Individual skill
performing-supply-chain-attack-simulation
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.
skills/performing-supply-chain-attack-simulation/SKILL.md
Source commit 54a798831d2266a3ca61ce68a7acb80b81160d57
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
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- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 35,862
- Repository last updated
- Aug 31, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-agents Repository topic
- claude-code Repository topic
- cybersecurity Repository topic
- incident-response Repository topic
- mitre-attack Repository topic
- penetration-testing Repository topic
- red-team Repository topic
- security Repository topic
- cloud-security Repository topic
- malware-analysis Repository topic
- supply-chain Declared skill tag
- typosquatting Declared skill tag
- dependency-confusion Declared skill tag
- package-verification Declared skill tag
- pip-audit Declared skill tag
- pypi Declared skill tag
- software-composition-analysis Declared skill tag
- engineering Derived category
- operations Derived category
- security Derived category
- testing Derived category
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LICENSEasset · 11,255 bytesSKILL.mdcore · 2,872 bytesreferences/api-reference.mddoc · 2,901 bytesscripts/agent.pyruns · 10,308 bytes
Read excerpt
# 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