Flag misspelled, brandjacked, and typosquatted package names across npm, PyPI, and crates.io before installation, using edit-distance, keyboard-proximity, and known-target corpus matching with typomania, Microsoft OSSGadget's oss-find-squats, and pypi-scan. Use before adding a new dependency, as a CI/CD gate on pull requests, when reviewing an AI-generated ("slopsquatting") dependency list, or when auditing a lockfile diff for a swapped package.
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Flag misspelled, brandjacked, and typosquatted package names across npm, PyPI, and crates.io before installation, using edit-distance, keyboard-proximity, and known-target corpus matching with typomania, Microsoft OSSGadget's oss-find-squats, and pypi-scan. Use before adding a new dependency, as a CI/CD gate on pull requests, when reviewing an AI-generated ("slopsquatting") dependency list, or when auditing a lockfile diff for a swapped package.
Authorized Use Only: This skill is for defensive software-supply-chain security, package screening, and authorized research. Use the corpus-matching and registry-query techniques here only against registries you are permitted to query at scale and packages you intend to evaluate for your own organization. Mass automated registry scraping may violate registry terms of service.
Overview
Typosquatting is a software-supply-chain attack (MITRE ATT&CK T1195.002 — Supply Chain Compromise: Compromise Software Supply Chain) in which an adversary publishes a malicious package whose name is a near-miss of a popular legitimate package — reqeusts for requests, python-sqlite for sqlite3, crossenv for cross-env. A developer who fat-fingers the name, copies a name from a poisoned tutorial, or trusts an AI-generated dependency list (the "slopsquatting" variant, where models hallucinate package names attackers then register) installs the squat instead. Because most ecosystems execute install-time scripts (postinstall in npm, setup.py/build hooks in PyPI), the payload runs immediately with the developer's privileges.
This skill covers proactive, pre-installation detection: screening a candidate package name against a corpus of popular/known-good names using the same name-mutation primitives attackers use, then triaging high-risk matches. The canonical open-source detector is typomania (Rust Foundation), a Rust port of the academic typogard tool ("Defending Against Package Typosquatting", University of Kansas); typomania powers crates.io's live typosquatting checks. Cross-ecosystem coverage comes from Microsoft OSSGadget's oss-find-squats and, for PyPI, IQTLabs pypi-scan. The ecosyste-ms typosquatting-dataset provides a curated ground-truth corpus of known squats mapped to their legitimate targets.
Sources: Rust Foundation typomania (https://github.com/rustfoundation/typomania), Microsoft OSSGadget, ecosyste-ms typosquatting-dataset, and OWASP CI/CD / SLSA supply-chain guidance.
When to Use
Before adding a new dependency to package.json, requirements.txt, pyproject.toml, or Cargo.toml
As a CI/CD gate that screens every newly introduced dependency name in a pull request
When triaging an AI-generated or tutorial-sourced dependency list ("slopsquatting" review)
During security review of a lockfile diff to catch a swapped or newly-pinned squat
When building a registry-side or proxy-side guardrail that blocks installs of suspected squats
Prerequisites
Rust toolchain (cargo) to build/use typomania, or a prebuilt OSSGadget release
Python 3.8+ for pypi-scan and the helper script in this skill
Network access to the target registry's public API (npmjs.org, pypi.org, crates.io)
A corpus of "popular" package names for the ecosystem (download counts or a top-N list)
Install the tooling:
# typomania (library + example harness) — Rust Foundation
git clone https://github.com/rustfoundation/typomania
cd typomania
cargo build --release
cargo run --example registry # demonstrates the Harness against a fake registry# OSSGadget (Microsoft) — cross-ecosystem squat finder# Download a release binary, then:
oss-find-squats pkg:npm/requests # purl syntax
oss-find-squats pkg:pypi/reqeusts
# pypi-scan (IQTLabs) — PyPI typosquat enumerator
git clone https://github.com/IQTLabs/pypi-scan
cd pypi-scan
pip install -r requirements.txt
# ecosyste-ms ground-truth dataset of known squats
git clone https://github.com/ecosyste-ms/typosquatting-dataset
Objectives
Generate the candidate squat set for a given legitimate name using the standard mutation primitives
Screen a candidate package name against a popular-name corpus and flag near-misses
Enrich each suspected squat with registry metadata (age, downloads, maintainer, install scripts)
Score and triage findings to suppress false positives (legitimate forks, scoped packages)
Wire the check into CI/CD as a blocking gate on new dependencies
A typosquatted dependency is the delivery vehicle for T1195.002: the attacker compromises the victim's supply chain not by breaching a real package but by getting a malicious look-alike installed in its place. Related downstream behavior frequently includes T1059 (Command and Scripting Interpreter) via install hooks and T1041/T1567 (Exfiltration) of tokens and environment variables.
Workflow
Step 1: Build the popular-name corpus
The detector needs a reference set of legitimate names to compare against. Pull top packages by download count for the ecosystem.
# PyPI: top packages dataset (Hugo van Kemenade's top-pypi-packages)
curl -s https://hugovk.github.io/top-pypi-packages/top-pypi-packages.min.json \
-o top-pypi-packages.json
# npm: query the registry's most-depended-upon search
curl -s 'https://registry.npmjs.org/-/v1/search?text=not:unstable&popularity=1.0&size=250' \
-o npm-top.json
# crates.io: top crates by downloads
curl -s 'https://crates.io/api/v1/crates?sort=downloads&per_page=100' \
-H 'User-Agent: typosquat-screen (security@example.com)' -o crates-top.json
Step 2: Generate candidate squats with the standard mutation primitives
typomania/typogard apply a fixed set of name transformations that mirror real attacker behavior. Reproduce them to understand what a screen must catch:
Run typomania's harness, which implements these as reusable primitives behind the Corpus and Harness traits:
# In the typomania checkout: feed your popular corpus, then check a name.# The Harness::check method (parallelized via rayon) compares the candidate# against every corpus entry using the squatting primitives.
cargo run --example registry -- --corpus top-pypi-packages.json --name reqeusts
Step 3: Screen with OSSGadget oss-find-squats
OSSGadget queries the live registry, generates mutations of the supplied package, and reports which mutated names actually exist as published packages.
# Find names that squat on a legitimate package, checking which exist in the registry
oss-find-squats pkg:npm/lodash
oss-find-squats pkg:pypi/requests
# Reverse direction: given a SUSPECT name, find the legitimate package it mimics
oss-find-squats --quiet pkg:npm/loadsh
Step 4: Enumerate PyPI squats with pypi-scan
cd pypi-scan
# Find candidate typosquats of a specific package
python pypi_scan.py -p requests
# Scan the top-N most-downloaded PyPI packages for existing squats
python pypi_scan.py -n 50
Step 5: Enrich suspected squats with registry metadata
A near-miss name is only suspicious if it is also young, low-download, or ships install scripts. Pull metadata to triage:
Combine signals into a risk score. High risk = small edit distance to a popular name AND (package age < 90 days OR downloads < 1000 OR presence of postinstall/preinstall/setup-time network calls). Cross-check against the ecosyste-ms known-squats dataset:
# Is this name a documented squat?
grep -i 'loadsh' typosquatting-dataset/data/*.csv
Confirm malicious intent by inspecting (in a sandbox/VM only) the install scripts and source tarball — never npm install or pip install a suspect on your workstation.
Step 7: Enforce in CI/CD
Add a blocking gate that screens every new dependency name introduced by a PR:
