GitHub - mongodb/kingfisher: Kingfisher is a blazingly fast and highly accurate tool for secret detection and live validation across files, Git repos, GitHub, GitLab, Azure Repos, BitBucket, Gitea, AWS S3, Docker images, Jira, Slack, and Confluence

Kingfisher

Kingfisher is a blazingly fast secret-scanning and live validation tool built in Rust.

It combines Intel's SIMD-accelerated regex engine (Hyperscan) with language-aware parsing to achieve high accuracy at massive scale, and ships with hundreds of built-in rules to detect, validate, and triage secrets before they ever reach production.

Designed for offensive security engineers and blue-teamers alike, Kingfisher helps you pivot across repo ecosystems, validate exposure paths, and hunt for developer-owned leaks that spill beyond the primary codebase.

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

Key Features

Multiple Scan Targets

Performance, Accuracy, and Hundreds of Rules

• Performance: multithreaded, Hyperscan‑powered scanning built for huge codebases
• Extensible rules: hundreds of built-in detectors plus YAML-defined custom rules (docs/RULES.md)
• Validate & Revoke: live validation of discovered secrets, plus direct revocation for supported platforms (GitHub, GitLab, Slack, AWS, GCP, and more) (docs/USAGE.md)
• Blast Radius Mapping: instantly map leaked keys to their effective cloud identities and exposed resources with --access-map. Supports AWS, GCP, Azure, GitHub, Gitlab, and more token support coming.
• Broad AI SaaS coverage: finds and validates tokens for OpenAI, Anthropic, Google Gemini, Cohere, AWS Bedrock, Voyage AI, Mistral, Stability AI, Replicate, xAI (Grok), Ollama, Langchain, Perplexity, Weights & Biases, Cerebras, Friendli, Fireworks.ai, NVIDIA NIM, Together.ai, Zhipu, and many more
• Compressed Files: Supports extracting and scanning compressed files for secrets
• Baseline management: generate and track baselines to suppress known secrets (docs/BASELINE.md)
• Checksum-aware detection: verifies tokens with built-in checksums (e.g., GitHub, Confluent, Zuplo) — no API calls required
• Built-in Report Viewer: Visualize and triage findings locally with kingfisher view ./report-file.json
• Library crates: Embed Kingfisher's scanning engine in your own Rust applications (docs/LIBRARY.md)

Benchmark Results

See (docs/COMPARISON.md)

Basic Usage Demo

kingfisher scan /path/to/scan --view-report

NOTE: Replay has been slowed down for demo

Report Viewer Demo

Explore Kingfisher's built-in report viewer and its --access-map, which can show what the token (AWS, GCP, Azure, GitHub, GitLab, and Slack...more coming) can actually access.

Note: when you pass --view-report, Kingfisher starts a localhost-only web server on port 7890 and opens it in your default browser. You'll see this near the end of the scan output, and Kingfisher will keep running until you stop it.

INFO kingfisher::cli::commands::view: Starting access-map viewer address=127.0.0.1:7890
Serving access-map viewer at http://127.0.0.1:7890 (Ctrl+C to stop)

Usage:

kingfisher scan /path/to/scan --access-map --view-report

Click to view video

Table of Contents

• Key Features
• Benchmark Results
• Getting Started
  • Quick Start
  • Installation
• Detection Rules
• Usage Examples
• Platform Integrations
  • Environment Variables
• Advanced Features
• Documentation
• Library Usage
• Roadmap
• License

Getting Started

Quick Start

1: Install Kingfisher

# Homebrew
brew install kingfisher

# Or use the install script (Linux/macOS)
curl -sSL https://raw.githubusercontent.com/mongodb/kingfisher/main/scripts/install-kingfisher.sh | bash

2: Scan a directory for secrets

kingfisher scan /path/to/code

3: Scan and view results in browser

kingfisher scan /path/to/code --view-report

4: Show only validated (live) secrets

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

5: Revoke a discovered secret

# Revoke a GitHub token
kingfisher revoke --rule github "ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Revoke AWS credentials (sets access key to Inactive)
kingfisher revoke --rule aws --arg "AKIAIOSFODNN7EXAMPLE" "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"

6: Scan a GitHub organization

KF_GITHUB_TOKEN="ghp_..." kingfisher scan github --organization my-org

7: Scan a GitLab group

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

8: Scan Azure Repos

KF_AZURE_PAT="pat" kingfisher scan azure --organization my-org

9: Scan Bitbucket workspace

KF_BITBUCKET_TOKEN="token" kingfisher scan bitbucket --workspace my-team

10: Scan Gitea organization

KF_GITEA_TOKEN="token" kingfisher scan gitea --organization my-org

11: Scan Hugging Face

KF_HUGGINGFACE_TOKEN="hf_..." kingfisher scan huggingface --organization my-org

12: Scan an S3 bucket

kingfisher scan s3 bucket-name --prefix path/

13: Scan Google Cloud Storage

kingfisher scan gcs bucket-name --prefix path/

14: Scan a Docker image

kingfisher scan docker ghcr.io/org/image:latest

15: Scan Jira issues

KF_JIRA_TOKEN="token" kingfisher scan jira --url https://jira.company.com --jql "project = SEC"

16: Scan Confluence pages

KF_CONFLUENCE_TOKEN="token" kingfisher scan confluence --url https://confluence.company.com --cql "label = secret"

17: Scan Slack messages

KF_SLACK_TOKEN="xoxp-..." kingfisher scan slack "api_key OR password"

18: Run with Docker (no install required)

docker run --rm -v "$PWD":/src ghcr.io/mongodb/kingfisher:latest scan /src

19: Output JSON results

kingfisher scan /path/to/code --format json --output findings.json

20: Map blast radius of discovered credentials

kingfisher scan /path/to/code --access-map --view-report

Installation

Kingfisher supports multiple installation methods:

• Homebrew: brew install kingfisher
• Pre-built releases: Download from GitHub Releases
• Install scripts: One-line installers for Linux, macOS, and Windows
• Docker: docker run ghcr.io/mongodb/kingfisher:latest
• Pre-commit hooks: Integrate with git hooks, pre-commit framework, or Husky
• Compile from source: Build with make for your platform

For complete installation instructions and pre-commit hook setup, see docs/INSTALLATION.md.

Detection Rules

Kingfisher ships with hundreds of rules that cover everything from classic cloud keys to the latest AI SaaS tokens. Below is an overview:

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.

For complete rule documentation, see docs/RULES.md.

Checksum Intelligence

Modern API tokens increasingly include built-in checksums, short internal digests that make each credential self-verifiable. (For background, see GitHub's write-up on their newer token formats and why checksums slash false positives.)

Kingfisher supports checksum-aware matching in rules, enabling offline structural verification of credentials without calling third-party APIs.

By validating each token's internal checksum (for tokens that support checksums), Kingfisher eliminates nearly all false positives—automatically skipping structurally invalid or fake tokens before validation ever runs.

Why this matters

• Offline verification — no API call required
• Industry-aligned — compatible with prefix + checksum token designs (e.g., modern PATs)
• Lower false positives — invalid tokens are filtered out by structure alone

Learn more: implementation details and templating are documented in docs/RULES.md

Usage Examples

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

Basic Scanning

# 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

# Scan 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

Access Map and Visualization

Stop Guessing, Start Mapping: Understand Your True Blast Radius

Finding a leaked credential is only the first step. The critical question isn't just "Is this a secret?"—it's "What can an attacker do with it?"

Kingfisher's --access-map feature transforms secret detection from a simple alert into a comprehensive threat assessment. Instead of leaving you with a cryptic API key, Kingfisher actively authenticates against your cloud provider (AWS, GCP, Azure Storage, Azure DevOps, GitHub, GitLab, or Slack) to map the full extent of the credential's power.

• Instant Identity Resolution: Immediately identify who the key belongs to—whether it's a specific IAM user, an assumed role, or a service account.
• Visualize the Blast Radius: See exactly which resources (S3 buckets, EC2 instances, projects, storage containers) are exposed and at risk.

# Generate access map during scan
kingfisher scan /path/to/code --access-map --view-report

# View access-map reports locally
kingfisher view kingfisher.json

Use the access map functionality only when you are authorized to inspect the target account, as Kingfisher will issue additional network requests to determine what access the secret grants

Direct Secret Validation & Revocation

# Validate a known secret without scanning
kingfisher validate --rule opsgenie "12345678-9abc-def0-1234-56789abcdef0"

# Validate from stdin
echo "ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" | kingfisher validate --rule github -

# Revoke a Slack token
kingfisher revoke --rule slack "xoxb-..."

# Revoke a GitHub PAT
kingfisher revoke --rule github "ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Advanced Scanning Options

# Pipe any text directly into Kingfisher
cat /path/to/file.py | kingfisher scan -

# Limit maximum file size scanned (default: 256 MB)
kingfisher scan /some/file --max-file-size 500

# Scan using a rule family
kingfisher scan /path/to/repo --rule kingfisher.aws

# Display rule performance statistics
kingfisher scan /path/to/repo --rule-stats

# Exclude specific paths
kingfisher scan ./my-project \
  --exclude '*.py' \
  --exclude '[Tt]ests'

# Scan changes in CI pipelines
kingfisher scan . \
  --since-commit origin/main \
  --branch "$CI_BRANCH"

Platform Integrations

Kingfisher can scan multiple platforms and services directly:

Version Control & Code Hosting:

• GitHub (organizations, users, repositories)
• GitLab (groups, users, projects)
• Azure Repos (organizations, projects)
• Bitbucket (workspaces, users, repositories)
• Gitea (organizations, users, repositories)
• Hugging Face (models, datasets, spaces)

Cloud Storage:

• AWS S3
• Google Cloud Storage

Containers:

• Docker (images from registries)

Collaboration & Documentation:

• Jira (issues via JQL queries)
• Confluence (pages via CQL queries)
• Slack (messages via search queries)

See docs/INTEGRATIONS.md for complete integration documentation and authentication setup.

Quick Examples

# Scan AWS S3 bucket
kingfisher scan s3 bucket-name --prefix path/

# Scan Google Cloud Storage
kingfisher scan gcs bucket-name

# Scan Docker image
kingfisher scan docker ghcr.io/owasp/wrongsecrets/wrongsecrets-master:latest-master

# Scan GitHub organization
kingfisher scan github --organization my-org

# Scan GitLab group
kingfisher scan gitlab --group my-group

# Scan Azure Repos
kingfisher scan azure --organization my-org

# Scan Jira issues
KF_JIRA_TOKEN="token" kingfisher scan jira --url https://jira.company.com \
  --jql "project = TEST AND status = Open"

# Scan Confluence pages
KF_CONFLUENCE_TOKEN="token" kingfisher scan confluence --url https://confluence.company.com \
  --cql "label = secret"

# Scan Slack messages
KF_SLACK_TOKEN="xoxp-..." kingfisher scan slack "from:username has:link"

For detailed integration instructions and authentication setup, see docs/INTEGRATIONS.md.

Environment Variables

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-…"

Advanced Features

Kingfisher offers powerful features for complex scanning scenarios. See docs/ADVANCED.md for complete advanced documentation.

Baseline Management

Track known secrets and detect only new ones:

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

# Scan with baseline (suppress known findings)
kingfisher scan /path/to/code \
  --baseline-file /path/to/baseline-file.yaml

Filtering and Suppression

# Skip known false positives
kingfisher scan --skip-regex '(?i)TEST_KEY' path/
kingfisher scan --skip-word dummy path/

# Skip AWS canary tokens
kingfisher scan /path/to/code \
  --skip-aws-account "171436882533,534261010715"

# Inline ignore directives in code
# Add `kingfisher:ignore` on the same line or surrounding lines

CI Pipeline Scanning

# Scan only changes between branches
kingfisher scan . \
  --since-commit origin/main \
  --branch "$CI_BRANCH"

# Scan specific commit range
kingfisher scan /tmp/repo --branch feature-1 \
  --branch-root-commit $(git -C /tmp/repo merge-base main feature-1)

For more advanced features including confidence levels, validation tuning, and custom rules, see docs/ADVANCED.md.

Documentation

Library Usage

(beta feature) - Kingfisher's scanning engine is available as a set of Rust library crates (kingfisher-core, kingfisher-rules, kingfisher-scanner) that can be embedded into other applications. This enables you to integrate secret scanning directly into your own tools and workflows.

For complete documentation and examples, see docs/LIBRARY.md.

Exit Codes

Lineage and Evolution

Kingfisher began as an internal fork of Nosey Parker, used as a high-performance foundation for secret detection.

Since then it has evolved far beyond that starting point, introducing live validation, hundreds of new rules, additional scan targets, and major architectural changes across nearly every subsystem.

Key areas of evolution

• Live validation of detected secrets directly within rules
• Hundreds of new built-in rules and an expanded YAML rule schema
• Baseline management to suppress known findings over time
• Tree-sitter parsing layered on Hyperscan for language-aware detection
• More scan targets (GitLab, Bitbucket, Gitea, Jira, Confluence, Slack, S3, GCS, Docker, Hugging Face, etc.)
• Compressed Files scanning support added
• New storage model (in-memory + Bloom filter, replacing SQLite)
• Unified workflow with JSON/BSON/SARIF outputs
• Cross-platform builds for Linux, macOS, and Windows

Roadmap

• More rules
• More targets
• Please file a feature request, or open a PR, if you have features you'd like added

License

Apache2 License