zn-gate
OfficialThis MCP server provides local zn-gate security checks for prompts, tool calls, and tool results, plus status reporting.
analyze_prompt: scans user input, system prompts, or external messages for prompt injection, jailbreak, credential exfiltration, and role hijacking; returns threat assessment, confidence score, and matched rule.check_tool_call: pre-flight checks tool names and arguments before execution for tool poisoning, argument injection, and unauthorized exfiltration attempts; arguments stay local.check_tool_result: post-execution checks tool outputs and third-party content before the model reads them; redacts secrets or neutralizes detected injections; content stays local.zn_status: reports the current engine, rules version, and exactly what, if anything, leaves the machine.All checks run locally by default; only
analyze_promptcan optionally use the cloud whenZN_CLOUD=1andZN_API_KEYare set, with detected secrets removed first.
Provides a Python package with a rules engine and secret redaction for agents built with CrewAI, helping keep secrets out of model inputs and checking tool calls for prompt injection.
Works as an MCP server in JetBrains IDEs, adding security checks for tool calls and secret redaction to protect coding agents.
Provides a Python package with a rules engine and secret redaction for agents built with LangChain, helping keep secrets out of model inputs and checking tool calls for prompt injection.
For: developers using Claude Code, Codex CLI, Cursor, OpenCode, Antigravity, ZCode desktop app or any MCP client, and teams building agents with MCP, LangChain, CrewAI or LlamaIndex.
What it does: keeps secret values out of what your agent sends to its model, checks what your agent reads and calls for prompt injection, and audits prompts, skills and configs. Everything runs on your machine, is MIT-licensed and needs no account. The Python package has no runtime dependencies; the npm package's only one is age-encryption (with its @noble/@scure dependencies) for the secret vault, bundled in the package.
Install
Everything runs on your machine; no account or API key is needed. Node.js 22 LTS is recommended (from nodejs.org). Node.js 18 or later works for the hooks and the CLI; the Claude Code plugin needs 20.19 or later. For security, use the latest patch of a supported Node.js line (22.23.4, 24.22.1, 26.12.1 or later): Node.js 18 and 20 are end-of-life and get no security fixes, even though zn-gate still runs on them. With a Node.js older than 18, the zn-gate and zn commands stop and say how to install it.
Getting started: four steps, one command each. Run them in your own terminal.
npx -y zn-gate reachshows what your coding agents can reach on this machine: secrets in your.envfiles (by name only), credential files and MCP servers. It is read only and sends nothing. Details in docs/reach.md.npx -y zn-gate initsets up protection for the coding agents it finds, one step at a time, and asks before each change.npx -y zn-gate checktests that protection with a fake secret in a temporary folder.initruns it at the end too.npx -y zn-gate env shadow .envmoves the real values of your.envinto the local vault and leaves markers in the file. The first time, runnpx -y zn-gate unlockto create the vault.
One command for the coding agents on your machine:
npx -y zn-gate initinit does this:
It finds Claude Code, Codex CLI, Cursor, OpenCode, Antigravity and ZCode desktop app on your machine.
For each one that is not protected yet, it shows the setup: the zn-gate plugin for Claude Code (or zn's hooks when your Node.js is older than 20.19), and zn's hooks for the others.
It runs each step only after you confirm it.
It ends with
zn-gate check.
Run it yourself, in your own terminal, with no arguments. When an AI agent or a script runs it, it only prints the steps.
Windows PowerShell:
npmandnpxcan fail with "running scripts is disabled on this system", because the default execution policy blocksnpm.ps1. Usenpm.cmd,npx.cmdandzn.cmdinstead (for examplenpm.cmd install -g zn-gate), run the commands from Command Prompt, or allow local scripts for your account withSet-ExecutionPolicy -Scope CurrentUser RemoteSigned.
If something fails:
npx: command not found, or a message about the Node.js version: install the LTS version of Node.js, open a new terminal and run the command again."running scripts is disabled on this system" on Windows: see the PowerShell note above.
initonly prints steps and changes nothing: an AI agent or a script ran it. Run it yourself in your own terminal.checkshows NOT protected: that line says why and what to run. What the output means.You want to remove zn-gate:
npx -y zn-gate uninstallshows what it will remove (the Claude Code plugin, zn's hooks, wrapped MCP servers) and asks first.init --revertonly puts back MCP configs saved before wrapping.
Then keep your .env values in the vault. With the plugin or the hooks, the agent cannot read .env files with real values; move them into zn-gate's encrypted vault, and the file keeps markers such as __ZN_STRIPE_KEY__:
npx -y zn-gate unlock # creates the vault the first time
npx -y zn-gate env shadow .envAfter this, your own app also reads the markers in .env: start it with zn-gate's run command once you allow it (docs/secrets.md). env shadow tells you how to undo it.
New here? First 5 minutes. All the guides: docs index. Five-minute walkthrough: docs/secrets.md. Limits: docs/threat-model.md. Words you do not know: docs/glossary.md. Every command and exit code: docs/cli.md. Removing zn-gate: docs/uninstall.md.
One agent at a time (the same steps init runs):
Claude Code: the plugin. You also need Git and Claude Code 2.1.295 or later. The plugin needs Node.js 20.19 or later. With an older Node.js, npx -y zn-gate init skips the plugin and sets up zn's hooks for Claude Code instead (zn-gate check says so if the plugin is installed); update Node.js to use the plugin.
claude plugin marketplace add usezn/zn
claude plugin install zn-gate@useznAlready ran zn hooks install claude-code before? Run zn hooks uninstall claude-code first, otherwise both sets of hooks run.
Start a new Claude Code session. Claude Code is then blocked from reading .env files with real values and credential files, secrets in what its tools return are replaced with markers or redacted before the model sees them, a secret pasted into a prompt is blocked. The plugin adds no MCP server since version 1.14.0: for the secrets server, run zn hooks install claude-code instead of the plugin. Not covered: Claude Code sessions in the cloud, Cowork and chat. Details: plugins/zn-gate.
Codex CLI, Cursor or OpenCode: the hooks.
npm install -g zn-gate
zn hooks install codex # or: cursor, opencodeIn Codex, approve the new hooks with /hooks. In Cursor, shell output and file contents are not scrubbed. What each agent supports: docs/compat.md.
Other editors (VS Code, Windsurf, Cline, Zed, JetBrains, Continue): zn-gate works there as an MCP server only. Setup for each one: usezn.com/docs/integrations/editors.
Antigravity. zn hooks install antigravity (or npx -y zn-gate init) adds a hook that runs before each tool call. It blocks reads and writes of secret files and shell commands that handle secrets, and the agent cannot edit the hook files. It cannot remove secrets from what a tool returns, Antigravity's MCP configuration files are not protected, and Antigravity does not document what happens when a hook fails (in our tests the call is blocked), so treat it as a first layer. The MCP server works there too. Details: Antigravity page.
ZCode desktop app. zn hooks install zcode (or npx -y zn-gate init) adds hooks that run before each tool call and before each prompt. It blocks reads and writes of secret files, shell commands that handle secrets, MCP calls that would send a secret, and a prompt that contains a secret, and the agent cannot edit ZCode's own config file. It cannot remove secrets from what a tool returns (zn adds a notice when an output holds one), ZCode ignores the hooks of a project, and zn's launcher blocks the call when its Node.js is gone, so treat it as a first layer. ZCode's documentation does not say what happens when a hook fails; in our tests with the ZCode desktop app 3.16.3 on Linux, a hook that failed (an exit code other than 2, a crash or a timeout) let the call run, and only exit code 2 or a deny answer blocked it. Tested end to end on the ZCode desktop app 3.16.3 on Linux x64 and Windows x64 (Windows Server 2022, through the agent the app ships; the Windows app window was not driven); macOS is not tested. Details: ZCode page.
Copilot CLI (experimental). zn hooks install copilot adds hooks that run before and after each tool call. They block reads and writes of secret files and shell commands that handle secrets, and replace secret values in the results of read-type and shell tools. They cannot block a typed prompt, Copilot lets a call through when a hook times out, so zn, which sets a 15-second timeout on its hooks, blocks the call itself at 11 seconds, it has not been tested on Windows, and Copilot's own session log keeps the original output, so treat it as a first layer. Details: Copilot CLI notes.
Gemini CLI (experimental). zn hooks install gemini adds a hook that runs before each tool call. It blocks reads and writes of secret files and shell commands that handle secrets, and the agent cannot edit Gemini CLI's settings files. It does not change what a tool returns, Gemini CLI hooks cannot ask you for confirmation, and Gemini CLI lets a call run when a hook crashes or times out, so zn blocks it instead (a hook that fails is answered with a block, and zn sets a 15-second timeout on its hook and answers a check still running after 11 seconds with a block), so treat it as a first layer. Google replaced Gemini CLI with Antigravity CLI for free and Google One accounts on 18 June 2026; Gemini CLI keeps working with zn-gate. Details: Gemini CLI notes.
Wrap any MCP server with the shield proxy, try it in shadow mode first, and review changes to its tools with pins: docs/shield.md.
Python: for agents you build yourself (rules engine and secret redaction for LangChain, CrewAI, LlamaIndex or plain tool calling). It does not protect Claude Code, Codex, Cursor or other coding agents. Install it in a virtual environment; many recent Linux systems, and Homebrew's Python on macOS, refuse pip install outside one ("externally-managed-environment"):
On Debian and Ubuntu, python3 -m venv needs the python3-venv package first: sudo apt update && sudo apt install python3-venv.
python3 -m venv .venv
.venv/bin/pip install zn-gate # Windows: .venv\Scripts\pip install zn-gate
.venv/bin/zn-gate scan . # Windows: .venv\Scripts\zn-gate scan .zn-gate is installed inside the environment, so run it as shown, or run source .venv/bin/activate once in each terminal and then zn-gate scan . (Windows: .venv\Scripts\activate).
Only the command-line tools (scan, test, status)? Install the Python CLI as a standalone tool, without a virtual environment. On Ubuntu and Debian, first run sudo apt update && sudo apt install pipx. Bare pip is often missing there too (pip: command not found), and pip install into the system Python is refused, so use pipx, uv or a virtual environment. To get uv, follow the official uv installation guide:
pipx install zn-gate
pipx ensurepath # then close and reopen your terminal
zn-gate scan .pipx ensurepath puts ~/.local/bin on your PATH. Until you reopen the terminal, zn-gate is reported as "command not found". With uv: uv tool install zn-gate (run uv tool update-shell if the command is not found). To run it once without installing: uvx zn-gate scan .
The Python CLI does not install hooks. To protect Claude Code, Codex CLI, Cursor, OpenCode, Antigravity or ZCode desktop app, run npx -y zn-gate init (Node 18 or later).
For LangChain and LangGraph, ZnGuardCallbackHandler stops the run when it blocks a prompt, a tool call or a tool output, and guard_langchain_tool(tool) wraps a tool so its arguments are checked before it runs and its output is checked and masked before the model reads it. A callback cannot change what a tool returns, so the handler with mask_secrets=True (default) also stops the run on a secret in a tool output (it fails closed); to hide secrets and keep going, wrap the tool and create the handler with mask_secrets=False. See the Python package guide.
zn-gate as an MCP server only (the agent calls it when it decides to; not automatic protection):
claude mcp add zn-gate -- npx -y zn-gate mcp # Claude Code
codex mcp add zn-gate -- npx -y zn-gate mcp # Codex CLICursor (~/.cursor/mcp.json) and other editors: { "mcpServers": { "zn-gate": { "command": "npx", "args": ["-y", "zn-gate", "mcp"] } } }. OpenCode (~/.config/opencode/opencode.json): { "mcp": { "zn-gate": { "type": "local", "command": ["npx", "-y", "zn-gate", "mcp"], "enabled": true } } }.
Protect the MCP servers you already use. init finds them in your agent configs, writes a backup, and wraps each local server with the zn-gate proxy:
npx -y zn-gate init --dry-run # preview
npx -y zn-gate init --wrap-mcp # wrap (backup first)
npx -y zn-gate init --revert # put back the MCP configs saved before wrappingIt reads Claude Desktop, Claude Code (~/.claude.json), Cursor (~/.cursor/mcp.json), Codex CLI (~/.codex/config.toml), OpenCode (~/.config/opencode/opencode.json) and Antigravity (~/.gemini/antigravity/mcp_config.json). Full list and rules.
Related MCP server: Bulkhead
See what zn-gate did
npx -y zn-gate reportA short summary of the last 7 days on this computer: blocked actions, secrets hidden, new protected MCP servers and tool changes waiting for your approval. It reads only local files, needs no account and sends nothing. --since 30d changes the period, --json prints it for scripts. What it shows.
Turn on the optional cloud check
npx -y zn-gate connectOptional, and you do it yourself in your own terminal (an agent cannot). It opens usezn.com so you can create an analysis-only API key (it cannot read your usage or manage keys), then asks you to paste it at a hidden prompt. A full-access key is refused. The key is kept in your system keychain (or the zn vault when there is no keychain), never written to a file, and zn's hooks refuse the direct ways for your agent to read it. After that, the text you or your agent send through analyze_prompt, with detected secrets removed first, is also checked by the Galvanize 3 classifier at usezn.com. Until you run it, nothing is sent. zn-gate disconnect removes the key from this computer and asks whether to revoke it on usezn.com too (default no). How it works.
Check that it works
npx -y zn-gate checkFor each agent set up for your user, zn-gate asks its hook to let the agent read a .env file with a fake key and an ordinary file, in a temporary folder. It shows what your agent would be told, and a line that says NOT protected says how to fix it. Nothing is sent over the network and no setting changes. What the output means.
npx -y zn-gate testThis runs the local self-test (51 curated vectors: 26 attacks, 25 benign) and shows per-decision latency on your machine. It is a smoke test, not a detection rate. Nothing is sent over the network.
For developers: see the redaction by hand. Send a tool output with a fake AWS key (AWS's documentation example) and a database password through zn-gate, exactly as your agent would, and look for sanitized_content in the output; that field is what the model would see. In the full output, verdict: allow only means that no prompt injection was found; the secrets are hidden in sanitized_content (here [REDACTED_AWS_KEY] and [REDACTED_DB_PASSWORD]), and secrets_redacted counts them:
printf '%s\n' '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"check_tool_result","arguments":{"tool_name":"read_file","content":"AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE\nDATABASE_URL=postgres://app:hunter2@db:5432/main"}}}' \
| npx -y zn-gate@latest mcpPython equivalent:
.venv/bin/python -c "from zn_gate import sanitize_tool_result as s; print(s('read_file', 'AWS_ACCESS_KEY_ID=AKIAIOSFODNN7EXAMPLE')['sanitized_content'])"What each piece does
Where zn-gate sits in a tool call, as an MCP server or as the shield proxy around other servers:
Scrubber: secrets stay out of the model's context
Today, through zn-gate: redacts AWS keys, OpenAI and Anthropic keys, GitHub tokens, JWTs, private keys and database URL passwords in everything that passes through zn-gate: tool output checked with
check_tool_result, tool arguments checked withcheck_tool_call, and output of MCP servers wrapped byzn-gate shield/init. Python:sanitize_tool_result,@guard(mask_secrets=True)and the framework integrations.Today, agent hooks and vault (npm package): hooks for Claude Code, Codex CLI and OpenCode scrub the output of the agents' built-in tools and block pasted secrets (in Cursor only MCP tool output is scrubbed); a local encrypted vault with stable markers (
__ZN_AWS_KEY_1__);zn rungives a value (as an environment variable) only to commands you approved; personal data (cards, IBAN, national IDs, e-mail, phone) is detected too. What each agent allows differs: see docs/compat.md.
Galvanize: prompt-injection detection
Today, local: a deterministic rules engine (signatures plus normalization against homoglyphs, zero-width characters, BiDi overrides, comment splicing and Base64) checks prompts, tool arguments, tool output and MCP tool descriptions.
Today, optional cloud check: the Galvanize 3 classifier (open weights on Hugging Face; announcement; the previous model, Galvanize 2, also has open weights under Apache-2.0) can also check prompts sent to
analyze_prompt, only if you setZN_CLOUD=1andZN_API_KEY. The secrets zn-gate detects are removed from the prompt on your machine before it is sent; personal data is not removed on this path, and a secret zn-gate does not recognize is sent with the text.Roadmap: running Galvanize locally inside zn-gate.
Scan: audit what your agent is about to trust
Today:
zn-gate scan <path>audits prompt templates,AGENTS.md/SKILL.mdfiles and configs for injection traps and leaked secrets (exit code 1 on findings, for CI). A link given as the folder to scan is followed. When no file is scanned, the report says "No files were scanned in " and the exit code is 3. A file or folder that could not be read (for example a file of 2 MiB or larger) is listed and also makes the exit code 3, unless you pass--allow-partial. A leaked secret is reported as Secret found, with the next step:npx -y zn-gate env shadow <file>moves the values of a.envfile into the local vault,npx -y zn-gate secret set NAMEstores any other one. It finds provider keys and tokens, opaqueAuthorization: Bearertokens, passwords in URLs, and secrets passed as command-line flags (--token=...) or assigned to a name such aspasswordorapi_key. The same check runs as a GitHub Action. It also checks the MCP server configs (.mcp.json,.cursor/mcp.json,.vscode/mcp.json,.codex/config.toml,claude_desktop_config.jsonand others) for keys and passwords written in plain text inenv,headers, arguments or URLs; it never prints the value, and--include-homechecks your own MCP configs too. MCP servers can be checked on the web at usezn.com/scan.Roadmap: auditing MCP servers and skills from the CLI before you add them.
What the cloud adds for teams
Everything above runs locally, for free, with no account. The optional cloud at usezn.com is for teams that need a shared record of what their agents did:
Audit and traceability: an evidence dashboard with the metadata of each cloud decision (verdict, rule, score, latency), 90-day retention and CSV/JSONL export.
A larger detection model for prompts, when you opt in (
ZN_CLOUD=1).Team controls (shared policies, organization-level audit): in progress.
By default nothing is sent. If you set ZN_CLOUD=1 and an API key, run zn-gate connect, or use the API key option of the Claude Code plugin, analyze sends the prompt text after removing the secrets zn-gate detects locally: personal data is not removed on this path, and a secret it does not detect would be sent with the text. The dashboard keeps the metadata of each cloud decision. Tool output and tool-call arguments are never sent. If the cloud call fails (no connection, a timeout, an error from the service, or a saved key that cannot be read), the prompt is checked by the local rules alone and the result says fallback: true. The check does not fail closed: a prompt the local rules allow goes through.
What leaves your machine
Nothing, by default.
Data | Evaluated | Sent anywhere? |
Tool output ( | Locally | Never, in any configuration |
Tool-call arguments ( | Locally | Never, in any configuration |
MCP tool and parameter descriptions ( | Locally | Never, in any configuration |
Counts of what zn-gate did ( | Written locally to | Never, unless you turn on |
Prompts ( | Locally | Only if you set both |
Team audit trail ( | Written locally | Only on a machine joined to a Team organization with |
Secret values in the vault ( | Encrypted locally | Never to zn. zn gives a value only to a program you allowed ( |
| Locally | Only when you run them yourself in your own terminal. They send only the API key, to |
ZN_API_KEY alone enables nothing. zn-gate connect is the other way to opt in: you run it, in your own terminal, and it keeps the key in your system keychain or the zn vault, never in a file (zn-gate disconnect undoes it and can revoke the key on usezn.com when you say yes; details). ZN_LOCAL_ONLY=true overrides ZN_CLOUD=1 and a connected key. npx -y zn-gate status shows the active detection engine and whether the optional cloud check is on. Sharing of usage statistics is off by default and has its own status: npx -y zn-gate stats share status.
1.4.0 and earlier: with
ZN_API_KEYset, the Node package sent tool output and tool-call arguments toapi.usezn.comwithout redaction. 1.4.1 removes that path. Details in the CHANGELOG.
Performance and accuracy
We only publish numbers we can reproduce, with the method next to them.
Latency of the local engine: one table by input size, with hardware, date and the script to reproduce it (
bench/bench_by_size.js), in the npm package README. Short summary from that run, with secret redaction (p50): about 0.045 ms for a 200 B prompt, 0.3 ms for a 4 KB tool output, about 4 ms for 64 KB.Self-test: 51 curated vectors pass. That is a smoke test, not a detection or false-positive rate.
Known limitation: on whole source files, the rules can flag ordinary code comments. Version 1.5.0 reduces these on code and documentation, at the cost of letting some loosely worded override attempts through the local rules. To check it on your own source files, run
zn-gate scan --code <path>; for the MCP servers you wrap,--shadowlogs detections without blocking.Known limitation: credentials under prefixed upper-case variable names (
OPENAI_API_KEY=,MY_APP_DB_PASSWORD=,export X_SECRET=) are redacted in env files, JSON, YAML, Dockerfile and shell forms, and placeholders such asYOUR_KEYor${VAR}are left alone (tested in the npm and PyPI packages). Lower-case or dotted names such asopenai_api_key=ormy.app.db.password=are not redacted by the prompt and tool-result rules.Detection rates: on a multilingual set of 3,309 examples, Galvanize 3 has 89.6% recall at a 0.6% false positive rate, and it flags 2.9% of 658 long legitimate documents; these numbers are for the cloud classifier only. They do not measure the local rules engine and are not protection that zn-gate gives you offline; the local engine has no detection rate, only the self-test and latency above. The set was not used for training; it was used to choose between candidate models. Method and the comparison with nine open detectors are in the announcement. The Galvanize 3 weights and model card are on Hugging Face. The previous model, Galvanize 2, has its own results on its model card. It is one layer of defense, not a guarantee.
What zn-gate is not
Not a sandbox. zn-gate checks what its hooks, MCP wrappers and library calls can see, and blocks or rewrites it. A program the agent starts can still do things the hooks cannot read. For that, use your agent's own sandbox or permission prompts (see the threat model).
Coverage depends on the agent. Each agent's hooks see and can change different things: for example, Cursor cannot rewrite shell or file output, and Codex replaces a result instead of editing it. Agent compatibility lists it per agent, and
npx -y zn-gate checkshows what your own agents are covered for.
Configuration
Variable | Default | Effect |
| off |
|
| unset | API key from usezn.com/dashboard. No effect without |
| unset |
|
|
| Cloud endpoint selection. |
zn Team: zn team join <token> connects a machine to your organization's signed secrets policy and uploads its audit log (metadata only). What the organization can and cannot change on the machine, offline behavior and the OpenTelemetry export (zn vault log export --otlp): docs/team.md.
Custom rules: add banned patterns and forbidden paths in .znrules or zn.config.json at your project root (example).
Audit log: zn-gate shield appends each decision to ~/.zn/evidence.jsonl as a hash-chained (SHA-256) record with a redacted preview. It detects accidental or partial edits; it is not tamper-proof against someone with write access to the file. Check it with npx -y zn-gate evidence --verify. The agent hooks add to the same log when they block something or hide a secret in a tool result: it records the agent, the kind of event, a rule name and the kinds of secret, never a secret value, a command, a file path or your prompt.
GitHub Action
Guide for the audit mode (permissions, code scanning, badge): docs/github-action.md. To report findings without failing the build, see Warn without failing the build.
The action installs the published zn-gate packages at an exact version and runs on the runner only; it sends nothing anywhere. It has two checks, chosen with mode:
scan(default):zn-gate scanlooks for prompt-injection traps and leaked secrets in prompts, tool definitions and config files (Python package from PyPI). This is what earlier releases of the action did.audit:zn auditreviews what your coding agents are configured to trust:.claude/,.mcp.json,.codex/,.cursor/, hooks,AGENTS.md/CLAUDE.md, plugins and skills (npm package; needs version 1.6.0 or later).both: runs the two. With aversionolder than 1.6.0, or a PyPI-only.postversion, it runs the scan only and prints a warning.
Scan prompts on pull requests:
name: AI agent security scan
on:
pull_request:
paths:
- 'prompts/**'
- '**/*.prompt'
- '**/AGENTS.md'
- '**/SKILL.md'
permissions:
contents: read
jobs:
zn-gate-scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
- name: Scan AI agent prompts and tools
# Pin the action to a full commit SHA; the comment names the release.
uses: usezn/zn@84f85b2ed5bd938d823bc2521d48044eddb2ad84 # v1.15.0
with:
version: '1.15.0'
package-sha256: '18733cfed66c80ee8114d3d3be5809cc74346dbaac42403c51c137fbc8c1d5fd'
path: '.'
fail-on-threat: 'true'
format: 'github'Audit agent configs and plugins on every pull request, and show the findings in code scanning. The audit takes seconds, so the example has no paths: filter: a filter would skip the configs of agents it does not list.
name: AI agent config audit
on:
pull_request:
permissions:
contents: read
jobs:
zn-gate-audit:
runs-on: ubuntu-latest
permissions:
contents: read
security-events: write # only for the SARIF upload
actions: read # private repositories only, for upload-sarif
steps:
- uses: actions/checkout@3d3c42e5aac5ba805825da76410c181273ba90b1 # v7.0.1
with:
persist-credentials: false
- name: Audit agent configs
id: zn
# Pin to the full commit SHA of an Action release v1.10.0 or later.
# The zn-gate package version is set by version below.
uses: usezn/zn@84f85b2ed5bd938d823bc2521d48044eddb2ad84 # v1.15.0
with:
version: '1.15.0'
mode: 'both'
format: 'sarif'
severity-threshold: 'medium' # stricter than the default (high)
- name: Upload SARIF
if: ${{ !cancelled() && steps.zn.outputs.sarif-file != '' }}
uses: github/codeql-action/upload-sarif@24c54180a607b1449ed407dd24f251e4e9147c8d # v4.38.3
with:
sarif_file: ${{ steps.zn.outputs.sarif-file }}The mode, agents, fail-on, severity-threshold, sarif-file and npm-integrity inputs and the audit-exit-code output need two things: an Action release of v1.10.0 or later (the ref in uses:) and a zn-gate package of 1.6.0 or later (the version input). The default version already meets the second. With uses: pinned to an older commit, the runner only warns about the unknown inputs ("Unexpected input") and the step runs a plain scan, which can pass: no audit runs.
Input | Default | Description |
|
| Exact |
|
|
|
|
| File or directory to scan. |
|
| Scan only: |
|
| Fail the step on findings. For |
|
| Audit only: lowest severity that fails the step when |
| empty | Audit only: lowest severity that fails the step ( |
| empty | Audit only: comma-separated agents to audit ( |
|
|
|
| empty | With |
| empty | Requires v1.5.0 or later of the action. SHA-256 of the PyPI wheel for |
| empty | Integrity string ( |
Output | Description |
| Path of the SARIF file ( |
| Exit code of the scan: 0 clean (or |
| Exit code of the audit: 0 clean, 1 findings at or above |
The scan finds more secret types with each release (1.9.1 added secrets passed as command-line flags and assigned to names such as password or api_key), so a job that fails on findings can start failing on files that passed before: pin version and read the findings before you update it.
Older releases of the action ignore the inputs they do not have, with only a warning: with one of them, mode: audit runs the scan instead.
Which audit findings fail the step: fail-on if it is set; otherwise none when fail-on-threat is false; otherwise severity-threshold. For the scan, only fail-on-threat applies.
With mode: both and format: sarif, the file holds one SARIF run per check (zn-gate scan and the audit's own run), each with its own category, so code scanning tracks their alerts separately. The step fails with the worse of the two results: an error beats findings.
Permissions: the action itself needs only contents: read; add security-events: write to the job only when you upload SARIF (and, in private repositories, actions: read, which upload-sarif needs). Packages are installed without running install scripts or build steps (--only-binary=:all: --no-deps for the wheel, --ignore-scripts for the npm tarball, whose dependencies are bundled) into private directories under the runner temp directory. pip still reads the runner's own pip settings (PIP_* environment variables and pip.conf), so a mirror or proxy you configure there is used; set package-sha256 so that the wheel is checked whatever index it comes from (and npm-integrity for the npm tarball). Run it on pull_request, never on pull_request_target: do not check out untrusted pull-request code in a pull_request_target workflow. With mode: audit or both the action also sets up Node.js 22 for the rest of the job.
pre-commit
Run both checks before each commit with the pre-commit framework (needs Node.js 18 or later; ids zn-gate-scan and zn-audit). The hooks file ships from release 1.14.0, so rev must be a release tag of 1.14.0 or later: earlier tags have no .pre-commit-hooks.yaml and pre-commit fails with InvalidManifestError. Add repos: [{repo: https://github.com/usezn/zn, rev: v1.15.0, hooks: [{id: zn-gate-scan}, {id: zn-audit}]}] to .pre-commit-config.yaml. Each tag's hooks run the exact zn-gate version of that tag (npx --yes zn-gate@1.15.0 ...), so the rev you pin decides which version runs on every commit. The scan hook reads the whole repository, so a file of 2 MiB or more makes it exit 3; to accept skipped files, add args: [--allow-partial] to the zn-gate-scan hook.
Repository layout
packages/zn-gate: Node.js CLI, MCP server andshieldproxy (npm).packages/zn-gate-py: Python SDK and CLI (PyPI).src/: legacy Rust gateway, not released (docs/rust-core.md).bench/: benchmark scripts and results.assets/brand/: logo files (SVG master and light/dark variants, PNG lockup and banners).
Examples
Runnable examples are in examples/. The code API checks text for sensitive paths, prompt injection in tool output and secrets, and redacts the secrets; it does not block destructive shell commands.
Example | Language | Framework | What it shows |
Python | None | Evaluate a prompt and guard a tool function. | |
JavaScript | None | Inspect a prompt and sanitize a tool result with the Node.js package. | |
JavaScript | None | Load project rules from | |
Python | LangChain / LangGraph | Wrap tools so a blocked call does not run and secrets in tool output are hidden; a callback handler stops the run on a block. | |
Python | CrewAI | Check what a tool returns before the model reads it: a page that gives the model orders is replaced by a notice, and a secret in it is hidden. | |
Python | LlamaIndex | Check what a tool returns before the model reads it: a page that gives the model orders is replaced by a notice, and a secret in it is hidden. | |
Python | AG2 | Check what a tool returns before the model reads it: a page that gives the model orders is replaced by a notice, and a secret in it is hidden. | |
Python | OpenAI Agents SDK | Check what a tool returns before the model reads it: a page that gives the model orders is replaced by a notice, and a secret in it is hidden. | |
Python | Pydantic AI | Check what a tool returns before the model reads it: a page that gives the model orders is replaced by a notice, and a secret in it is hidden. | |
Python | Google ADK | Check what a tool returns before the model reads it: a page that gives the model orders is replaced by a notice, and a secret in it is hidden. | |
JavaScript | LangChain.js / LangGraph.js | Block a dangerous tool call and scrub what a tool returns before the model reads it. | |
JavaScript | OpenAI Agents SDK for JavaScript | Block a dangerous tool call and scrub what a tool returns before the model reads it. | |
YAML | Promptfoo | Run a deterministic guardrail suite with Promptfoo. | |
YAML | GitHub Actions | Scan agent prompts and configs in pull requests with the GitHub Action. |
FAQ
Do I need an account or API key? No. Everything local works without one. An account is only needed for the optional cloud (team audit and the larger detection model).
Does zn-gate slow my agent down? The local check adds under a millisecond for typical prompts and tool outputs (p99 0.8 ms at 4 KB on our test host). See the measured table above.
Will it block legitimate content? Sometimes. Use init --shadow or shield --shadow to log detections without blocking while you evaluate it. Details in docs/shield.md.
Can zn-gate block keys in tool output? Yes. When a tool returns a secret value (an AWS, OpenAI or Anthropic key, a GitHub token, a JWT, a private key or a password in a database URL), zn-gate replaces each value it recognises with a marker or redacts it before the model sees it. "Block" here means that rewrite of the output; the tool call itself is not stopped. What is covered depends on the agent (the full table is in docs/compat.md):
Claude Code and OpenCode: every tool, including shell commands, file reads, search and MCP.
Codex CLI: the result is replaced by its scrubbed text, so the model gets text instead of the structured result. Codex's own session log keeps the original output.
Cursor: MCP tool output only. Cursor's hooks cannot rewrite shell or file-read output.
ZCode:
zn hooks install zcodeblocks reads and writes of secret files, shell commands that handle secrets, MCP calls that would send a secret and prompts that contain one. It cannot rewrite what a tool returns, so a value a command prints still reaches the model (zn adds a notice). ZCode ignores the hooks of a project. Details: docs/editors/zcode.md.Copilot CLI (experimental):
zn hooks install copilotblocks reads and writes of secret files and shell commands that handle secrets, and replaces secret values in the results of read-type and shell tools. It cannot block a typed prompt, lets a call through when its hook times out (zn sets a 15-second timeout on its hooks and blocks the call itself at 11 seconds), has not been tested on Windows, and Copilot's own session log keeps the original output. Details: docs/compat.md.Gemini CLI (experimental): Google replaced Gemini CLI with Antigravity CLI for free and Google One accounts on 18 June 2026; Gemini CLI keeps working with zn-gate.
zn hooks install geminiblocks reads and writes of secret files and shell commands that handle secrets. It does not change what a tool returns, so a value a command prints still reaches the model. Details: docs/compat.md.Hermes Agent and the Cursor CLI: not covered yet. For any MCP client, the output of an MCP server wrapped with
zn-gate shieldorinitis scrubbed.
For Codex CLI, zn also reviews the model's final answer. By default it only warns you; with "stopReview": "block" under hooks in ~/.zn/config.json, Codex asks the model to rewrite a final answer that contains a secret value. A second opt-in, "replaceToolOutputOnSecret": true under hooks in the same file, makes Codex replace a tool result that holds a real secret with zn's stop text and carry on, so the model gets that text instead of the output. See docs/compat.md.
Which agents are supported? Claude Code, Codex CLI, Cursor, OpenCode, Antigravity and ZCode desktop app, through hooks (what each one supports: docs/compat.md). Other editors (VS Code, Windsurf, Cline, Zed, JetBrains, Continue) work as an MCP server only: setup for each one. init can also wrap the MCP servers you already use.
How do I remove it? Run npx -y zn-gate uninstall in your own terminal. It lists what it found (the Claude Code plugin, zn's hooks for each agent, wrapped MCP servers), asks before removing, and ends with zn-gate check. Your vault is only deleted if you confirm that separately. npx -y zn-gate uninstall --dry-run shows the list and changes nothing. To remove one part at a time, or for the steps by agent, see the removal guide. init --revert is narrower: it only puts back the MCP configs that were saved before init --wrap-mcp wrapped them. For a standalone server, remove the zn-gate entry from your agent config.
Security
Report vulnerabilities to security@usezn.com. Policy, scope and safe harbor: SECURITY.md. Hall of Fame: usezn.com/security.
Releases published by the release workflow, starting with the first one after 1.5.0-beta.1, are signed and carry build provenance. 1.5.0-beta.1 and earlier versions were published by hand and have no signatures or provenance. To check a download: docs/verify-release.md.
Community and support
Discussions: questions about setup and use, ideas, and show and tell. Start here if you are not sure where your question belongs.
Issues: reproducible bugs and concrete feature requests.
Security vulnerabilities: do not post them in Discussions or Issues. Report them privately as described in SECURITY.md.
Contributing
Issues and pull requests are welcome. See CONTRIBUTING.md.
License
MIT. Copyright (c) 2026 usezn (usezn.com). The Galvanize 2 and Galvanize-60M weights are Apache-2.0. The zn-prompt-injection-bench dataset is CC-BY-4.0. The npm package bundles third-party code listed in THIRD_PARTY_NOTICES.md.
Available Tools
4 toolsanalyze_promptARead-onlyIdempotent
Analyze text, prompt, or agent messages for security threats (prompt injection, jailbreak, credential exfiltration, role hijacking). Runs locally; only if the user set ZN_CLOUD=1 and ZN_API_KEY is the text, with secrets redacted, also sent to the zn cloud gate. Returns threat assessment, confidence score, and matched rule.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The user input, system prompt, or external message to inspect. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), yet the description still adds real behavioral context the annotations cannot: it runs locally, it only egresses to the cloud gate when ZN_CLOUD=1 and ZN_API_KEY are set, and secrets are redacted before transmission. That data-flow disclosure is exactly what an agent needs before sending sensitive prompts. The sentence is syntactically mangled ('ZN_API_KEY is the text, with secrets redacted, also sent to...'), which blunts the disclosure somewhat.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with the detection scope, which is correct, and there is little fat. However, the second sentence crams the local/cloud routing, env-var condition, redaction, and return shape into one run-on clause that is grammatically broken and forces re-reading.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Compensates well for the absent output schema by naming the return payload (threat assessment, confidence score, matched rule), and discloses the privacy/data-flow model. Remaining gaps are minor for a single-string read-only tool: no severity thresholds, no guidance on acting on a low-confidence match.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
One parameter with 100% schema coverage, so the schema already defines 'text' fully; baseline is 3. The description adds marginal value by broadening the accepted input to 'text, prompt, or agent messages', but gives no length limits, encoding expectations, or guidance on chunking large inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Names a specific verb (analyze/check) and resource (text, prompt, agent messages) and enumerates the exact threat classes it detects: prompt injection, jailbreak, credential exfiltration, role hijacking. Sibling differentiation is only implicit — the scope wording implies it handles free text rather than tool calls/results, but check_tool_call and check_tool_result are never named, so an agent must infer the boundary.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the resource description (analyze standalone text rather than a tool call or tool result), but there is no explicit 'use this when / use X instead' routing, no prerequisites, and no statement about whether it should be run on every inbound message or only on untrusted input.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_tool_callARead-onlyIdempotent
Pre-flight safety inspection for tool invocations and arguments before execution. Always local: arguments never leave the machine. Defends against tool poisoning, argument injection, and unauthorized exfiltration attempts.
| Name | Required | Description | Default |
|---|---|---|---|
| arguments | Yes | Parameters/payload object that will be supplied to the tool. | |
| tool_name | Yes | The name of the tool to be called (e.g. bash, write_file, execute_sql, send_message). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: local-only execution ('arguments never leave the machine') and the specific threat classes it guards against (tool poisoning, argument injection, exfiltration). It does not say what happens on a failed check, which is the remaining gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three tight sentences, front-loaded with the core purpose and capped by the security rationale. Slightly promo-flavored in the last clause, but every sentence carries information an agent can use.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool's security posture and locality are well covered, and there is no output schema to explain. But for a gate-style check, the description never indicates what a caller receives (verdict, flags, blocking vs advisory), which an agent needs to know to act on the result.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for both parameters, so tool_name and arguments are already well documented. The description adds no syntax, format, or nesting guidance beyond the schema, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action (pre-flight safety inspection) on a specific resource (tool invocations and their arguments) with a clear temporal qualifier ('before execution'). An agent can distinguish it from check_tool_result (post-execution) by timing alone, though the description never names that sibling explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'before execution' gives clear timing context for invocation, which implies when to call it. However, it never states when NOT to use it or points to the sibling tools (check_tool_result, analyze_prompt) that cover adjacent checks, leaving the routing decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_tool_resultARead-onlyIdempotent
Post-execution safety inspection for tool outputs and third-party content (web pages, git commits, database queries) before LLM context assimilation. Always local: content never leaves the machine. Returns the content with secrets redacted, or neutralized if an injection is detected.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The output text, markdown, or JSON payload returned by the tool. | |
| tool_name | Yes | The name of the tool that produced the output (e.g. fetch, web_search, read_file). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, not open world), yet the description adds genuinely new behavioral facts: execution is always local and content never leaves the machine, and the return value is the original content with secrets redacted or neutralized when an injection is found. That transformation/return behavior is not derivable from any structured field.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the core purpose, then the privacy guarantee, then the return contract. Every sentence carries distinct information and there is no filler or repetition of the title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description carries the burden of describing return values and does so (redacted content or neutralized content on injection detection). The remaining gap is the ambiguity of 'neutralized' — the actual returned shape or replacement text is not specified — plus no note on idempotency of repeated checks.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with only two required parameters, both already self-documented in the schema. The description mentions content categories (web pages, git commits, database queries) but adds no syntax, format, or interpretation guidance for 'content' or 'tool_name' beyond what the schema provides, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action (post-execution safety inspection), a specific resource (tool outputs and third-party content such as web pages, git commits, database queries), and a precise timing scope ('post-execution ... before LLM context assimilation'). That timing/scope framing implicitly separates it from the call-side sibling check_tool_call, so an agent can route between them without opening a schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It states exactly when the tool belongs in a workflow: after a tool has produced output and before that content enters the model's context. However, it names no alternatives (check_tool_call, analyze_prompt) and gives no exclusions or 'when not to use' condition, which is what a 5 would require.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
zn_statusARead-onlyIdempotent
Get current zn-gate status: engine, rules version, and exactly what (if anything) leaves the machine.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds real value by promising an egress disclosure — "exactly what (if anything) leaves the machine" — which tells the agent this call answers a privacy question, not just a version lookup.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with the resource first and the three returned items listed afterward. Every clause carries information; there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, non-destructive status read with no output schema, the description supplies what an agent needs: what is returned and the notable data-egress angle. Annotations handle safety, so nothing material is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there is nothing for the description to disambiguate; baseline applies. The enumerated return fields arguably compensate for the absence of an output schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ("Get") and resource ("zn-gate status") and enumerates the returned fields: engine, rules version, and data-egress disclosure. It is clearly distinguishable from the analyze_/check_ siblings, though it never names them explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: an agent can infer this is the inspection/health-check tool rather than a prompt or tool-call validator. There is no explicit statement of when to call it (e.g. before enabling the gate, when diagnosing config) and no mention of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
v1.12.0- First observed
analyze_prompt - First observed
check_tool_call - First observed
check_tool_result - First observed
zn_status
TDQS
Scored across 4 tools
Each tool targets a distinct phase of the LLM pipeline: analyze_prompt handles user/agent input, check_tool_call inspects outbound tool invocations, check_tool_result inspects inbound tool outputs, and zn_status reports configuration. There is no meaningful overlap in purpose or timing, so an agent can select unambiguously.
All names use snake_case and are readable, with check_tool_call/check_tool_result forming a clean parallel pair. The minor deviations are analyze_prompt using a different verb and zn_status using no verb at all, but the convention is still largely predictable.
Four tools is exactly right for a security gate covering the three inspection checkpoints plus a status probe. Nothing is redundant and nothing feels padded or missing.
The surface covers the full lifecycle of a security gate: pre-prompt analysis, pre-execution tool call checking, post-execution result checking, and introspection via zn_status. No obvious operational gap remains for the stated domain.
Maintenance
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