Kingfisher

Kingfisher is a blazingly fast secret‑scanning and validation tool built in Rust. It combines Intel’s hardware‑accelerated Hyperscan regex engine with language‑aware parsing via Tree‑Sitter, and ships with hundreds of built‑in rules to detect, validate, and triage secrets before they ever reach production

Kingfisher originated as a fork of Praetorian's Nosey Parker, and is built atop their incredible work and the work contributed by the Nosey Parker community.

What Kingfisher Adds

• Live validation via cloud-provider APIs
• Language-aware detection (source-code parsing) for ~20 languages
• Extra targets: GitLab repos, S3 buckets, Docker images, Jira issues, and Slack messages
• Baseline mode: ignore known secrets, flag only new ones
• Native Windows binary

Key Features

• Performance: multithreaded, Hyperscan‑powered scanning built for huge codebases
• Extensible rules: hundreds of built-in detectors plus YAML-defined custom rules (docs/RULES.md)
• Multiple targets:
  • Git history: local repos or GitHub/GitLab orgs/users
  • Docker images: public or private via --docker-image
  • Jira issues: JQL‑driven scans with --jira-url and --jql
  • Slack messages: query‑based scans with --slack-query
  • AWS S3: bucket scans via --s3-bucket/--s3-prefix with credentials from KF_AWS_KEY/KF_AWS_SECRET, --role-arn, --aws-local-profile, or anonymous
• Baseline management: generate and track baselines to suppress known secrets (docs/BASELINE.md)

Learn more: Introducing Kingfisher: Real‑Time Secret Detection and Validation

Benchmark Results

See (docs/COMPARISON.md)

Getting Started

Installation

On macOS, you can simply

brew install kingfisher

Pre-built binaries are also available on the Releases section of this page.

You can also install using ubi, which downloads the correct binary for your platform:

# Linux, macOS
curl --silent --location \
    https://raw.githubusercontent.com/houseabsolute/ubi/master/bootstrap/bootstrap-ubi.sh | \
    sh && \
  ubi --project mongodb/kingfisher --in "$HOME/bin"

# Windows
powershell -exec bypass -c "Invoke-WebRequest -URI 'https://raw.githubusercontent.com/houseabsolute/ubi/master/bootstrap/bootstrap-ubi.ps1' -UseBasicParsing | Invoke-Expression" && ubi --project mongodb/kingfisher --in .

This installs ubi and then places the kingfisher executable in ~/bin on Unix-like systems (or the current directory on Windows).

Or you may compile for your platform via make:

# NOTE: Requires Docker
make linux

# macOS --- must build from a macOS host
make darwin

# Windows x64 --- requires building from a Windows host with Visual Studio installed
./buildwin.bat -force

# Build all targets
make linux-all # builds both x64 and arm64
make darwin-all # builds both x64 and arm64
make all # builds for every OS and architecture supported

Run Kingfisher in Docker

Run the dockerized Kingfisher container:

# GitHub Container Registry 
docker run --rm ghcr.io/mongodb/kingfisher:latest --version

# Scan the current working directory
# (mounts your code at /src and scans it)
docker run --rm \
  -v "$PWD":/src \
  ghcr.io/mongodb/kingfisher:latest scan /src


# Scan while providing a GitHub token
# Mounts your working dir at /proj and passes in the token:
docker run --rm \
  -e KF_GITHUB_TOKEN=ghp_… \
  -v "$PWD":/proj \
  ghcr.io/mongodb/kingfisher:latest \
    scan --git-url https://github.com/org/private_repo.git

# Scan an S3 bucket
# Credentials can come from KF_AWS_KEY/KF_AWS_SECRET, --role-arn, or --aws-local-profile
docker run --rm \
  -e KF_AWS_KEY=AKIA... \
  -e KF_AWS_SECRET=g5nYW... \
  ghcr.io/mongodb/kingfisher:latest \
    scan --s3-bucket bucket-name


# Scan and write a JSON report locally
# Here we:
#    1. Mount $PWD → /proj
#    2. Tell Kingfisher to write findings.json inside /proj/reports
#   3. Ensure ./reports exists on your host so Docker can mount it
mkdir -p reports

# run and output into host’s ./reports directory
docker run --rm \
  -v "$PWD":/proj \
  ghcr.io/mongodb/kingfisher:latest \
    scan /proj \
    --format json \
    --output /proj/reports/findings.json


# Tip: you can combine multiple mounts if you prefer separating source vs. output:
# Here /src is read‑only, and /out holds your generated reports
docker run --rm \
  -v "$PWD":/src:ro \
  -v "$PWD/reports":/out \
  ghcr.io/mongodb/kingfisher:latest \
    scan /src \
    --format json \
    --output /out/findings.json

🔐 Detection Rules at a Glance

Kingfisher ships with hundreds of rules that cover everything from classic cloud keys to the latest LLM-API secrets. Below is an overview:

Category	What we catch
AI / LLM APIs	OpenAI, Anthropic, Google Gemini, Cohere, Mistral, Stability AI, Replicate, xAI (Grok), and more
Cloud Providers	AWS, Azure, GCP, Alibaba Cloud, DigitalOcean, IBM Cloud, Cloudflare, and more
Dev & CI/CD	GitHub/GitLab tokens, CircleCI, TravisCI, TeamCity, Docker Hub, npm & PyPI publish token, and more
Messaging & Comms	Slack, Discord, Microsoft Teams, Twilio, Mailgun/SendGrid/Mailchimp, and more
Databases & Data Ops	MongoDB Atlas, PlanetScale, Postgres DSNs, Grafana Cloud, Datadog, Dynatrace, and more
Payments & Billing	Stripe, PayPal, Square, GoCardless, and more
Security & DevSecOps	Snyk, Dependency-Track, CodeClimate, Codacy, OpsGenie, PagerDuty, and more
Misc. SaaS & Tools	1Password, Adobe, Atlassian/Jira, Asana, Netlify, Baremetrics, and more

Write Custom Rules!

Kingfisher ships with hundreds of rules with HTTP and service‑specific validation checks (AWS, Azure, GCP, etc.) to confirm if a detected string is a live credential.

However, you may want to add your own custom rules, or modify a detection to better suit your needs / environment.

First, review docs/RULES.md to learn how to create custom Kingfisher rules.

Once you've done that, you can provide your custom rules (defined in a YAML file) and provide it to Kingfisher at runtime --- no recompiling required!

Usage

Basic Examples

Note

  kingfisher scan detects whether the input is a Git repository or a plain directory—no extra flags required.

Scan with secret validation

kingfisher scan /path/to/code
## NOTE: This path can refer to:
# 1. a local git repo
# 2. a directory with many git repos
# 3. or just a folder with files and subdirectories

## To explicitly prevent scanning git commit history add:
#   `--git-history=none`

Scan a directory containing multiple Git repositories

kingfisher scan /projects/mono‑repo‑dir

Scan a Git repository without validation

kingfisher scan ~/src/myrepo --no-validate

Display only secrets confirmed active by third‑party APIs

kingfisher scan /path/to/repo --only-valid

Output JSON and capture to a file

kingfisher scan . --format json | tee kingfisher.json

Output SARIF directly to disk

kingfisher scan /path/to/repo --format sarif --output findings.sarif

Pipe any text directly into Kingfisher by passing -

cat /path/to/file.py | kingfisher scan -

Scan using a rule family with one flag

_(prefix matching: --rule kingfisher.aws loads kingfisher.aws._)*

# Only apply AWS-related rules (kingfisher.aws.1 + kingfisher.aws.2)
kingfisher scan /path/to/repo --rule kingfisher.aws

Display rule performance statistics

kingfisher scan /path/to/repo --rule-stats

Scan while ignoring likely test files

--exclude skips any file or directory whose path matches this glob pattern (repeatable, uses gitignore-style syntax, case sensitive)

# Scan source but skip likely unit / integration tests
kingfisher scan ./my-project \
  --exclude='[Tt]est' \
  --exclude='spec' \
  --exclude='[Ff]ixture' \
  --exclude='example' \
  --exclude='sample'

Exclude specific paths

# Skip all Python files and any directory named tests
kingfisher scan ./my-project \
  --exclude '*.py' \
  --exclude '[Tt]ests'

If you want to know which files are being skipped, enable verbose debugging (-v) when scanning, which will report any files being skipped by the baseline file (or via --exclude):

# Skip all Python files and any directory named tests, and report to stderr any skipped files
kingfisher scan ./my-project \
  --exclude '*.py' \
  --exclude tests \
  -v

Scan an S3 bucket

You can scan S3 objects directly:

kingfisher scan --s3-bucket bucket-name [--s3-prefix path/]

Credential resolution happens in this order:

1. KF_AWS_KEY and KF_AWS_SECRET environment variables
2. --aws-local-profile pointing to a profile in ~/.aws/config (works with AWS SSO)
3. anonymous access for public buckets

If --role-arn is supplied, the credentials from steps 1–2 are used to assume that role.

Examples:

# using explicit keys
export KF_AWS_KEY=AKIA...
export KF_AWS_SECRET=g5nYW...
kingfisher scan --s3-bucket some-example-bucket

# Above can also be run as:
KF_AWS_KEY=AKIA... KF_AWS_SECRET=g5nYW... kingfisher scan --s3-bucket some-example-bucket

# using a local profile (e.g., SSO) that exists in your AWS profile (~/.aws/config)
kingfisher scan --s3-bucket some-example-bucket --aws-local-profile default

# anonymous scan of a bucket, while providing an object prefix to only scan subset of the s3 bucket
kingfisher scan \
  --s3-bucket awsglue-datasets \
  --s3-prefix examples/us-legislators/all

# assuming a role when scanning
kingfisher scan --s3-bucket some-example-bucket \
  --role-arn arn:aws:iam::123456789012:role/MyRole

# anonymous scan of a public bucket
kingfisher scan --s3-bucket some-example-bucket

Docker example:

docker run --rm \
  -e KF_AWS_KEY=AKIA... \
  -e KF_AWS_SECRET=g5nYW... \
  ghcr.io/mongodb/kingfisher:latest \
    scan --s3-bucket bucket-name

Scanning Docker Images

Kingfisher will first try to use any locally available image, then fall back to pulling via OCI.

Authentication happens in this order:

1. KF_DOCKER_TOKEN env var
   • If it contains user:pass, it’s used as Basic auth
   • Otherwise it’s sent as a Bearer token
2. Docker CLI credentials
   • Checks credHelpers (per-registry) and credsStore in ~/.docker/config.json.
   • Falls back to the legacy auths → auth (base64) entries.
3. Anonymous (no credentials)

# 1) Scan public or already-pulled image
kingfisher scan --docker-image ghcr.io/owasp/wrongsecrets/wrongsecrets-master:latest-master

# 2) For private registries, explicitly set KF_DOCKER_TOKEN:
#    - Basic auth:     "user:pass"
#    - Bearer only:    "TOKEN"
export KF_DOCKER_TOKEN="AWS:$(aws ecr get-login-password --region us-east-1)"
kingfisher scan --docker-image some-private-registry.dkr.ecr.us-east-1.amazonaws.com/base/amazonlinux2023:latest

# 3) Or rely on your Docker CLI login/keychain:
#    (e.g. aws ecr get-login-password … | docker login …)
kingfisher scan --docker-image private.registry.example.com/my-image:tag

Scanning GitHub

Scan GitHub organisation (requires KF_GITHUB_TOKEN)

kingfisher scan --github-organization my-org

Scan remote GitHub repository

kingfisher scan --git-url https://github.com/org/repo.git

# Optionally provide a GitHub Token
KF_GITHUB_TOKEN="ghp_…" kingfisher scan --git-url https://github.com/org/private_repo.git

---

Scanning GitLab

Scan GitLab group (requires KF_GITLAB_TOKEN)

kingfisher scan --gitlab-group my-group

Scan GitLab user

kingfisher scan --gitlab-user johndoe

Scan remote GitLab repository by URL

kingfisher scan --git-url https://gitlab.com/group/project.git

List GitLab repositories

kingfisher gitlab repos list --group my-group

Scanning Jira

Scan Jira issues matching a JQL query

KF_JIRA_TOKEN="token" kingfisher scan \
    --jira-url https://jira.company.com \
    --jql "project = TEST AND status = Open" \
    --max-results 500

Scan the last 1,000 Jira issues:

KF_JIRA_TOKEN="token" kingfisher scan \
  --jira-url https://jira.mongodb.org \
  --jql 'ORDER BY created DESC' \
  --max-results 1000

---

Scanning Slack

Scan Slack messages matching a search query

KF_SLACK_TOKEN="xoxp-1234..." kingfisher scan \
    --slack-query "from:username has:link" \
    --max-results 1000

KF_SLACK_TOKEN="xoxp-1234..." kingfisher scan \
    --slack-query "akia" \
    --max-results 1000

The Slack token must be a user token with the search:read scope. Bot tokens (those beginning with xoxb-) cannot call the Slack search API.

Environment Variables for Tokens

Variable	Purpose
KF_GITHUB_TOKEN	GitHub Personal Access Token
KF_GITLAB_TOKEN	GitLab Personal Access Token
KF_JIRA_TOKEN	Jira API token
KF_SLACK_TOKEN	Slack API token
KF_DOCKER_TOKEN	Docker registry token (user:pass or bearer token). If unset, credentials from the Docker keychain are used
KF_AWS_KEY and KF_AWS_SECRET	AWS Credentials to use with S3 bucket scanning

Set them temporarily per command:

KF_GITLAB_TOKEN="glpat-…" kingfisher scan --gitlab-group my-group

Or export for the session:

export KF_GITLAB_TOKEN="glpat-…"

To authenticate Jira requests:

export KF_JIRA_TOKEN="token"

If no token is provided Kingfisher still works for public repositories.

---

Exit Codes

Code	Meaning
0	No findings
200	Findings discovered
205	Validated findings discovered

Update Checks

Kingfisher automatically queries GitHub for a newer release when it starts and tells you whether an update is available.

• Hands-free updates – Add --self-update to any Kingfisher command

  • If a newer version exists, Kingfisher will download it, replace the running binary, and re-launch itself with the exact same arguments.
  • If the update fails or no newer release is found, the current run proceeds as normal

• Disable version checks – Pass --no-update-check to skip both the startup and shutdown checks entirely

Advanced Options

Build a Baseline / Detect New Secrets

There are situations where a repository already contains checked‑in secrets, but you want to ensure no new secrets are introduced. A baseline file lets you document the known findings so future scans only report anything that is not already in that list.

The easiest way to create a baseline is to run a normal scan with the --manage-baseline flag (typically at a low confidence level to capture all potential matches):

kingfisher scan /path/to/code \
  --confidence low \
  --manage-baseline \
  --baseline-file ./baseline-file.yml

Use the same YAML file with the --baseline-file option on future scans to hide all recorded findings:

kingfisher scan /path/to/code \
  --baseline-file /path/to/baseline-file.yaml

See (docs/BASELINE.md) for full detail.

List Builtin Rules

kingfisher rules list

To scan using only your own my_rules.yaml you could run:

kingfisher scan \
  --load-builtins=false \
  --rules-path path/to/my_rules.yaml \
  ./src/

To add your rules alongside the built‑ins:

kingfisher scan \
  --rules-path ./custom-rules/ \
  --rules-path my_rules.yml \
  ~/path/to/project-dir/

Other Examples

# Check custom rules - this ensures all regular expressions compile, and can match the rule's `examples` in the YML file
kingfisher rules check --rules-path ./my_rules.yml

# List GitHub repos
kingfisher github repos list --user my-user
kingfisher github repos list --organization my-org

Notable Scan Options

• --no-dedup: Report every occurrence of a finding (disable the default de-duplicate behavior)
• --confidence <LEVEL>: (low|medium|high)
• --min-entropy <VAL>: Override default threshold
• --no-binary: Skip binary files
• --no-extract-archives: Do not scan inside archives
• --extraction-depth <N>: Specifies how deep nested archives should be extracted and scanned (default: 2)
• --redact: Replaces discovered secrets with a one-way hash for secure output
• --exclude <PATTERN>: Skip any file or directory whose path matches this glob pattern (repeatable, uses gitignore-style syntax, case sensitive)
• --baseline-file <FILE>: Ignore matches listed in a baseline YAML file
• --manage-baseline: Create or update the baseline file with current findings

Finding Fingerprint

The document below details the four-field formula (rule SHA-1, origin label, start & end offsets) hashed with XXH3-64 to create Kingfisher’s 64-bit finding fingerprint, and explains how this ID powers safe deduplication; plus how --no-dedup can be used shows every raw match. See (docs/FINGERPRINT.md)

Rule Performance Profiling

Use --rule-stats to collect timing information for every rule. After scanning, the summary prints a Rule Performance Stats section showing how many matches each rule produced along with its slowest and average match times. Useful when creating rules or debugging rules.

CLI Options

kingfisher scan --help

Business Value

By integrating Kingfisher into your development lifecycle, you can:

• Prevent Costly Breaches
  Early detection of embedded credentials avoids expensive incident response, legal fees, and reputation damage
• Automate Compliance
  Enforce secret‑scanning policies across GitOps, CI/CD, and pull requests to help satisfy SOC 2, PCI‑DSS, GDPR, and other standards
• Reduce Noise, Focus on Real Threats
  Validation logic filters out false positives and highlights only active, valid secrets (--only-valid)
• Accelerate Dev Workflows
  Run in parallel across dozens of languages, integrate with GitHub Actions or any pipeline, and shift security left to minimize delays

The Risk of Leaked Secrets

Real breaches show how one exposed key can snowball into a full-scale incident:

• Uber (2016): GitHub-hosted AWS key let attackers access data on 57 M riders and 600 k drivers. [BBC] [Ars]
• AWS engineer (2020): Pushed log files with root credentials to GitHub. [Register] [UpGuard]
• Infosys (2023): Full-admin AWS key left in a public PyPI package for a year. [Stack] [Blog]
• Microsoft (2023): Azure SAS token in an AI repo exposed 38 TB of internal data. [Wiz] [TechCrunch]
• GitHub (2023): RSA SSH host key briefly went public; company rotated it. [GitHub]

Leaked secrets fuel unauthorized access, lateral movement, regulatory fines, and brand-damaging incident-response costs.

Roadmap

• More rules
• More targets
• Please file a feature request if you have specific features you'd like added

License

Apache2 License