GuardDog

GuardDog is a CLI tool that allows to identify malicious PyPI and npm packages or Go modules. It runs a set of heuristics on the package source code (through Semgrep rules) and on the package metadata.

GuardDog can be used to scan local or remote PyPI and npm packages or Go modules using any of the available heuristics.

Getting started

Installation

pip install guarddog

Or use the Docker image:

docker pull ghcr.io/datadog/guarddog
alias guarddog='docker run --rm ghcr.io/datadog/guarddog'

Note: On Windows, the only supported installation method is Docker.

Sample usage

# Scan the most recent version of the 'requests' package
guarddog pypi scan requests

# Scan a specific version of the 'requests' package
guarddog pypi scan requests --version 2.28.1

# Scan the 'request' package using 2 specific heuristics
guarddog pypi scan requests --rules exec-base64 --rules code-execution

# Scan the 'requests' package using all rules but one
guarddog pypi scan requests --exclude-rules exec-base64

# Scan a local package archive
guarddog pypi scan /tmp/triage.tar.gz

# Scan a local package directory
guarddog pypi scan /tmp/triage/

# Scan every package referenced in a requirements.txt file of a local folder
guarddog pypi verify workspace/guarddog/requirements.txt

# Scan every package referenced in a requirements.txt file and output a sarif file - works only for verify
guarddog pypi verify --output-format=sarif workspace/guarddog/requirements.txt

# Output JSON to standard output - works for every command
guarddog pypi scan requests --output-format=json

# All the commands also work on npm or go
guarddog npm scan express

# Run in debug mode
guarddog --log-level debug npm scan express

Heuristics

GuardDog comes with 2 types of heuristics:

• Source code heuristics: Semgrep rules running against the package source code.

• Package metadata heuristics: Python or Javascript heuristics running against the package metadata on PyPI or npm.

PyPI

Source code heuristics:

Metadata heuristics:

npm

Source code heuristics:

Metadata heuristics:

go

Source code heuristics:

Custom Rules

Guarddog allows to implement custom sourcecode rules. Sourcecode rules live under the guarddog/analyzer/sourcecode directory, and supported formats are Semgrep or Yara.

• Semgrep rules are language-dependent, and Guarddog will import all .yml rules where the language matches the ecosystem selected by the user in CLI.
• Yara rules on the other hand are language agnostic, therefore all matching .yar rules present will be imported.

Is possible then to write your own rule and drop it into that directory, Guarddog will allow you to select it or exclude it as any built-in rule as well as appending the findings to its output.

For example, you can create the following semgrep rule:

rules:
  - id: sample-rule 
    languages:
      - python
    message: Output message when rule matches
    metadata:
      description: Description used in the CLI help
    patterns:
        YOUR RULE HEURISTICS GO HERE  
    severity: WARNING

Then you'll need to save it as sample-rule.yml and note that the id must match the filename

In the case of Yara, you can create the following rule:

rule sample-rule
{
  meta:
    description = "Description used in the output message"
    target_entity = "file"
  strings:
    $exec = "exec"
  condition:
    1 of them
}

Then you'll need to save it as sample-rule.yar.

Note that in both cases, the rule id must match the filename

Running GuardDog in a GitHub Action

The easiest way to integrate GuardDog in your CI pipeline is to leverage the SARIF output format, and upload it to GitHub's code scanning feature.

Using this, you get:

• Automated comments to your pull requests based on the GuardDog scan output
• Built-in false positive management directly in the GitHub UI

Sample GitHub Action using GuardDog:

name: GuardDog

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

permissions:
  contents: read

jobs:
  guarddog:
    permissions:
      contents: read # for actions/checkout to fetch code
      security-events: write # for github/codeql-action/upload-sarif to upload SARIF results
    name: Scan dependencies
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.10"

      - name: Install GuardDog
        run: pip install guarddog

      - run: guarddog pypi verify requirements.txt --output-format sarif --exclude-rules repository_integrity_mismatch > guarddog.sarif

      - name: Upload SARIF file to GitHub
        uses: github/codeql-action/upload-sarif@v3
        with:
          category: guarddog-builtin
          sarif_file: guarddog.sarif

Development

Running a local version of GuardDog

Using pip

• Ensure >=python3.10 is installed
• Clone the repository
• Create a virtualenv: python3 -m venv venv && source venv/bin/activate
• Install requirements: pip install -r requirements.txt
• Run GuardDog using python -m guarddog

Using poetry

• Ensure poetry has an env with python >=3.10 poetry env use 3.10.0
• Install dependencies poetry install
• Run guarddog poetry run guarddog or poetry shell then run guarddog

Unit tests

Running all unit tests: make test

Running unit tests against Semgrep rules: make test-semgrep-rules (tests are here). These use the standard methodology for testing Semgrep rules.

Running unit tests against package metadata heuristics: make test-metadata-rules (tests are here).

Benchmarking

You can run GuardDog on legitimate and malicious packages to determine false positives and false negatives. See ./tests/samples

Code quality checks

Run the type checker with

mypy --install-types --non-interactive guarddog

and the linter with

flake8 guarddog --count --select=E9,F63,F7,F82 --show-source --statistics --exclude tests/analyzer/sourcecode,tests/analyzer/metadata/resources,evaluator/data
flake8 guarddog --count --max-line-length=120 --statistics --exclude tests/analyzer/sourcecode,tests/analyzer/metadata/resources,evaluator/data --ignore=E203,W503

Maintainers

Authors:

• Ellen Wang
• Christophe Tafani-Dereeper
• Vladimir de Turckheim
• Sebastian Obregoso

Acknowledgments

Inspiration:

• Backstabber’s Knife Collection: A Review of Open Source Software Supply Chain Attacks
• What are Weak Links in the npm Supply Chain?
• A Survey on Common Threats in npm and PyPi Registries
• A Benchmark Comparison of Python Malware Detection Approaches
• Towards Measuring Supply Chain Attacks on Package Managers for Interpreted Languages