GuardDog is a CLI tool that identifies malicious PyPI and npm packages, Go modules, GitHub actions, or VSCode extensions. It runs static analysis on package source code (through YARA rules) and analyzes package metadata to detect supply chain attacks.
What makes GuardDog different: Instead of just listing suspicious patterns, GuardDog correlates findings to identify actual risks based on attack chains. A package needs both the capability to perform an action (e.g., network access) and a threat indicator (e.g., suspicious domain) in the same file to be flagged as high risk.
It downloads and scans code from:
- NPM: Packages hosted in npmjs.org
- PyPI: Source files (tar.gz) packages hosted in PyPI.org
- Go: GoLang source files of repositories hosted in GitHub.com
- RubyGems: Gem packages hosted in rubygems.org
- GitHub Actions: Javascript source files of repositories hosted in GitHub.com
- VSCode Extensions: Extensions (.vsix) packages hosted in marketplace.visualstudio.com
GuardDog uses a risk-based detection model that correlates code capabilities with threat indicators:
- Detection: Rules identify either capabilities (what code can do) or threats (suspicious indicators)
- Correlation: Capabilities and threats found in the same file form risks (cross-file matches also form risks, with downgraded severity)
- Scoring: Risks are scored (0-10) based on attack chain completeness and sophistication
- Reporting: Packages receive a severity rating (low/medium/high) with detailed risk breakdown
Traditional SAST tools flag every suspicious pattern independently, leading to alert fatigue. GuardDog understands that:
- Capability alone isn't malicious (network libraries should make HTTP requests)
- Threat indicators alone might be false positives (test fixtures, documentation)
- Capability + Threat together indicates actual risk (code that can and will do something malicious)
Packages receive a score from 0-10 based on four factors:
| Factor | Weight | Description |
|---|---|---|
| Severity | 30% | Highest severity finding (low/medium/high) |
| Attack Chain | 20% | Presence of complete attack stages (early → mid/late) |
| Specificity | 30% | How specific patterns are to malware vs legitimate code |
| Sophistication | 20% | Technique advancement level |
Score Labels:
- 0: No risks detected
- 0.1-3: Low risk (single-stage threats, low specificity)
- 3.1-7.5: Medium risk (partial attack chain, metadata indicators, or single-stage code findings)
- 7.6-10: High risk (multi-stage attack chain with source code evidence — near-certainty of compromise)
Attack Chain Stages (based on MITRE ATT&CK):
- Early: Initial access, execution capabilities
- Mid: Persistence, defense evasion, credential access
- Late: Command & control, exfiltration, impact
Check out the new Datadog Agent integration and Cloud SIEM content pack for GuardDog.
The easiest way to run GuardDog is to use uvx:
uvx guarddog pypi scan requestsTo install it locally:
uv tool install guarddog
# or
pip install guarddogOr 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.
# 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, go, rubygems
guarddog npm scan express
guarddog go scan github.com/DataDog/dd-trace-go
guarddog go verify /tmp/repo/go.mod
# Scan RubyGems packages
guarddog rubygems scan rails
guarddog rubygems verify /tmp/repo/Gemfile.lock
# Additionally can support scanning GitHub actions that are implemented in JavaScript
guarddog github_action scan DataDog/synthetics-ci-github-action
guarddog github_action verify /tmp/repo/.github/workflows/main.yml
# Scan VSCode extensions from the marketplace
guarddog extension scan ms-python.python
# Scan a specific version of a VSCode extension
guarddog extension scan ms-python.python --version 2023.20.0
# Scan a local VSCode extension directory or VSIX archive
guarddog extension scan /tmp/my-extension/
# Run in debug mode
guarddog --log-level debug npm scan expressWhen scanning packages, GuardDog runs source code analysis inside a kernel-level sandbox (Linux via Landlock, macOS via Seatbelt, using nono). The sandbox blocks all network access and restricts filesystem operations to only the paths needed for analysis. This protects against malicious packages that attempt to execute code during archive extraction or scanning.
By default, the sandbox is used if available, with a warning if it's not. You can also force it on (hard-fail if unavailable) or off:
# Default: auto-detect, warn if unavailable
guarddog pypi scan requests
# Force sandbox on (exit with error if unavailable)
guarddog pypi scan requests --sandbox
# Explicitly disable the sandbox (not recommended)
guarddog pypi scan requests --no-sandboxFor remote packages, three phases run with different privilege levels:
- Download and metadata analysis run without sandbox (need network access)
- Archive extraction runs in a sandboxed subprocess (network blocked, filesystem restricted)
- Source code analysis (YARA) runs in the main process after a sandbox is applied (network blocked, filesystem restricted to extracted files)
The sandbox was introduced to mitigate path traversal and code execution vulnerabilities during archive extraction (CVE-2022-23530, CVE-2022-23531, CVE-2026-22870, CVE-2026-22871).
GuardDog uses two types of detection rules, both participating in the risk-based scoring engine:
- Source code rules (YARA): Static analysis of package source code detecting capabilities and threats
- Metadata rules (Python detectors): Analysis of package registry metadata detecting supply chain attack indicators
For the full list of rules per ecosystem, see RULES.md.
For guidance on writing new rules, see WRITING_RULES.md.
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
- uses: astral-sh/setup-uv@v7
- run: uvx 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- Ensure poetry has an env with
python >=3.10poetry env use 3.10.0 - Install dependencies
poetry install - Run guarddog
poetry run guarddogorpoetry shellthen runguarddog
Running all unit tests: make test
Running unit tests against package metadata heuristics: make test-metadata-rules (tests are here).
You can run GuardDog on legitimate and malicious packages to determine false positives and false negatives. See ./tests/samples
Run the type checker with
mypy --install-types --non-interactive guarddogand 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,W503GuardDog's behavior can be customized using environment variables:
| Environment Variable | Description | Default Value |
|---|---|---|
GUARDDOG_PARALLELISM |
Number of threads to use for parallel processing | Number of CPUs available |
GUARDDOG_VERIFY_EXHAUSTIVE_DEPENDENCIES |
Analyze all possible versions of dependencies (true/false) |
false |
GUARDDOG_TOP_PACKAGES_CACHE_LOCATION |
Location of the top packages cache directory | guarddog/analyzer/metadata/resources |
GUARDDOG_YARA_EXT_EXCLUDE |
Comma-separated list of file extensions to exclude from YARA scanning | ini,md,rst,txt,lock,json,yaml,yml,toml,xml,html,csv,sql,pdf,doc,docx,ppt,pptx,xls,xlsx,odt,changelog,readme,makefile,dockerfile,pkg-info,d.ts |
GuardDog implements multiple security checks when extracting package archives to protect against compression bombs and file descriptor exhaustion attacks:
| Environment Variable | Description | Default Value |
|---|---|---|
GUARDDOG_MAX_UNCOMPRESSED_SIZE |
Maximum allowed uncompressed size in bytes (prevents disk space exhaustion) | 2147483648 (2 GB) |
GUARDDOG_MAX_COMPRESSION_RATIO |
Maximum allowed compression ratio (detects suspicious compression patterns) | 100 (100:1) |
GUARDDOG_MAX_FILE_COUNT |
Maximum number of files allowed in an archive (prevents file descriptor/inode exhaustion) | 100000 |
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

