hate_crack automates advanced password cracking methodologies using Hashcat, targeting security professionals and researchers. It streamlines complex cracking workflows by integrating diverse data sources and advanced attack techniques, significantly enhancing efficiency.

How It Works

The tool orchestrates Hashcat by integrating with Hashview for centralized hash management and distributed cracking. It leverages external services like Weakpass and Hashmob for wordlists and rules, and incorporates an LLM Attack using Ollama for AI-generated password candidates. Its modular design and extensive pre-defined attack methodologies offer a comprehensive solution for password auditing.

Quick Start & Requirements

Installation involves first setting up Hashcat. Then, clone the repository with submodules (git clone --recurse-submodules) and run make install. This command automatically installs external dependencies (p7zip, transmission-cli) and the Python tool via uv. Alternatively, dependencies can be installed manually, followed by uv tool install .. Key prerequisites include Hashcat, Python, p7zip, and transmission-cli. Official documentation and demo links are available via the project's GitHub repository.

Highlighted Details

• Extensive Attack Library: Features over 15 distinct cracking methodologies, including Quick Crack, Pure_Hate, Combinator, Hybrid, PRINCE, and an AI-driven LLM Attack.
• Hashview Integration: Provides robust capabilities for managing hashes, creating cracking jobs, and facilitating distributed cracking through the Hashview platform.
• LLM-Powered Candidates: The LLM Attack option utilizes a local Ollama instance to generate intelligent password candidates based on contextual information.
• Data Source Aggregation: Seamlessly downloads wordlists and rules from Weakpass (via torrents) and Hashmob.net.
• Automated Synchronization: Automatically merges downloaded cracked hashes with local uncracked files, ensuring consistent results.

Maintenance & Community

Developed by the TrustedSec team. The provided README does not detail specific community channels (e.g., Discord, Slack), roadmap links, or notable contributors beyond the team itself.

Licensing & Compatibility

The license type is not explicitly stated in the provided README content, which is a critical omission for due diligence. Compatibility is tested across Python versions 3.9 through 3.14, with installation instructions suggesting Linux/macOS environments.

Limitations & Caveats

Requires manual installation of Hashcat and other system dependencies (p7zip, transmission-cli). The LLM Attack necessitates a running Ollama instance. The absence of a clearly defined license presents a significant adoption blocker.