performing-supply-chain-attack-simulation

name	performing-supply-chain-attack-simulation
description	Simulate and detect software supply chain attacks including typosquatting detection via Levenshtein distance, dependency confusion testing against private registries, package hash verification with pip, and known vulnerability scanning with pip-audit.
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

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.

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

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.