13o-bbr-bbq/machine_learning_security resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2088 stars · 676 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Python source code and educational materials combining machine learning with cybersecurity, including a training course for security engineers and tools like Deep Exploit (automated penetration testing), SAIVS (AI vulnerability scanner), and adversarial example generators. It serves as both a learning resource and a set of experimental security tools presented at Black Hat and DEF CON.

Use cases

• learn machine learning fundamentals as a security engineer
• run automated penetration testing with machine learning
• generate adversarial examples against CNNs
• analyze packet capture data with k-means clustering
• scan web applications for vulnerabilities using AI
• generate injection codes for web app assessment
• recommend optimal injection codes for vulnerability detection

When to choose

• you want to study the intersection of ML and offensive security
• you need reference implementations of ML-driven pentest tools
• you're building AI-based vulnerability scanners or exploit generators
• you want a structured ML course tailored for security engineers

When to avoid

• you need a production-ready, maintained security product with a license
• you want defensive ML security tooling rather than offensive testing
• you need polished software with documentation and support
• you're not comfortable with experimental research-grade code

Facets

learning-resource · maturity maintenance

machine-learning penetration-testing vulnerability-scanning security deep-learning security penetration-testing machine-learning artificial-intelligence developer-tools python cli adversarial-examples pentesting adversarial-machine-learning vulnerability-scanner security-course exploit-generation linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="13o-bbr-bbq/machine_learning_security")