What it does. Simulates and detects software supply chain attacks: typosquatting detection via Levenshtein distance against popular PyPI package names, dependency confusion testing against private registries, SHA-256 package hash verification, and known-CVE scanning with pip-audit. Use when auditing a project's dependencies for malicious or confused packages, or when assessing package-registry supply-chain risk. Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).

Install

• npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-supply-chain-attack-simulation, or copy the skill folder into ~/.claude/skills/performing-supply-chain-attack-simulation/.
• Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/performing-supply-chain-attack-simulation/SKILL.md

SKILL.md (verbatim)

name: performing-supply-chain-attack-simulation
description: >-
  Simulates and detects software supply chain attacks: typosquatting
  detection via Levenshtein distance against popular PyPI package names,
  dependency confusion testing against private registries, SHA-256 package
  hash verification, and known-CVE scanning with pip-audit. Use when auditing
  a project's dependencies for malicious or confused packages, or when
  assessing package-registry supply-chain risk.
domain: cybersecurity
subdomain: application-security
tags:
- supply-chain
- typosquatting
- dependency-confusion
- package-verification
- pip-audit
- PyPI
- software-composition-analysis
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- PR.PS-01
- PR.PS-04
- ID.RA-01
- PR.DS-10
mitre_attack:
- T1078
- T1190
- T1059
- T1195
- T1554

Performing Supply Chain Attack Simulation

Overview

Software supply chain attacks exploit trust in package registries through typosquatting (registering names similar to popular packages), dependency confusion (publishing higher-version public packages matching private names), and compromised package distribution. This skill detects these attack vectors by computing Levenshtein distance between package names and popular PyPI packages, verifying package integrity via SHA-256 hash comparison, scanning for known CVEs with pip-audit, and testing dependency resolution order for confusion vulnerabilities.

When to Use

• When conducting security assessments that involve performing supply chain attack simulation
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Prerequisites

• Python 3.9+ with pip-audit, Levenshtein, requests
• Access to PyPI JSON API (https://pypi.org/pypi/{package}/json)
• Network access for package metadata retrieval

Legal Notice: This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.

Key Detection Areas

1. Typosquatting — compare package names against top PyPI packages using edit distance thresholds
2. Dependency confusion — check if internal package names exist on public PyPI with higher version numbers
3. Hash verification — download packages and verify SHA-256 digests match published hashes
4. Vulnerability scanning — audit installed packages against OSV and PyPA advisory databases
5. Metadata anomalies — flag packages with suspicious author emails, missing homepages, or very recent first upload dates

Output

JSON report with risk scores per package, detected attack vectors, hash verification results, and CVE findings.

Other files in this skill

• LICENSE
• references/api-reference.md
• scripts/agent.py

references/api-reference.md (verbatim)

Supply Chain Attack Simulation Reference

Tool Installation

pip install pip-audit python-Levenshtein requests

PyPI JSON API

# Get package metadata
curl https://pypi.org/pypi/{package_name}/json

# Get specific version
curl https://pypi.org/pypi/{package_name}/{version}/json

Response Structure

{
  "info": {
    "name": "requests",
    "version": "2.31.0",
    "author": "Kenneth Reitz",
    "author_email": "me@kennethreitz.org",
    "home_page": "https://requests.readthedocs.io",
    "summary": "Python HTTP for Humans."
  },
  "urls": [
    {
      "filename": "requests-2.31.0.tar.gz",
      "packagetype": "sdist",
      "digests": {
        "sha256": "942c5a758f98d790eaed1a29cb6eefc7f0edf3fcb0fce8aea3fbd5951d bfcfeb"
      }
    }
  ]
}

pip-audit CLI

# Audit current environment
pip-audit

# JSON output
pip-audit --format json

# Audit requirements file
pip-audit -r requirements.txt

# Hash-checking mode (pinned deps only)
pip-audit --require-hashes -r requirements.txt

# Fix vulnerabilities automatically
pip-audit --fix

pip Hash Verification

# Download with hash verification
pip download --no-deps --require-hashes -r requirements.txt

# requirements.txt with hashes
requests==2.31.0 \
    --hash=sha256:942c5a758f98d790eaed1a29cb6eefc7f0edf3fcb0fce8aea3fbd5951dbfcfeb

pypi-scan Typosquatting Detection

# Install
pip install pypi-scan

# Scan for typosquatting of a package
pypi-scan --package requests

# Scan with custom edit distance
pypi-scan --package numpy --edit-distance 2

Levenshtein Distance Examples

Dependency Confusion Test Structure

[
  {"name": "my-internal-lib", "version": "1.2.0"},
  {"name": "company-utils", "version": "0.5.3"},
  {"name": "private-auth-sdk", "version": "2.1.0"}
]

MITRE ATT&CK Mapping

Metadata Anomaly Indicators

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