A curated collection of tools, research, and resources for password cracking and security auditing.
Awesome Password Cracking is a curated GitHub repository that compiles tools, research papers, wordlists, and other resources related to password cracking and password security. It serves as a reference for security professionals and researchers involved in cryptanalysis, penetration testing, and auditing authentication systems. The list covers everything from popular cracking software like Hashcat and John the Ripper to academic studies on password strength and machine learning attacks.
Security researchers, penetration testers, red teamers, and cryptanalysis enthusiasts who need a centralized resource for password auditing tools and techniques. It's also valuable for academics studying authentication security and password psychology.
It saves significant time by aggregating a wide array of password security resources—from practical tools to cutting-edge research—in one meticulously organized list. Unlike scattered blog posts or forums, it provides a community-vetted, structured directory that is continuously updated.
A curated list of awesome tools, research, papers and other projects related to password cracking and password security.
Curates hundreds of tools, wordlists, and research papers in organized sections like 'Hashcat', 'Wordlists', and 'Research', saving time for professionals.
Includes recent academic papers from 2023-2026 on topics like machine learning attacks and password strength modeling, sourced from conferences like USENIX Security.
Features a 'Notable People' section with profiles of key contributors, ensuring the list stays relevant and connected to industry trends.
Lists niche utilities like hash converters (e.g., 7z2hashcat) and distributed cracking setups (e.g., Hashtopolis), which are hard to find elsewhere.
The list is purely a directory without tutorials or examples, requiring users to have prior knowledge to implement tools effectively.
As a community-maintained project, some links or tools might become stale or broken over time, with no automated update guarantees.
The sheer volume and technical depth—like advanced Hashcat rules or machine learning papers—can be intimidating without curated learning paths.
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