performing-supply-chain-attack-simulation

Detect typosquatting and dependency confusion by querying the PyPI JSON API to calculate Levenshtein distance against top packages and testing internal registry configurations. This generates a structured JSON report detailing detected vectors and risk scores for your target environment.

A software supply chain attack simulation identifies and evaluates risks by detecting typosquatting, dependency confusion, and hash mismatches in Python ecosystems. It helps security teams validate controls and reduce risk by producing a structured JSON report of detected vectors and package metadata.

Yes, you can use pip-audit for optional vulnerability scanning and metadata anomaly checks during supply chain testing. It integrates with hash verification via SHA-256 checks to enhance the structured JSON risk report output for public PyPI packages.

Yes, this supply chain simulation applies detection across common scenarios including internal registries and private dependencies, as well as public PyPI packages. It evaluates risk scores and package metadata to validate security controls for these specific environments.

Hash verification detects SHA-256 mismatches by querying the PyPI JSON API, but limitations arise if internal registries mirror outdated package metadata or if optional pip-audit scanning encounters unsupported package formats. Always validate controls across all target scenarios to reduce risk effectively.