forked from mirrors/kingfisher
650 lines
22 KiB
Markdown
650 lines
22 KiB
Markdown
# Kingfisher
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<p align="center">
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<img src="docs/kingfisher_logo.png" alt="Kingfisher Logo" width="126" height="173" style="vertical-align: right;" />
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[](https://opensource.org/licenses/Apache-2.0)
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Kingfisher is a blazingly fast secret‑scanning and live 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
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</p>
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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.
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## What Kingfisher Adds
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- **Live validation** via cloud-provider APIs
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- **Extra targets**: GitLab repos, S3 buckets, Docker images, Jira issues, Confluence pages, and Slack messages
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- **Compressed Files**: Supports extracting and scanning compressed files for secrets
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- **Baseline mode**: ignore known secrets, flag only new ones
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- **Language-aware detection** (source-code parsing) for ~20 languages
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- **Native Windows** binary
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## Key Features
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- **Performance**: multithreaded, Hyperscan‑powered scanning built for huge codebases
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- **Extensible rules**: hundreds of built-in detectors plus YAML-defined custom rules ([docs/RULES.md](/docs/RULES.md))
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- **Multiple targets**:
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- **Git history**: local repos or GitHub/GitLab orgs/users
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- **Docker images**: public or private via `--docker-image`
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- **Jira issues**: JQL‑driven scans with `--jira-url` and `--jql`
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- **Confluence pages**: CQL‑driven scans with `--confluence-url` and `--cql`
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- **Slack messages**: query‑based scans with `--slack-query`
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- **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
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- **Compressed Files**: Supports extracting and scanning compressed files for secrets
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- **Baseline management**: generate and track baselines to suppress known secrets ([docs/BASELINE.md](/docs/BASELINE.md))
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**Learn more:** [Introducing Kingfisher: Real‑Time Secret Detection and Validation](https://www.mongodb.com/blog/post/product-release-announcements/introducing-kingfisher-real-time-secret-detection-validation)
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# Benchmark Results
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See ([docs/COMPARISON.md](docs/COMPARISON.md))
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<p align="center">
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<img src="docs/runtime-comparison.png" alt="Kingfisher Runtime Comparison" style="vertical-align: center;" />
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</p>
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# Getting Started
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## Installation
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On macOS, you can simply
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```bash
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brew install kingfisher
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```
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Pre-built binaries are also available on the [Releases](https://github.com/mongodb/kingfisher/releases) section of this page.
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You can also install using [ubi](https://github.com/houseabsolute/ubi), which downloads the correct binary for your platform:
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```bash
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# Linux, macOS
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curl --silent --location \
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https://raw.githubusercontent.com/houseabsolute/ubi/master/bootstrap/bootstrap-ubi.sh | \
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sh && \
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ubi --project mongodb/kingfisher --in "$HOME/bin"
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```
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```powershell
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# Windows
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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 .
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```
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This installs `ubi` and then places the `kingfisher` executable in `~/bin` on Unix-like
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systems (or the current directory on Windows).
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Or you may compile for your platform via `make`:
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```bash
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# NOTE: Requires Docker
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make linux
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# macOS --- must build from a macOS host
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make darwin
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# Windows x64 --- requires building from a Windows host with Visual Studio installed
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./buildwin.bat -force
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```
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```bash
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# Build all targets
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make linux-all # builds both x64 and arm64
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make darwin-all # builds both x64 and arm64
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make all # builds for every OS and architecture supported
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```
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### Run Kingfisher in Docker
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Run the dockerized Kingfisher container:
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```bash
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# GitHub Container Registry
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docker run --rm ghcr.io/mongodb/kingfisher:latest --version
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# Scan the current working directory
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# (mounts your code at /src and scans it)
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docker run --rm \
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-v "$PWD":/src \
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ghcr.io/mongodb/kingfisher:latest scan /src
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# Scan while providing a GitHub token
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# Mounts your working dir at /proj and passes in the token:
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docker run --rm \
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-e KF_GITHUB_TOKEN=ghp_… \
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-v "$PWD":/proj \
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ghcr.io/mongodb/kingfisher:latest \
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scan --git-url https://github.com/org/private_repo.git
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# Scan an S3 bucket
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# Credentials can come from KF_AWS_KEY/KF_AWS_SECRET, --role-arn, or --aws-local-profile
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docker run --rm \
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-e KF_AWS_KEY=AKIA... \
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-e KF_AWS_SECRET=g5nYW... \
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ghcr.io/mongodb/kingfisher:latest \
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scan --s3-bucket bucket-name
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# Scan and write a JSON report locally
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# Here we:
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# 1. Mount $PWD → /proj
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# 2. Tell Kingfisher to write findings.json inside /proj/reports
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# 3. Ensure ./reports exists on your host so Docker can mount it
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mkdir -p reports
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# run and output into host’s ./reports directory
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docker run --rm \
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-v "$PWD":/proj \
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ghcr.io/mongodb/kingfisher:latest \
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scan /proj \
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--format json \
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--output /proj/reports/findings.json
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# Tip: you can combine multiple mounts if you prefer separating source vs. output:
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# Here /src is read‑only, and /out holds your generated reports
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docker run --rm \
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-v "$PWD":/src:ro \
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-v "$PWD/reports":/out \
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ghcr.io/mongodb/kingfisher:latest \
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scan /src \
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--format json \
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--output /out/findings.json
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```
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# 🔐 Detection Rules at a Glance
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Kingfisher ships with hundreds of rules that cover everything from classic cloud keys to the latest LLM-API secrets. Below is an overview:
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| Category | What we catch |
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|----------|---------------|
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| **AI / LLM APIs** | OpenAI, Anthropic, Google Gemini, Cohere, Mistral, Stability AI, Replicate, xAI (Grok), and more
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| **Cloud Providers** | AWS, Azure, GCP, Alibaba Cloud, DigitalOcean, IBM Cloud, Cloudflare, and more
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| **Dev & CI/CD** | GitHub/GitLab tokens, CircleCI, TravisCI, TeamCity, Docker Hub, npm & PyPI publish token, and more
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| **Messaging & Comms** | Slack, Discord, Microsoft Teams, Twilio, Mailgun/SendGrid/Mailchimp, and more
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| **Databases & Data Ops** | MongoDB Atlas, PlanetScale, Postgres DSNs, Grafana Cloud, Datadog, Dynatrace, and more
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| **Payments & Billing** | Stripe, PayPal, Square, GoCardless, and more
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| **Security & DevSecOps** | Snyk, Dependency-Track, CodeClimate, Codacy, OpsGenie, PagerDuty, and more
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| **Misc. SaaS & Tools** | 1Password, Adobe, Atlassian/Jira, Asana, Netlify, Baremetrics, and more
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## Write Custom Rules!
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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.
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However, you may want to add your own custom rules, or modify a detection to better suit your needs / environment.
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First, review [docs/RULES.md](/docs/RULES.md) to learn how to create custom Kingfisher rules.
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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!
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# Usage
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## Basic Examples
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> **Note** `kingfisher scan` detects whether the input is a Git repository or a plain directory—no extra flags required.
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### Scan with secret validation
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```bash
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kingfisher scan /path/to/code
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## NOTE: This path can refer to:
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# 1. a local git repo
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# 2. a directory with many git repos
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# 3. or just a folder with files and subdirectories
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## To explicitly prevent scanning git commit history add:
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# `--git-history=none`
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```
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### Scan a directory containing multiple Git repositories
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```bash
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kingfisher scan /projects/mono‑repo‑dir
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```
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### Scan a Git repository without validation
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```bash
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kingfisher scan ~/src/myrepo --no-validate
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```
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### Display only secrets confirmed active by third‑party APIs
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```bash
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kingfisher scan /path/to/repo --only-valid
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```
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### Output JSON and capture to a file
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```bash
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kingfisher scan . --format json | tee kingfisher.json
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```
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### Output SARIF directly to disk
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```bash
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kingfisher scan /path/to/repo --format sarif --output findings.sarif
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```
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### Pipe any text directly into Kingfisher by passing `-`
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```bash
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cat /path/to/file.py | kingfisher scan -
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```
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### Scan using a rule _family_ with one flag
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_(prefix matching: `--rule kingfisher.aws` loads `kingfisher.aws._`)\*
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```bash
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# Only apply AWS-related rules (kingfisher.aws.1 + kingfisher.aws.2)
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kingfisher scan /path/to/repo --rule kingfisher.aws
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```
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### Display rule performance statistics
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```bash
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kingfisher scan /path/to/repo --rule-stats
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```
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### Scan while ignoring likely test files
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`--exclude` skips any file or directory whose path matches this glob pattern (repeatable, uses gitignore-style syntax, case sensitive)
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```bash
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# Scan source but skip likely unit / integration tests
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kingfisher scan ./my-project \
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--exclude='[Tt]est' \
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--exclude='spec' \
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--exclude='[Ff]ixture' \
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--exclude='example' \
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--exclude='sample'
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```
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### Exclude specific paths
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```bash
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# Skip all Python files and any directory named tests
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kingfisher scan ./my-project \
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--exclude '*.py' \
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--exclude '[Tt]ests'
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```
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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):
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```bash
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# Skip all Python files and any directory named tests, and report to stderr any skipped files
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kingfisher scan ./my-project \
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--exclude '*.py' \
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--exclude tests \
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-v
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```
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## Scan an S3 bucket
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You can scan S3 objects directly:
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```bash
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kingfisher scan --s3-bucket bucket-name [--s3-prefix path/]
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```
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Credential resolution happens in this order:
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1. `KF_AWS_KEY` and `KF_AWS_SECRET` environment variables
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2. `--aws-local-profile` pointing to a profile in `~/.aws/config` (works with AWS SSO)
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3. anonymous access for public buckets
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If `--role-arn` is supplied, the credentials from steps 1–2 are used to assume that role.
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Examples:
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```bash
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# using explicit keys
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export KF_AWS_KEY=AKIA...
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export KF_AWS_SECRET=g5nYW...
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kingfisher scan --s3-bucket some-example-bucket
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# Above can also be run as:
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KF_AWS_KEY=AKIA... KF_AWS_SECRET=g5nYW... kingfisher scan --s3-bucket some-example-bucket
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# using a local profile (e.g., SSO) that exists in your AWS profile (~/.aws/config)
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kingfisher scan --s3-bucket some-example-bucket --aws-local-profile default
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# anonymous scan of a bucket, while providing an object prefix to only scan subset of the s3 bucket
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kingfisher scan \
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--s3-bucket awsglue-datasets \
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--s3-prefix examples/us-legislators/all
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# assuming a role when scanning
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kingfisher scan --s3-bucket some-example-bucket \
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--role-arn arn:aws:iam::123456789012:role/MyRole
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# anonymous scan of a public bucket
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kingfisher scan --s3-bucket some-example-bucket
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```
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Docker example:
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```bash
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docker run --rm \
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-e KF_AWS_KEY=AKIA... \
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-e KF_AWS_SECRET=g5nYW... \
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ghcr.io/mongodb/kingfisher:latest \
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scan --s3-bucket bucket-name
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```
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## Scanning Docker Images
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Kingfisher will first try to use any locally available image, then fall back to pulling via OCI.
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Authentication happens *in this order*:
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1. **`KF_DOCKER_TOKEN`** env var
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- If it contains `user:pass`, it’s used as Basic auth
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- Otherwise it’s sent as a Bearer token
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2. **Docker CLI credentials**
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- Checks `credHelpers` (per-registry) and `credsStore` in `~/.docker/config.json`.
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- Falls back to the legacy `auths` → `auth` (base64) entries.
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3. **Anonymous** (no credentials)
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```bash
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# 1) Scan public or already-pulled image
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kingfisher scan --docker-image ghcr.io/owasp/wrongsecrets/wrongsecrets-master:latest-master
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# 2) For private registries, explicitly set KF_DOCKER_TOKEN:
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# - Basic auth: "user:pass"
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# - Bearer only: "TOKEN"
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export KF_DOCKER_TOKEN="AWS:$(aws ecr get-login-password --region us-east-1)"
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kingfisher scan --docker-image some-private-registry.dkr.ecr.us-east-1.amazonaws.com/base/amazonlinux2023:latest
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# 3) Or rely on your Docker CLI login/keychain:
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# (e.g. aws ecr get-login-password … | docker login …)
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kingfisher scan --docker-image private.registry.example.com/my-image:tag
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```
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## Scanning GitHub
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### Scan GitHub organisation (requires `KF_GITHUB_TOKEN`)
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```bash
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kingfisher scan --github-organization my-org
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```
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### Scan remote GitHub repository
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```bash
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kingfisher scan --git-url https://github.com/org/repo.git
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# Optionally provide a GitHub Token
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KF_GITHUB_TOKEN="ghp_…" kingfisher scan --git-url https://github.com/org/private_repo.git
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```
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---
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## Scanning GitLab
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### Scan GitLab group (requires `KF_GITLAB_TOKEN`)
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```bash
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kingfisher scan --gitlab-group my-group
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# include repositories from all nested subgroups
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kingfisher scan --gitlab-group my-group --gitlab-include-subgroups
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```
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### Scan GitLab user
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```bash
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kingfisher scan --gitlab-user johndoe
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```
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### Scan remote GitLab repository by URL
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```bash
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kingfisher scan --git-url https://gitlab.com/group/project.git
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```
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### List GitLab repositories
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```bash
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kingfisher gitlab repos list --group my-group
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# include repositories from all nested subgroups
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kingfisher gitlab repos list --group my-group --include-subgroups
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```
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## Scanning Jira
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### Scan Jira issues matching a JQL query
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```bash
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KF_JIRA_TOKEN="token" kingfisher scan \
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--jira-url https://jira.company.com \
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--jql "project = TEST AND status = Open" \
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--max-results 500
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```
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### Scan the last 1,000 Jira issues:
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```bash
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KF_JIRA_TOKEN="token" kingfisher scan \
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--jira-url https://jira.mongodb.org \
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--jql 'ORDER BY created DESC' \
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--max-results 1000
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```
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## Scanning Confluence
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### Scan Confluence pages matching a CQL query
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```bash
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# Bearer token
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KF_CONFLUENCE_TOKEN="token" kingfisher scan \
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--confluence-url https://confluence.company.com \
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--cql "label = secret" \
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--max-results 500
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# Basic auth with username and token
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KF_CONFLUENCE_USER="user@example.com" KF_CONFLUENCE_TOKEN="token" kingfisher scan \
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--confluence-url https://confluence.company.com \
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--cql "text ~ 'password'" \
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--max-results 500
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```
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Use the base URL of your Confluence site for `--confluence-url`. Kingfisher
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automatically adds `/rest/api` to the end, so `https://example.com/wiki` and
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`https://example.com` both work depending on your server configuration.
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Generate a personal access token and set it in the `KF_CONFLUENCE_TOKEN` environment variable. By default, Kingfisher sends the token as a bearer token in the `Authorization` header.
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To use basic authentication instead, also set `KF_CONFLUENCE_USER` to your Confluence email address; Kingfisher will then send the username and `KF_CONFLUENCE_TOKEN` as a Basic auth header. If the server responds with a redirect to a login page, the credentials are invalid or lack the required permissions.
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## Scanning Slack
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### Scan Slack messages matching a search query
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```bash
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KF_SLACK_TOKEN="xoxp-1234..." kingfisher scan \
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--slack-query "from:username has:link" \
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--max-results 1000
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KF_SLACK_TOKEN="xoxp-1234..." kingfisher scan \
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--slack-query "akia" \
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--max-results 1000
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```
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*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.*
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## Environment Variables for Tokens
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| Variable | Purpose |
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| ----------------- | ---------------------------- |
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| `KF_GITHUB_TOKEN` | GitHub Personal Access Token |
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| `KF_GITLAB_TOKEN` | GitLab Personal Access Token |
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| `KF_JIRA_TOKEN` | Jira API token |
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| `KF_CONFLUENCE_TOKEN` | Confluence API token |
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| `KF_SLACK_TOKEN` | Slack API token |
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| `KF_DOCKER_TOKEN` | Docker registry token (`user:pass` or bearer token). If unset, credentials from the Docker keychain are used |
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| `KF_AWS_KEY` and `KF_AWS_SECRET` | AWS Credentials to use with S3 bucket scanning |
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Set them temporarily per command:
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```bash
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KF_GITLAB_TOKEN="glpat-…" kingfisher scan --gitlab-group my-group
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```
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Or export for the session:
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```bash
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export KF_GITLAB_TOKEN="glpat-…"
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```
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To authenticate Jira requests:
|
||
```bash
|
||
export KF_JIRA_TOKEN="token"
|
||
```
|
||
|
||
To authenticate Confluence requests:
|
||
```bash
|
||
export KF_CONFLUENCE_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):
|
||
|
||
```bash
|
||
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:
|
||
|
||
```bash
|
||
kingfisher scan /path/to/code \
|
||
--baseline-file /path/to/baseline-file.yaml
|
||
```
|
||
|
||
Running the scan again with `--manage-baseline` refreshes the baseline by adding new findings and pruning entries for secrets that no longer appear. See [docs/BASELINE.md](docs/BASELINE.md) for full detail.
|
||
|
||
## List Builtin Rules
|
||
|
||
```bash
|
||
kingfisher rules list
|
||
```
|
||
|
||
## To scan using **only** your own `my_rules.yaml` you could run:
|
||
|
||
```bash
|
||
kingfisher scan \
|
||
--load-builtins=false \
|
||
--rules-path path/to/my_rules.yaml \
|
||
./src/
|
||
```
|
||
|
||
## To add your rules alongside the built‑ins:
|
||
|
||
```bash
|
||
kingfisher scan \
|
||
--rules-path ./custom-rules/ \
|
||
--rules-path my_rules.yml \
|
||
~/path/to/project-dir/
|
||
```
|
||
|
||
## Other Examples
|
||
|
||
```bash
|
||
# 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](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
|
||
|
||
```bash
|
||
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](https://www.bbc.com/news/technology-42075306)] [[Ars](https://arstechnica.com/tech-policy/2017/11/report-uber-paid-hackers-100000-to-keep-2016-data-breach-quiet/)]
|
||
- **AWS engineer (2020):** Pushed log files with root credentials to GitHub. [[Register](https://www.theregister.com/2020/01/23/aws_engineer_credentials_github/)] [[UpGuard](https://www.upguard.com/breaches/identity-and-access-misstep-how-an-amazon-engineer-exposed-credentials-and-more)]
|
||
- **Infosys (2023):** Full-admin AWS key left in a public PyPI package for a year. [[Stack](https://www.thestack.technology/infosys-leak-aws-key-exposed-on-pypi/)] [[Blog](https://tomforb.es/blog/infosys-leak/)]
|
||
- **Microsoft (2023):** Azure SAS token in an AI repo exposed 38 TB of internal data. [[Wiz](https://www.wiz.io/blog/38-terabytes-of-private-data-accidentally-exposed-by-microsoft-ai-researchers)] [[TechCrunch](https://techcrunch.com/2023/09/18/microsoft-ai-researchers-accidentally-exposed-terabytes-of-internal-sensitive-data/)]
|
||
- **GitHub (2023):** RSA SSH host key briefly went public; company rotated it. [[GitHub](https://github.blog/news-insights/company-news/we-updated-our-rsa-ssh-host-key/)]
|
||
|
||
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](https://github.com/mongodb/kingfisher/issues) if you have specific features you'd like added
|
||
|
||
# License
|
||
|
||
[Apache2 License](LICENSE)
|