A Python REST API and web GUI for managing Hashcat password cracking jobs in a queuing system.
CrackQ is a Python-based REST API and web interface that acts as a job queuing system for Hashcat. It allows users to manage multiple password-cracking jobs, automate workflows, and analyze results through a centralized platform. The tool integrates directly with Hashcat's library to provide real-time control and supports features like multi-user authentication, intelligent job scheduling, and detailed reporting.
Security professionals, penetration testers, and red teamers who need to orchestrate and manage large-scale password cracking campaigns using Hashcat.
Developers choose CrackQ because it transforms Hashcat from a command-line tool into a scalable, multi-user system with job queuing, automation, and reporting capabilities, significantly improving workflow efficiency for security testing.
CrackQ: A Python Hashcat cracking queue system
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Uses libhashcat directly via the Hashcat API, ensuring support for the latest algorithms and avoiding shell command overhead, as stated in the README.
Implements intelligent queuing with speed checks and automated Brain activation, optimizing large-scale cracking campaigns based on job efficiency.
Features role-based access control with privilege separation for jobs, enabling secure team management and collaboration on cracking tasks.
Includes analysis for cracked passwords and Active Directory dumps with detailed statistics and visualizations, enhancing post-cracking insights.
Requires a server with ample RAM/CPU and specific GPU drivers (OpenCL, Nvidia, AMD), making it resource-intensive and costly for small setups.
Relies entirely on Docker and docker-compose for installation, which can be a barrier in environments without container support or where Docker is restricted.
Limited to Hashcat integration, so it cannot manage other password cracking tools or alternative recovery methods, reducing versatility for mixed workflows.