Klarion

README

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

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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.

$ 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

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

Or download a binary from the releases page.

Klarion needs an AI model. Pick one:

Model	Setup
Anthropic (default)	export ANTHROPIC_API_KEY=...
OpenAI or compatible	export OPENAI_API_KEY=... and set provider = "openai"
Local model, nothing leaves your machine	Run Ollama and set provider = "ollama"
Your Claude Code login	Set provider = "claude-cli"

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

Use it

What you want	How
Scan a folder	klarion scan .
Block commits that add a secret	klarion protect
Block commits with the pre-commit framework	The .pre-commit-config.yaml below
Stop Claude Code from writing a secret	/plugin marketplace add 0x1Adi/Klarion then /plugin install klarion@klarion
Stop Cline, Cursor or any MCP agent from writing a secret	The MCP server below
Fail pull requests that add a secret	The GitHub Action below
Start on a repo that already has findings	klarion baseline create, so only new secrets fail

# .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.

# .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.

// ~/.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.

	Klarion	gitleaks	detect-secrets
Real secrets found	35%	21%	35%
Alerts that were real	89%	91%	34%
False alarms on flask and rails	9	33	247

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 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