms report                    # agentperms-report.html
agentperms init                      # scaffold .github/workflows/agentperms.yml

This produces mcp.policy.yaml, mcp.lock, agentperms-report.html, and a CI workflow.

When you're done recording or want to roll back enforcement, restore the original client config:

agentperms record --client cursor --stop
agentperms enforce --client cursor --stop

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Command reference

Supported clients (--client)

scan defaults to all and auto-discovers across every known client. record/enforce default to cursor.

Don't see your client? Point any command at a config explicitly with --path.

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The killer command

agentperms infer

Your agent only used read-only GitHub calls and local ./src access. I generated a least-privilege policy. The agent does not need shell, home directory, secrets, Gmail send, or database write access.

infer reads your recorded traces and emits the minimum policy that still lets the agent do what it actually did: used tools become allowed_tools, the directories it touched collapse into the smallest covering set of allowed_paths, and known-dangerous categories (shell, repo deletion, email send, DB writes) are seeded into denied_tools / human-approval.

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How enforcement works

AgentPerms is a transparent stdio proxy. record/enforce rewrite your MCP client's config so each server launches through agentperms _proxy instead of directly:

Agent  →  AgentPerms proxy  →  MCP server
              │
              ├─ record:  log every tools/call, then forward
              └─ enforce: allow / deny / require-approval before forwarding

A rewritten server entry looks like this (the original command is preserved after --, and a *.agentperms.bak backup is written so --stop can restore it):

// before
{ "command": "python3", "args": ["server.py"] }

// after `agentperms enforce`
{
  "command": "/usr/bin/python3",
  "args": ["-m", "agentperms", "_proxy",
           "--mode", "enforce", "--server", "demo",
           "--policy", "/abs/path/mcp.policy.yaml",
           "--", "python3", "server.py"]
}

Denied calls never reach the server — the client gets a clean JSON-RPC error. Approval-gated calls prompt on your terminal. The exact same decision function powers both live enforcement and offline replay, so what you test is what you get.

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The policy file

mcp.policy.yaml is a small, readable contract. This is the bundled example (examples/policies/example.mcp.policy.yaml):

version: 1

servers:
  github:
    allowed_tools: [list_repos, read_file, create_issue]
    denied_tools:  [delete_repo, write_secret, force_push]
  filesystem:
    allowed_paths:    [./src, ./docs]
    denied_paths:     [~/.ssh, ~/.env, /etc]
    denied_patterns:  ["*.pem", "*.key"]

approvals:
  require_human_approval: [gmail.send_email, github.merge_pr, postgres.execute_write, shell.exec]

redaction: { secrets: true, emails: true, api_keys: true }

Per-server fields: allowed_tools, denied_tools, allowed_paths, denied_paths, denied_patterns. Top-level: approvals.require_human_approval (entries are server.tool or bare tool) and redaction (scrubs secrets/emails/API keys from traces and reports).

How a decision is made

For each tools/call, the engine returns allow / deny / require-approval using first-match-wins precedence:

1. On the human-approval list → require approval
2. In denied_tools → deny
3. A path argument hits denied_paths / denied_patterns → deny
4. allowed_tools is set and the tool isn't in it → deny (default-deny)
5. allowed_paths is set and a path argument falls outside it → deny
6. Otherwise → allow

An empty policy (no servers) allows everything. Once any server is constrained, unknown servers default-deny.

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The attack pack

replay runs your policy against a built-in set of real-world attack shapes — entirely offline, no server required:

A call counts as blocked if the policy denies it or routes it to human approval.

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CI integration

agentperms init scaffolds a GitHub Actions workflow that fails the build on drift or weak policy:

agentperms init   # writes .github/workflows/agentperms.yml

It runs, on every push/PR:

agentperms scan --path .     # surface risky configs in the repo
agentperms lock --check      # fail if any tool's identity changed (tool poisoning)
agentperms replay            # fail if the committed policy stops blocking attacks

Commit mcp.policy.yaml and mcp.lock, and your agent's permissions become a reviewable, enforceable part of your codebase.

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Status

v0.1 — supports Claude Desktop, Cursor, VS Code/Copilot, Windsurf, and Gemini CLI configs, plus local stdio MCP servers. Roadmap: HTTP/SSE transport, a Node wrapper, and a live dashboard.

License

MIT