| name | detecting-typosquatting-packages-in-npm-pypi |
| description | Detects typosquatting attacks in npm and PyPI package registries by analyzing package name similarity using Levenshtein distance and other string metrics, examining publish date heuristics to identify recently created packages mimicking established ones, and flagging download count anomalies where suspicious packages have disproportionately low usage compared to their legitimate targets. The analyst queries the PyPI JSON API and npm registry API to gather package metadata for automated comparison. Activates for requests involving package typosquatting detection, dependency confusion analysis, malicious package identification, or software supply chain threat hunting in package registries.
|
| domain | cybersecurity |
| subdomain | supply-chain-security |
| tags | ["typosquatting","npm","pypi","supply-chain","package-security","Levenshtein","dependency-confusion","malicious-packages"] |
| version | 1.0.0 |
| author | mukul975 |
| license | Apache-2.0 |
| nist_csf | ["GV.SC-01","GV.SC-03","GV.SC-06","GV.SC-07"] |
| mitre_attack | ["T1195.001","T1195.002","T1608.001","T1554"] |
Detecting Typosquatting Packages in npm and PyPI
When to Use
- Auditing project dependencies to identify packages whose names are suspiciously similar to popular libraries
- Proactively scanning package registries for newly published packages that may be typosquats of your organization's packages
- Investigating a suspected supply chain compromise where a developer installed a misspelled package name
- Building automated monitoring that alerts when new packages appear with names close to critical dependencies
- Assessing the risk profile of unfamiliar packages before adding them to a project's dependency tree
Do not use as the sole determination of malicious intent; name similarity alone does not prove a package is malicious. Do not use for bulk automated takedown requests without manual review of flagged packages. Do not use against private registries without authorization.
Prerequisites
- Python 3.9+ with
requests and python-Levenshtein (or rapidfuzz) packages installed
- Network access to
https://pypi.org/pypi/<package>/json (PyPI JSON API) and https://registry.npmjs.org/<package> (npm registry API)
- A list of popular or critical packages to monitor (e.g., top 1000 PyPI packages, organization's dependency list)
- Understanding of common typosquatting patterns: character omission, transposition, insertion, substitution, and hyphen/underscore manipulation
Workflow
Step 1: Build the Target Package Watchlist
Establish the set of legitimate packages to monitor for typosquats:
- Extract project dependencies: Parse
requirements.txt, Pipfile.lock, package.json, or package-lock.json to extract all direct and transitive dependency names
- Include popular packages: Supplement with high-value targets from the top 1000 PyPI downloads (available from
https://hugovk.github.io/top-pypi-packages/) or top npm packages by download count
- Add organization packages: Include any packages published by your organization that attackers might target with typosquats to intercept internal installations
- Normalize names: PyPI treats hyphens, underscores, and periods as equivalent (PEP 503 normalization:
re.sub(r"[-_.]+", "-", name).lower()). npm package names are case-sensitive but scoped packages use @scope/name format. Normalize before comparison.
