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Klarion

User RatingsBe the first to rate and review this MCP server! Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time — check back soon.
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Secret scanning for AI agents: rules and entropy find candidates, a model decides which are real.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON â–¾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for Klarion, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Directory Badge Claim listing Alternatives💻 More in Developer Tools

Documentation Overview

Klarion finds leaked secrets in code. An AI model checks each one, so you see the real ones.



CI Go Reference License MIT

Install · Use it · Results · How it works · Reference


Klarion finds secrets in your code: API keys, passwords and tokens.

It works in two steps:

  1. Find. Fast local checks flag anything that looks like a secret.
  2. Check. An AI model reads each one with the code around it and decides if it is real.

Pattern scanners stop at step 1, so they also flag test values, IDs and hashes. Klarion shows you the real leaks.

It runs where the leak happens

A Claude Code hook that blocks the write before the file exists. The agent cannot route around it, not through the file tools and not through Bash.

A coding agent is told to hardcode an AWS key pair. Klarion's pre-tool hook blocks the write at 95% confidence, and the agent reports that it cannot land the credentials through Bash or the file tools.

console
$ klarion scan .

services/billing/client.py
  [critical] stripe-secret-key  39:18  sk_l****4bQx
    verdict: secret (confidence 0.96)
    | stripe.api_key = "sk_l****4bQx"

Summary: 1 finding(s) across 1 file(s), 198 suppressed [1204 files scanned in 612ms]

Install

macOS:

sh
brew install 0x1Adi/tap/klarion

Anywhere else, with Go 1.25 or newer:

sh
go install github.com/0x1Adi/Klarion/cmd/klarion@latest

Or download a binary from the releases page.

Klarion needs an AI model. Pick one:

ModelSetup
Anthropic (default)export ANTHROPIC_API_KEY=...
OpenAI or compatibleexport OPENAI_API_KEY=... and set provider = "openai"
Local model, nothing leaves your machineRun Ollama and set provider = "ollama"
Your Claude Code loginSet provider = "claude-cli"

Settings go in .klarion.toml under [ai]. All options are in the reference.

Use it

What you wantHow
Scan a folderklarion scan .
Block commits that add a secretklarion protect
Block commits with the pre-commit frameworkThe .pre-commit-config.yaml below
Stop Claude Code from writing a secret/plugin marketplace add 0x1Adi/Klarion, /plugin install klarion@klarion, then restart Claude Code
Stop Cline, Cursor or any MCP agent from writing a secretThe MCP server below
Fail pull requests that add a secretThe GitHub Action below
Start on a repo that already has findingsklarion baseline create, so only new secrets fail
yaml
# .github/workflows/secrets.yml
name: secret-scan
on: [push, pull_request]

jobs:
  klarion:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      security-events: write
    steps:
      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
      - uses: 0x1Adi/Klarion@v0.4.3
        with:
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}

On a pull request it scans only what the PR adds. It caches the AI answers, so unchanged code costs nothing. Findings show in the job log. On a private repo without GitHub Code Security, add upload-sarif: "false" to skip the Security tab upload. GitLab CI and every option are in the reference.

yaml
# .pre-commit-config.yaml
repos:
  - repo: https://github.com/0x1Adi/Klarion
    rev: v0.4.3
    hooks:
      - id: klarion
        stages: [pre-commit]

Without the stages line the hook is off and runs only when you ask for it, with pre-commit run --hook-stage manual klarion. The klarion hook builds Klarion with Go the first time it runs; use id: klarion-system if klarion is already installed. Everyone who commits needs a model set up.

json
// ~/.cline/mcp.json, or the MCP settings of any other client
{
  "mcpServers": {
    "klarion": {
      "command": "klarion",
      "args": ["mcp"]
    }
  }
}

The agent calls scan_text before it writes code and scan_file before it commits. Approve each call rather than auto-approving the server: scan_file reads any path it is given. With no model configured the server hands each candidate and its decision rules to that agent to judge, so it needs no API key. Configure one and Klarion judges instead. Full steps, including the tools and how to verify the install: llms-install.md.

Results

Klarion found about 1.7× more real secrets than gitleaks, and 89% of its alerts were real (gitleaks: 91%).

We tested Klarion v0.3.0 on CredData: 337 real open source repos where Samsung labeled the lines that hold secrets. These numbers cover code outside test folders.

Klariongitleaksdetect-secrets
Real secrets found35%21%35%
Alerts that were real89%91%34%
False alarms on flask and rails933247

Good to know:

  • Klarion skips test fixtures and docs examples on purpose. CredData counts those as real. Counted that way, Klarion found 9% and gitleaks 46%.
  • Klarion's numbers come from a sample of 612 AI checks with one model (Claude Haiku). The likely range is 30 to 40% found and 82 to 97% real.
  • CredData's labels came from what other scanners found, which helps those scanners.

Full method and data: benchmark/REPORT.md.

How it works

Step 1 runs local checks and finds candidates. Step 2 an AI model checks each one: real secrets fail the scan, the rest are hidden.
  • Step 1 runs on your machine. It looks for known key formats, password = value style lines, credential files like .pgpass, and secrets hidden in base64 or hex. It also checks how random a string looks.
  • Step 2 asks the model. Each candidate goes out with a few lines of code. The model answers secret, false positive or uncertain. Uncertain counts as a secret.
  • You can see what was hidden. Run klarion scan . --show-suppressed.
  • Your code goes only to the model you pick. Set send_secret = false to send a masked value instead. It is less accurate.

Limits

  • It needs a model. Without one, klarion scan and the commit hooks stop with an error. The Claude Code hook still blocks provider keys such as AWS, GitHub and Stripe, and the MCP server hands the candidates and its rules to your agent to judge.
  • No API key? A hosted judge with no setup is coming. Join the list.
  • The model can be wrong. Check what it hid with --show-suppressed.
  • It finds secrets, it does not fix them. Rotate any secret it finds.
  • Local hooks can be skipped with git commit --no-verify. Run Klarion in CI too, with the Action. The pre-commit hook checks staged changes only, so in CI it has nothing to check.

More

Reference · Benchmark · Design · Changelog · Contributing · Security

MIT © The Klarion Authors

Read the full README →View source on GitHub →

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Frequently Asked Questions about Klarion

We don't have a confirmed install command for Klarion yet, so we don't publish a generated one — a guessed package name would point at the wrong package or none at all. Follow the project's own README or setup instructions (https://github.com/0x1Adi/Klarion) for the current steps.

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Technical Specs & Signals

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Last updatedSep 28, 2026
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27Quality signal: Emerging · 27/100How this signal is calculated ▾
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Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

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