CrackQ is a management layer for Hashcat: it organizes password-audit jobs in a queue and exposes them through a REST API, a JavaScript web interface, and a Python client. Hashcat—not CrackQ—does the password-recovery work. Daniel Turner introduced CrackQ as an alpha project in December 2019 for authorized red-team and penetration-testing assessments.
What CrackQ does—and what Hashcat does
Hashcat is the password-recovery engine. CrackQ is the software around it for submitting and managing work. Turner described the project as a queueing system with a REST API and web interface; the repository describes a Python 3 API and JavaScript GUI for managing Hashcat jobs. The project also documents a remote Python client. Turner’s December 4, 2019 launch announcement and the CrackQ repository describe those roles.
Turner summarized the interface as: “It’s an intuitive interface for Hashcat served by a REST API and a JavaScript front-end web application for ease of use.” That makes CrackQ a way to coordinate Hashcat work, not a replacement cracking algorithm or engine.
| Component | Role |
|---|---|
| CrackQ | Queues and manages jobs; provides the documented API, web GUI, client, user controls, notifications, and password-analysis reporting. |
| Hashcat | Performs the password recovery using supported compute devices and backends. |
Who CrackQ was designed for
Turner positioned CrackQ for offensive security teams, particularly red teams and penetration testers working on authorized assessments. Its reporting features are intended to help teams examine password choices and patterns in an assessed password store, including Active Directory dump analysis. This is an assessment and reporting use case; CrackQ is not presented as a tool that independently prevents weak passwords or replaces a broader identity-security program.
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Features documented by the project
The repository lists management capabilities intended to make multi-job, multi-user work easier to administer:
- Queue operations, job controls, and queue or job statistics.
- Multi-user support with privilege separation, plus SQL, LDAP, or SAML2 authentication options listed by the project.
- Notifications and rate limiting.
- Password analysis and reporting, including Active Directory dump analysis.
- A remote Python client in addition to the web interface and REST API.
These are project-documented features, not independently verified current capabilities or a security review. Teams considering deployment should confirm which features work with the version they intend to run.
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Can CrackQ distribute jobs across machines?
Not according to Turner’s description of the initial release. In the December 2019 announcement, he wrote: “For example, it currently is not able to work as a distributed system, rather it’s a client-server setup.” That statement describes the release at that time; the sources cited here do not establish whether later code changed the topology. If distributing workers across multiple machines is a requirement, verify it against the project’s current code and documentation rather than assuming the original client-server design has changed.
Is CrackQ still maintained?
The available project pages do not establish a recent release date or a current support policy. The PyPI page for crackq-client records version 0.0.1, released September 18, 2019, but that client-package history alone does not establish the maintenance status of the CrackQ server. Check the repository for current commits, releases, issue activity, and compatibility notes before adopting it. The launch announcement’s alpha label applies to the initial release, not necessarily to every later state of the project.
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- FAST & CONVENIENT LOGIN: Plug in your YubiKey 5 NFC via USB and tap it, or tap it against your phone (NFC), to authenticate. No batteries, no internet connection, and no extra fees required
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Hardware and deployment considerations
CrackQ manages Hashcat jobs, so the compute hardware and driver setup matter to the work Hashcat performs. Hashcat describes support for CPUs and GPUs, multiple devices, and CUDA, HIP, Metal, and OpenCL compute backends in its project repository. CrackQ’s repository lists Docker and GPU-driver requirements, including OpenCL and NVIDIA or AMD drivers. These project requirements have not been independently tested for present-day compatibility.
No specific graphics-card model or expected recovery rate can be recommended from these sources. Workload, hash type, compute device, drivers, and configuration all affect performance. A GPU is a relevant hardware category, not a complete system recommendation.
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- FAST & CONVENIENT LOGIN: Plug in your Security Key NFC via USB-A and tap it, or tap it against your phone (NFC) to authenticate. No batteries, no internet connection, and no extra fees required.
- TRUSTED PASSKEY TECHNOLOGY: Uses the latest passkey standards (FIDO2/WebAuthn & FIDO U2F) but does not support One-Time Passwords. For complex needs, check out the YubiKey 5 Series.
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Turner’s 2019 announcement also discussed Hashcat Brain, which can prevent repeated guesses across runs, and said CrackQ activates it when expected to be efficient. He put Brain’s bottleneck at “around 500kH/s.” This is a release-era figure attributed to Turner, not an independently verified current benchmark or a general speed estimate for CrackQ.
The announcement described Docker deployment and cloud integration, including an EC2 example, in its 2019 release context. That does not establish current cloud-provider compatibility or deployment guidance. Validate the actual deployment path, drivers, authentication, and access controls for the version you plan to operate.
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What to verify before adopting CrackQ
For a security assessment workflow, confirm the operational details that determine whether a job manager fits your environment:
Quick Recap
- Whether the current server code is maintained and which Hashcat versions it supports.
- Whether its client-server topology meets your needs, especially if you require distributed workers.
- Which authentication, privilege-separation, and rate-limiting options are implemented and configured in your target version.
- Whether its Docker, GPU-driver, and compute-backend requirements match your operating system and hardware.
- How queue operations, reporting, and notifications fit your assessment process.
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