___ ___         __             _________                       __
 /   |   \_____ _/  |_  ____     \_   ___ \____________    ____ |  | __
/    ~    \__  \\   __\/ __ \    /    \  \/\_  __ \__  \ _/ ___\|  |/ /
\    Y    // __ \|  | \  ___/    \     \____|  | \// __ \\  \___|    <
 \___|_  /(____  /__|  \___  >____\______  /|__|  (____  /\___  >__|_ \
       \/      \/          \/_____/      \/            \/     \/     \/

macOS (Homebrew):```bash
brew install hashcat

Si clonaste sin submódulos, inicialízalos:```bash
git submodule update --init --recursive

`.env.example` está incluido en el repositorio y se distribuye con todas las claves de credenciales vacías. `.env` **nunca** debe incluirse en el repositorio: está en `.gitignore`, junto con sus variantes habituales de respaldo, y `hate_crack` siempre lo crea con modo `0600` (solo lectura/escritura para el propietario). `.env.example` se genera a partir del esquema; regenéralo después de cambiar `hate_crack/config_schema.py` con `uv run python -m hate_crack.config_writer`.

### 3. Instalar dependencias y hate_crack

La forma más sencilla es ejecutar `make` (o `make install`), que detecta automáticamente tu sistema operativo e instala:
- Dependencias externas (p7zip, transmission-daemon / transmission-remote)
- Compila submódulos (hashcat-utils, princeprocessor, pcfg_cracker y, opcionalmente, omen)
- Dependencias de Python mediante uv y un shim de CLI en `~/.local/bin/hate_crack````bash
make

**O instala las dependencias manualmente:**

### Dependencias Externas
Estas son necesarias para ciertos flujos de descarga/extracción:

- `7z`/`7za` (p7zip) — se usa para extraer archivos `.7z`.
- `transmission-daemon` / `transmission-remote` — se usa para descargar torrents de Weakpass.

Comandos de instalación manual:

Ubuntu/Kali:```bash
sudo apt-get update
sudo apt-get install -y p7zip-full transmission-daemon

Luego instala las dependencias de Python y el shim de la CLI:```bash
uv sync
mkdir -p ~/.local/bin
printf '#!/usr/bin/env bash\nset -euo pipefail\nexec uv run --directory %s python -m hate_crack "$@"\n' "$(pwd)" > ~/.local/bin/hate_crack
chmod +x ~/.local/bin/hate_crack

Alternativamente, ejecuta mediante `uv`:```bash
uv run hate_crack.py <hash_file> <hash_type>

El comando `make install` crea un shim de bash en `~/.local/bin/hate_crack` que se ejecuta desde el directorio del repositorio, por lo que la configuración y los assets siempre se encuentran independientemente de tu directorio de trabajo actual.

La configuración también se busca en:
- La raíz del repositorio y el directorio del paquete
- `~/.hate_crack`

**Nota:** El `hcatPath` en `config.json` es únicamente para la ubicación del binario de hashcat (opcional si hashcat está en el PATH). Los assets de Hate_crack (hashcat-utils, princeprocessor, pcfg_cracker, omen) se cargan desde el directorio del repositorio y se incluyen automáticamente mediante `make install`.

### Ejecutar como script
El script utiliza un shebang de `uv`. Hazlo ejecutable y ejecútalo:```bash
chmod +x hate_crack.py
./hate_crack.py

### Uso no interactivo / mediante scripts

Para la automatización puedes lanzar un único ataque directamente, omitiendo el menú. El nombre del ataque es el primer argumento, seguido del archivo de hash y el tipo de hash de hashcat. Las indicaciones de preprocesamiento (filtrado de cuentas de equipo, fuerza bruta LM primero, deduplicación de cuentas duplicadas) aceptan automáticamente sus valores predeterminados en este modo. El proceso termina con `0` en caso de éxito y distinto de cero en caso de error (archivo de hash faltante, tipo de hash no numérico, lista de palabras faltante o nombre de archivo de regla desconocido).```bash
# Quick crack: one wordlist + optional rule(s) from the rules directory
hate_crack quick hashes.txt 1000 --wordlist rockyou.txt --rules best64.rule

# Chain two rules in a single run
hate_crack quick hashes.txt 1000 --wordlist rockyou.txt --rules best64.rule+d3ad0ne.rule

# Run two rules as two separate passes
hate_crack quick hashes.txt 1000 --wordlist rockyou.txt --rules best64.rule d3ad0ne.rule

# Canned dictionary methodology (uses your configured wordlists)
hate_crack dict hashes.txt 1000

# Brute force lengths 1-8
hate_crack brute hashes.txt 1000 --min 1 --max 8

# Top-mask attack targeting ~4 hours
hate_crack topmask hashes.txt 1000 --target-time 4

Esto afecta a clones creados antes de julio de 2026. El historial publicado se reescribió
entonces para eliminar algunos archivos que nunca debieron haberse commiteado, lo que dio
a cada commit una nueva ID; por lo tanto, las etiquetas de un clon antiguo apuntan a objetos que
este repositorio ya no contiene, y git se niega a mover una etiqueta que ya tiene.
No hay nada malo en tu checkout y ningún dato de cracking está en riesgo.

Recupéralo con un restablecimiento único. Esto descarta commits locales y ediciones en el
checkout, así que si has personalizado algo rastreado por git (a diferencia de
`config.json`, que no está rastreado), hazle commit a una rama primero:```bash
cd /path/to/hate_crack
git fetch --tags --force origin
git checkout -B main origin/main
make install

Esto significa que los activos de hate_crack no se incluyeron en el paquete instalado.

**Entendiendo las rutas:**
- `hcatPath` en config.json → apunta a la **ubicación del binario de hashcat** (opcional, puede estar en PATH)
- `hashcat-utils/` y `princeprocessor/` → se incluyen en el paquete mediante `make install`

**Solución:**
Reinstale usando el Makefile, que compila los submódulos e instala la herramienta:```bash
cd /path/to/hate_crack  # the repository checkout
make install

**Carga de configuración:**
- Precedencia para cada clave: `os.environ` > el archivo propio de esa clave (`.env` o `config.json`) > valor predeterminado integrado
- Las claves faltantes recurren a los valores predeterminados integrados; `config.json.example` documenta cada clave de `config.json`
- Ambos archivos se buscan, independientemente entre sí, en este orden: **raíz del repositorio**, luego **directorio del paquete instalado**, luego **`~/.hate_crack`**. La primera coincidencia gana; es normal que los dos archivos provengan de directorios distintos.
- En la primera ejecución, ambos se crean — `config.json` a partir de `config.json.example`, `.env` a partir de los valores predeterminados integrados. Si un `config.json` anterior aún contiene claves de integración, se copian al nuevo `.env` y hate_crack te indica cuáles debes eliminar de `config.json`; nunca edita ese archivo por sí mismo.
- En cada ejecución, hate_crack imprime los dos archivos que realmente cargó:  ```
  [*] config.json: /home/you/.hate_crack/config.json
  [*] .env:        /home/you/.hate_crack/.env

La rama predeterminada se renombró de `master` a `main`. Corrígelo con:```bash
git remote set-head origin -a
git branch -m master main
git branch --set-upstream-to=origin/main main
git pull

Esto es idempotente: omite las herramientas ya instaladas.

**Forzar reinstalación limpia:**```bash
make reinstall

**Desinstalar** - elimina las dependencias del sistema operativo y la herramienta:```bash
make uninstall

**Ejecutar pruebas** - maneja automáticamente HATE_CRACK_SKIP_INIT cuando sea necesario:```bash
make test

**Limpiar artefactos de compilación/prueba:**```bash
make clean

### Ejecución de linters y comprobaciones de tipos

Antes de enviar cambios, ejecuta estas comprobaciones localmente. Usa `make lint` para todo, o ejecuta comprobaciones individuales:

**Ruff (linting y formateo):**```bash
make ruff
# or manually:
uv run ruff check hate_crack tests tools packaging hate_crack.py

**ty (comprobación de tipos):**```bash
make ty
# or manually:
uv run ty check hate_crack

### Ejecutar pruebas

Las pruebas detectan automáticamente cuando los submódulos no están compilados y establecen `HATE_CRACK_SKIP_INIT=1` automáticamente.```bash
make test

Con cobertura:```bash
make coverage

### Ganchos de Git (prek)

Los ganchos de Git son gestionados por [prek](https://github.com/j178/prek) (v0.3.3+). Instala los ganchos con:```bash
prek install --hook-type pre-push --hook-type pre-commit

Opciones del menú:
- **(1) Subir hashes crackeados** - Subir los resultados crackeados de la sesión actual a Hashview
- **(2) Subir wordlist** - Subir un archivo de wordlist a Hashview
- **(3) Descargar wordlist** - Descargar una wordlist desde Hashview
- **Descargar regla** - Descargar un archivo de reglas desde Hashview (descomprimido a texto plano, listo para `hashcat -r`)
- **(4) Descargar hashes restantes** - Descargar los hashes que aún no han sido crackeados (pide cambiar para crackear)
- **(5) Descargar hashes encontrados** - Descargar los hashes ya crackeados con contraseñas en texto claro (para referencia/análisis)
- **(6) Subir hashfile y crear trabajo** - Subir un nuevo hashfile y crear un trabajo de crackeo
- **(99) Volver al menú principal** - Regresar al menú principal

**Importante: Descargar encontrados vs Descargar restantes**
- **Descargar hashes restantes (4)**: Descarga los hashes sin crackear que necesitan ser crackeados. Se combina automáticamente con los hashes encontrados si están disponibles, y pide cambiar a este hashfile para crackear.
- **Descargar hashes encontrados (5)**: Descarga los hashes ya crackeados en formato hash:cleartext. Estos son de referencia y no pueden ser crackeados más. No se muestra ninguna solicitud de cambio.

#### Interfaz de línea de comandos

Las operaciones de Hashview también se pueden realizar mediante la línea de comandos:

Subir hashes crackeados:```bash
hate_crack.py --hashview upload-cracked --file <output_file>.out --hash-type 1000

Descarga un archivo de reglas (guardado descomprimido, listo para `hashcat -r`):```bash
hate_crack.py --hashview download-rules --rules-id 4 --output best64.rule

Descargar hashes encontrados (hashes ya descifrados con texto en claro):```bash
hate_crack.py --hashview download-found --customer-id 1 --hashfile-id 123

#### Configuración

Establezca las credenciales de Hashview en `.env` (son ajustes de integración, por lo que no se encuentran en `config.json`):```
HASHVIEW_URL=https://hashview.example.com
HASHVIEW_API_KEY=your-api-key-here

- **`OLLAMA_MODEL`** — El modelo de Ollama utilizado para la generación de candidatos (predeterminado: `qwen2.5:32b`). El ataque LLM usa salida estructurada (JSON), así que elige un modelo con buen soporte de herramientas/JSON.
- **`OLLAMA_NUM_CTX`** — Tamaño de la ventana de contexto para el modelo (predeterminado: `8192`). Antes era `2048` hasta que se introdujeron las estadísticas del corpus, que era demasiado pequeño para contener el prompt que se le daba: 500 textos planos muestreados ocupan aproximadamente 2,000–3,500 tokens antes del prompt del sistema y la respuesta, por lo que Ollama truncaba silenciosamente parte de la muestra que el muestreador había repartido cuidadosamente por el archivo.
- **`OLLAMA_TIMEOUT`** — Segundos de espera para una respuesta de generación antes de rendirse (predeterminado: `300`). Auméntalo si un modelo grande todavía está cargando en VRAM en la primera solicitud, lo que de otro modo puede superar el tiempo de espera; hate_crack imprime el tiempo de espera transcurrido y el nombre de esta configuración cuando se activa.
- **`OLLAMA_MAX_SAMPLE_LINES`** — El umbral por debajo del cual los modos LLM también pegan los textos planos literales en el prompt (predeterminado: `500`). Los valores ≤ 0 se tratan como 500.

  Los modos derivados del corpus (**Wordlist**, **Cracked passwords**, **Pattern rules**) describen siempre el *corpus* completo estadísticamente — proporciones de palabras base, máscaras, mayúsculas/minúsculas, longitudes, dígitos y símbolos finales, años — en lugar de pegar una parte del mismo. La agregación está acotada, por lo que un volcado de 120,000 contraseñas cuesta aproximadamente el mismo espacio de prompt que uno de 500 líneas. Cuando todo el corpus cabe por debajo de este umbral, también se incluyen los textos planos sin procesar, ya que no se gana nada ocultando un corpus pequeño al modelo.

  Esto reemplaza el comportamiento anterior de pegar una muestra espaciada uniformemente de hasta `ollamaMaxSampleLines` contraseñas. Una muestra de un volcado grande no transmitía ninguna información de frecuencia: el modelo no podía distinguir una palabra base usada por el 8% de la organización de una usada por una sola persona, que es precisamente la señal que hace que valga la pena probar una suposición.
- **`OLLAMA_NO_CLOUD`** — Cuando es `true`, se rechaza enviar cualquier cosa a un modelo *cloud* de Ollama. Ollama hace de proxy para un modelo con etiqueta `-cloud` (`gpt-oss:120b-cloud`, `deepseek-v3.1:671b-cloud`) a ollama.com a través del mismo endpoint local que usa un modelo local, por lo que nada de la solicitud parece diferente — pero los prompts de hate_crack contienen textos planos recuperados, estadísticas del corpus y el nombre, la industria y la ubicación del cliente. Con esto activado, se rechaza un nombre de modelo en la nube antes de construir cualquier solicitud. El valor predeterminado es `false`, por lo que un modelo en la nube configurado deliberadamente sigue funcionando; actívalo para compromisos en los que los datos del cliente no deban salir del host.
- **`OLLAMA_AUTO_RESEARCH`** — Cuando es `true` (predeterminado), el modo **Target info** le pide al modelo local que sugiera la industria y la ubicación en cuanto hayas escrito el nombre de la empresa, y los ofrece como valores predeterminados editables del prompt. Configúralo en `false` para obtener siempre prompts vacíos (útil con un modelo lento, ya que la investigación cuesta un viaje de ida y vuelta adicional antes de que comience el ataque).
- **`OLLAMA_HOST`** — Dónde está escuchando Ollama. Acepta un `host:port` simple (`theplague.lan:11434`) o una URL completa con esquema (`https://ollama.example.com`); en cualquier caso, la URL base se normaliza antes de usarse. El valor predeterminado es `localhost:11434`. Configúralo en `.env`, o expórtalo como una variable de entorno real para anularlo en una única ejecución; es el mismo nombre de variable que lee la propia CLI de Ollama.
- Asegúrate de que Ollama esté en ejecución y de que el modelo esté descargado (`ollama pull qwen2.5:32b`) antes de usar el Ataque LLM — hate_crack ya no descarga automáticamente los modelos que faltan.

El ataque ofrece tres modos de generación:

1. **Target info** — empresa / industria / ubicación; el modelo deriva candidatos a partir de esos detalles.

   Después de que escribas el nombre de la empresa, hate_crack le pregunta al mismo modelo local qué sabe ya sobre esa organización y rellena previamente los prompts de **Industry** y **Location** con las respuestas, mostradas entre paréntesis:   ```
   Company name: Acme Rail Services

   [!] The values in parentheses below are the local model's GUESSES, not verified OSINT.
       Press Enter to accept, or type your own value to override.
   Industry (freight rail maintenance):
   Location (Omaha, Nebraska):

Un fallo de investigación — timeout, Ollama no ejecutándose, respuesta vacía — nunca bloquea el ataque; simplemente recurre a prompts vacíos. Establece `ollamaAutoResearch` en `false` para omitir la investigación por completo.
2. **Wordlist** — deriva palabras base de una wordlist de muestra.
3. **Contraseñas crackeadas** — reintroduce los textos planos ya recuperados en esta sesión (`<hashfile>.out`) al modelo para que pueda inferir las convenciones de contraseñas propias de la organización objetivo (palabras base, estaciones, años, sufijos, leetspeak) y generar candidatos *nuevos* en el mismo estilo. Esta opción solo aparece una vez que se ha crackeado al menos un hash; el archivo completo se analiza estadísticamente exactamente igual que en el modo Wordlist (ver `ollamaMaxSampleLines` arriba).

#### Configuración PCFG

El Ataque PCFG (opción 20) y el Ataque PRINCE-LING (opción 21) usan el submódulo `pcfg_cracker`. Configúralos en `config.json`:```json
{
"pcfgRuleset": "DEFAULT",
"pcfgMaxCandidates": 50000000,
"pcfgPrinceLingMaxCandidates": 10000000
}

- **`check_for_updates`** — Habilita las comprobaciones automáticas de versiones al inicio (por defecto: `true`).
- Cuando está habilitado, hate_crack obtiene la información de la última versión desde GitHub y muestra un aviso si hay una actualización disponible.
- La comprobación se ejecuta de forma asíncrona y no bloquea el inicio. Los errores de red se ignoran silenciosamente.

##### Canales de actualización

| Canal | Flag | Fuente | Qué obtienes |
|---------|------|--------|--------------|
| Release | `--update` | `main` | La última versión publicada. Este es el valor predeterminado y lo que ofrece la comprobación de inicio. |
| Nightly | `--nightly` | `nightly-dev` | Trabajo que ha pasado la CI pero aún no ha sido publicado. |

Las versiones siguen el semver ordinario, con el incremento derivado de lo que realmente contiene el lote. El segundo componente se mueve **solo para características**: un ciclo que contenga cualquier commit `feat` se dirige a `X.(Y+1).0`, y un ciclo de solo correcciones, documentación y tareas menores se dirige a `X.Y.(Z+1)`.

`nightly-dev` etiqueta candidatas de lanzamiento para la versión a la que se dirige el lote — `v2.20.1rc1`, `v2.20.1rc2`, … — y la fusión en `main` promueve ese mismo objetivo a su lanzamiento final. Las candidatas son prelanzamientos PEP 440 reales, por lo que ordenan correctamente en ambos extremos:

    2.20.0  <  2.20.1rc1  <  2.20.1rc2  <  2.20.1  <  2.21.0rc1  <  2.21.0

El objetivo puede cambiar a mitad de ciclo: el primer `feat` que llegue lo mueve de `X.Y.(Z+1)` a `X.(Y+1).0`, y la numeración de candidatas se reinicia para el nuevo objetivo. El número siempre nombra lo que el lote publicaría hoy.

El componente mayor nunca se incrementa automáticamente: un asunto con `!` o un pie de `BREAKING CHANGE:` cuenta como característica, porque un incremento mayor automático está a una línea de asunto mal escrita de una publicación irreversible. Un incremento mayor es un acto humano explícito: etiquétalo y empújalo manualmente.

La política vive en `tools/next_version.py`, compartida por ambos flujos de etiquetado y probada unitariamente en `tests/test_next_version.py`.

La comprobación de inicio solo ofrece lanzamientos, porque las compilaciones nocturnas no publican ningún lanzamiento en GitHub y la comprobación lee el endpoint de "último lanzamiento" de GitHub — así que habilitar `check_for_updates` nunca te llevará a una nightly. Dos cosas mantienen separados los canales ahora: eso, y el hecho de que una candidata es un prelanzamiento PEP 440 genuino, por lo que una herramienta que ordene números de versión crudos también la trata como más antigua que el lanzamiento en el que se convierte.

Cualquiera de las dos flags cambia primero tu checkout a la rama correspondiente (y se niega a hacerlo si tienes cambios sin confirmar). Si estás ejecutando una nightly y quieres volver al código publicado, `--update` te devuelve a `main`.

#### Fusión automática de hashes encontrados (solo Download Left)

Al descargar hashes restantes (hashes sin descifrar), hate_crack automáticamente:
1. Intenta descargar cualquier hash encontrado (descifrado) desde Hashview como operación auxiliar
2. Fusiona los hashes encontrados con los archivos `.out` locales (por ejemplo, `left_1_123.txt.out` o `left_1_123.nt.txt.out` para el formato pwdump)
3. Elimina entradas duplicadas
4. Limpia los archivos divididos temporales después de la fusión

Esto garantiza que tus resultados locales de descifrado permanezcan sincronizados con la base de datos centralizada de Hashview cuando trabajas con hashes sin descifrar.

**Nota:** La opción download-found descarga los hashes ya descifrados por separado con fines de referencia y no realiza ninguna fusión ni solicita el descifrado.

El <hash_type> se obtiene ejecutando `hashcat --help`

Hashes de ejemplo: http://hashcat.net/wiki/doku.php?id=example_hashes```
$ hashcat --help |grep -i ntlm
   5500 | NetNTLMv1                                        | Network protocols
   5500 | NetNTLMv1 + ESS                                  | Network protocols
   5600 | NetNTLMv2                                        | Network protocols
   1000 | NTLM                                             | Operating-Systems

## Pruebas

El conjunto de pruebas es mayormente offline y utiliza mocks/fixtures. Las comprobaciones de red en vivo y las comprobaciones de dependencias del sistema se habilitan mediante variables de entorno.

### Ejecutar pruebas localmente```bash
# Run all tests
uv run pytest -v

# Run specific test
uv run pytest tests/test_hashview.py -v

### Pruebas en vivo de Hashview contra una pila Docker local

En lugar de apuntar las pruebas en vivo a un servidor Hashview remoto, puedes hacer
que el conjunto levante una pila Docker local de [Hashview](https://github.com/hashview/hashview),
la inicialice, ejecute las pruebas en vivo contra ella y la derribe. Establece
`HASHVIEW_TEST_LOCAL=1` y apunta `HASHVIEW_REPO` a un checkout de Hashview:```bash
HASHVIEW_TEST_LOCAL=1 HASHVIEW_REPO=~/projects/hashview \
  HATE_CRACK_SKIP_INIT=1 uv run pytest tests/test_hashview_cli_subcommands_subprocess.py -v

Instalación/ejecución de extremo a extremo basada en Docker (almacenada en caché mediante `Dockerfile.test`):```bash
HATE_CRACK_RUN_DOCKER_TESTS=1 uv run pytest tests/test_docker_script_install.py -v

La VM de prueba se aprovisiona automáticamente con todas las dependencias de Linux (hashcat, build-essential, curl, git, gzip, p7zip-full, transmission-daemon, ocl-icd-libopencl1, pocl-opencl-icd, uv).```bash
HATE_CRACK_RUN_LIMA_TESTS=1 uv run pytest tests/test_lima_vm_install.py -v

¿Qué regla(s) te gustaría ejecutar?
(1) best64.rule
(2) d3ad0ne.rule
(3) T0XlC.rule
(4) dive.rule
(99) YOLO...ejecutar todas las reglas
Introduce una lista separada por comas de las reglas que te gustaría ejecutar. Para ejecutar reglas encadenadas usa el símbolo +.
Por ejemplo 1+1 ejecutará best64.rule encadenada dos veces y 1,2 ejecutará best64.rule y luego d3ad0ne.rule secuencialmente.
Elige sabiamente:```




#### Extensive Pure_Hate Methodology Crack
Runs several attack methods provided by Martin Bos (formerly known as pure_hate):
  * Brute Force Attack (7 characters)
  * Dictionary Attack
    * All wordlists in `hcatWordlists` with `best64.rule`
    * `rockyou.txt` with `d3ad0ne.rule`
    * `rockyou.txt` with `T0XlC.rule`
  * Top Mask Attack (Target Time = 4 Hours)
  * Fingerprint Attack
  * Combinator Attack
  * Hybrid Attack
  * Extra - Just For Good Measure
    - Runs a dictionary attack using `rockyou.txt` with chained `combinator.rule` and `InsidePro-PasswordsPro.rule` rules

#### Brute Force Attack
Brute forces all characters with the choice of a minimum and maximum password length.

#### Top Mask Attack
Uses StatsGen and MaskGen from PACK (https://thesprawl.org/projects/pack/) to perform a top mask attack using passwords already cracked for the current session.
Presents the user a choice of target cracking time to spend (default 4 hours).

#### Fingerprint Attack
https://hashcat.net/wiki/doku.php?id=fingerprint_attack

Runs a fingerprint attack using passwords already cracked for the current session.

#### Combinator Attack
https://hashcat.net/wiki/doku.php?id=combinator_attack

Runs a combinator attack using the "rockyou.txt" wordlist.

#### Hybrid Attack
https://hashcat.net/wiki/doku.php?id=hybrid_attack

* Runs several hybrid attacks using the "rockyou.txt" wordlists.
  - Hybrid Wordlist + Mask - ?s?d wordlists/rockyou.txt ?1?1
  - Hybrid Wordlist + Mask - ?s?d wordlists/rockyou.txt ?1?1?1
  - Hybrid Wordlist + Mask - ?s?d wordlists/rockyou.txt ?1?1?1?1
  - Hybrid Mask + Wordlist - ?s?d ?1?1 wordlists/rockyou.txt
  - Hybrid Mask + Wordlist - ?s?d ?1?1?1 wordlists/rockyou.txt
  - Hybrid Mask + Wordlist - ?s?d ?1?1?1?1 wordlists/rockyou.txt

#### Pathwell Top 100 Mask Brute Force Crack
Runs a brute force attack using the top 100 masks from KoreLogic:
https://blog.korelogic.com/blog/2014/04/04/pathwell_topologies

#### PRINCE Attack
https://hashcat.net/events/p14-trondheim/prince-attack.pdf

Runs a PRINCE attack using wordlists/rockyou.txt

#### YOLO Combinator Attack
Runs a continuous combinator attack using random wordlists from the configured wordlists directory for the left and right sides.

#### Middle Combinator Attack
https://jeffh.net/2018/04/26/combinator_methods/

Runs a modified combinator attack adding a middle character mask:
wordlists/rockyou.txt + masks + worklists/rockyou.txt

Where the masks are some of the most commonly used separator characters:
2 4 <space> - _ , + . &

#### Thorough Combinator Attack
https://jeffh.net/2018/04/26/combinator_methods/

* Runs many rounds of different combinator attacks with the rockyou list.
  - Standard Combinator attack: rockyou.txt + rockyou.txt
  - Middle Combinator attack: rockyou.txt + ?n + rockyou.txt
  - Middle Combinator attack: rockyou.txt + ?s + rockyou.txt
  - End Combinator attack: rockyou.txt + rockyou.txt + ?n
  - End Combinator attack: rockyou.txt + rockyou.txt + ?s
  - Hybrid middle/end attack: rockyou.txt + ?n + rockyou.txt + ?n
  - Hybrid middle/end attack: rockyou.txt + ?s + rockyou.txt + ?s


#### Bandrel Methodology

Prompts for comma-separated names and creates a pseudo hybrid attack by capitalizing the first letter and adding up to six additional characters at the end. Each word is limited to a total of five minutes.

  - Built-in common words (seasons, months) included as a customizable `config.json` entry (`bandrel_common_basedwords`)
  - The default five-minute time limit is customizable via `bandrelmaxruntime` in `config.json`

#### Loopback Attack
https://hashcat.net/wiki/doku.php?id=loopback_attack

Uses hashcat's loopback mode to feed cracked passwords from the current session back into the attack pipeline with rules applied. This generates new password candidates based on variations of already-cracked passwords, which is particularly effective for finding related passwords that follow similar patterns.

* Prompts for rule selection to apply to the loopback candidates
* Uses an empty wordlist with the --loopback flag to process previously cracked passwords
* Automatically downloads Hashmob rules if no rules are available locally

#### LLM Attack
Uses a local Ollama instance to generate password candidates for a capture-the-flag scenario. Prompts for the fake company name, industry, and location, then sends these details to the configured LLM model to produce likely password candidates using industry terms and company name permutations. The generated candidates are fed into a hashcat wordlist+rules attack.

* Requires a running Ollama instance (default: `http://localhost:11434`, override with `OLLAMA_HOST` in `.env` or the environment) with the model already pulled — hate_crack does not auto-pull
* Candidate generation uses structured (JSON) output via Atomic Agents, so pick a model with good schema adherence (default: `qwen2.5:32b`)
* Configurable model, context window, request timeout, and sample size via `.env` (see Ollama Configuration below)
* Prompts for target company name, industry, and location. The industry and location prompts are pre-filled with the local model's guesses about the named organization (editable, and clearly labelled as guesses rather than verified OSINT); disable with `ollamaAutoResearch: false`
* Alternatively derives basewords from a sample **wordlist**, or from the **cracked passwords** of the current session (`<hashfile>.out`) so the model mirrors the target organization's own password conventions and produces new candidates in that style (only offered once something has been cracked)
* A live spinner with an elapsed-seconds counter runs during generation, and requests are bounded by `ollamaTimeout` so a model stuck loading into VRAM reports a timeout instead of hanging

**Pattern rules mode** (option 4 in the LLM submenu) takes the same shape as the [Spoonman Attack](#spoonman-attack) — a baseword list run through a rule file, both derived from one corpus — but infers each side with the model instead of extracting it. Spoonman is exact and therefore bounded: its basewords all appear in the corpus and its rules only reproduce transformations the corpus already shows. This asks the model to generalize on both axes, so it can name the *word families* behind a sample (the company and its products, site names, local sports teams, seasons, mascots) and write decorations the corpus does not contain.

* Pattern source is either the current session's cracked passwords (offered first, and only once something has been cracked, since those reveal the target's real conventions) or a sample wordlist
* **You are not asked to pick a rule file.** The model writes one, from the same corpus statistics — a stock rule file encodes the internet's habits, and the point of spending a model round trip is to encode *this* organization's
* Basewords are normalized to lowercase letters only, discarding anything under 3 characters, so the generated rules supply case, digits, and punctuation exactly once
* Generated rules are validated before hashcat sees them, and anything using an op hashcat does not have, a position argument outside `0-9A-Z`, more than 31 functions, or a stray comment or non-ASCII character is discarded. hashcat drops an invalid rule *silently* when valid rules share the file, so an unscreened line would become missing coverage rather than an error. The op table was established by testing hashcat itself, not from its rule documentation, which lists ops hashcat will not actually run
* Local-model yield varies a lot run to run, so a thin answer is asked again once and the two rounds are merged — a handful of rules would waste the pass they are spent on
* If no rule survives validation the basewords still run, unmutated, rather than throwing away the expensive half of the run
* Output lands in `<hashfile>.llm_patterns/` as `basewords.txt` and `rules.rule` — per-run scratch, laid out like `.spoonman/` and removed on exit

#### OMEN Attack
Uses the Ordered Markov ENumerator (OMEN) to train a statistical password model from a wordlist and generate password candidates. This attack learns patterns from known passwords and generates new candidates based on those patterns.

* Requires OMEN binaries (createNG and enumNG) to be built from the omen submodule
* Interactive menu: use existing model, train new model, or cancel
* Training wordlist picker shows available wordlists from configured directory or accepts a custom path
* Validates all 5 required model files (createConfig, CP/IP/EP/LN.level) before running
* Captures and reports enumNG errors instead of failing silently
* Generates up to a specified number of password candidates (configurable via `omenMaxCandidates`)
* Pipes generated candidates directly into hashcat for cracking
* Model files and metadata are stored in `~/.hate_crack/omen/` for persistence across sessions

#### Combinator Attacks Submenu
Opens an interactive submenu with six combinator attack variants (formerly at menu keys 10-12). Consolidates related attacks for cleaner menu organization:
- Combinator Attack - combines two wordlists
- YOLO Combinator Attack - combines all permutations of multiple wordlists
- Middle Combinator Attack - combines wordlists with an extra word in the middle
- Thorough Combinator Attack - comprehensive combination of wordlists with rules
- Combinator3 Attack - combines exactly 3 wordlists using `combinator3.bin`, generating all `word1+word2+word3` combinations piped to hashcat
- CombinatorX Attack - combines 2-8 wordlists using `combinatorX.bin` with optional `--sepFill` separator character between word segments

#### Ad-hoc Mask Attack
Runs hashcat mask attack (mode 3) with a user-specified custom mask string. Allows fine-grained control over character-set brute forcing.

* Opens with a choice between typing a mask and selecting a mask file
* Prompts for a hashcat mask (e.g., `?u?l?l?l?d?d` for uppercase + lowercase + lowercase + lowercase + digit + digit)
* Supports custom character sets (`-1`, `-2`, `-3`, `-4`) for specialized character combinations
* Interactive charset entry with early exit on blank input
* Mask files (`.hcmask`) can be selected with tab completion, defaulting to the bundled `masks/` directory; hashcat runs every mask in the file in order. Because a mask file defines its own charsets inline, the `-1` through `-4` prompts are skipped when one is chosen
* Useful for targeted brute forcing when you know password structure patterns

#### Markov Brute Force Attack
Generates password candidates using Markov chain statistical models. Similar to OMEN but simpler and faster.

* Checks for existing `.hcstat2` Markov table from previous sessions (with option to reuse, regenerate, or cancel)
* Generates table from training source if needed:
  - Can use cracked passwords from current session (`.out` file) as training data
  - Or select any wordlist from configured directory or custom path
* Interactive menu: choose minimum and maximum password length
* Uses `--increment` flag to test lengths in sequence
* Markov table persists with hash file (filename.out.hcstat2) for fast subsequent runs
* Faster than OMEN for general-purpose brute forcing

#### N-gram Attack
Generates n-gram candidates from a corpus file using `ngramX.bin` from hashcat-utils and pipes them into hashcat.

* Prompts for a corpus file with tab completion, defaulting to the configured wordlist directory
* Prompts for an n-gram group size (default 3)
* Gzip-compressed corpus files are auto-detected and decompressed on the fly
* Useful when you have target-relevant prose (scraped site copy, leaked documents, internal wiki exports) rather than a password list

#### Permutation Attack
Generates all character permutations of each word in a targeted wordlist and pipes them to hashcat via `permute.bin` from hashcat-utils.

* Prompts for a single wordlist file (not a directory)
* Effective against short targeted wordlists where the character set is known but the order is not (company abbreviations, name fragments, known tokens)
* WARNING: Scales as N! per word - an 8-character word produces 40,320 permutations. Only practical for words up to ~8 characters.
* Uses `permute.bin < wordlist | hashcat` pipeline pattern

#### Random Rules Attack
Generates a set of random hashcat mutation rules using `generate-rules.bin`, writes them to a temporary file, then runs hashcat against a chosen wordlist with those rules.

* Prompts for rule count (default 65536)
* Prompts for wordlist path with tab-completion and numbered selection
* Temporary rules file is cleaned up after the run regardless of outcome
* Useful when known rule sets are exhausted - explores random rule-space for additional cracks

#### Combipow Passphrase Attack
Generates all unique non-empty subset combinations from a short wordlist using `combipow.bin` and pipes them into hashcat. Designed for passphrase cracking when you know the pool of words a password was built from.

* Prompts for a wordlist file (max 63 lines - combipow generates up to 2^n-1 combinations)
* Optional space separator (`-s` flag) to insert spaces between words in each combination
* Warns if the wordlist exceeds 20 lines (output volume may be large)
* Aborts with a clear message if the wordlist exceeds 63 lines (hard limit)
* Candidates are piped directly to hashcat stdin

#### PCFG Attack
Uses [pcfg_cracker](https://github.com/lakiw/pcfg_cracker) to generate candidates from a Probabilistic Context-Free Grammar, piping `pcfg_guesser.py` output directly into hashcat's stdin mode. A PCFG models password *structure* (baseword + digits + symbol, capitalization habits, keyboard walks) with learned probabilities, so candidates come out roughly in descending likelihood order.

* Requires the `pcfg_cracker` submodule. Presence is checked at startup and reported non-fatally: if it is missing, the PCFG attacks are simply unavailable. Run `make` to fetch it.
* Uses the trained grammar named by `pcfgRuleset` in `config.json` (default `DEFAULT`), read from `pcfg_cracker/Rules/<name>/`
* Candidate count is capped by `pcfgMaxCandidates` (default 50,000,000)
* hate_crack does not wrap grammar training. To build a grammar from a target-specific password set, run pcfg_cracker's own `trainer.py` and point `pcfgRuleset` at the resulting ruleset name

#### PRINCE-LING Attack
Uses pcfg_cracker's `prince_ling.py` to derive an optimized PRINCE base wordlist from a trained grammar, then hands it to the existing PRINCE attack. PRINCE-LING picks base words the grammar says are actually productive, so the PRINCE combination space is far less wasteful than pointing PRINCE at a generic wordlist.

* Requires the `pcfg_cracker` submodule and a trained ruleset directory, same as the PCFG attack
* The generated wordlist is cached at `<hcatOptimizedWordlists>/pcfg_prince_ling_<ruleset>.txt` and reused across sessions
* Regenerates only when the ruleset directory is newer than the cached wordlist, so retraining a grammar invalidates the cache automatically
* Generation is written to a temporary file and atomically moved into place; a failed or interrupted run cleans up its partial file and leaves any existing cache intact
* Base wordlist size is capped by `pcfgPrinceLingMaxCandidates` (default 10,000,000)

#### Spoonman Attack
Derives a baseword list and a hashcat rule file from a corpus of known plaintext passwords — a previous engagement's cracked output, a leak dump, or any password list — such that the baseword x rule cross product reconstructs the corpus exactly (see the memory bound below for the one case where it does not). Contributed as issue #169 by @Spoonman1091.

Each password is split into its letters-only lowercased core (the baseword) plus a rule that rebuilds the original from it, using `l`/`u`/`c` for casing, `T{p}` toggles, `${x}`/`^{x}` for trailing and leading characters, and `i{p}{x}` for interior ones.

* When the current session already has cracked plaintexts (`<hash file>.out` exists and is non-empty), a picker offers those as the corpus ahead of a free-form path — the target's own recovered passwords derive rules describing that target's actual conventions, which is exactly what you want to fire back at the remaining uncracked hashes. Deriving from `.out` and then cracking the same hash file appends new plaintexts to that same file, growing the corpus for the next run; that is the intended feedback loop, not corruption. Sessions with no cracked output yet see no picker at all — just today's path prompt
* Prompts for the corpus, then for how much of the rule file to run: top 50% coverage (listed first and recommended), top 75%, top 95%, top 99%, or the full set
* Rules are sorted by how many passwords each one rebuilds, so a truncated file keeps the most productive rules. Coverage is extremely long-tailed: on a 98.2M-password sample, 50% coverage needed 4,120 rules while 95% needed 16,119,661 and 100% needed 21,029,696 — the last few percent typically costs orders of magnitude more rules than the first half, which is why the smallest tier is listed first and is usually the right choice
* Output is written beside the hash file in `<hash file>.spoonman/`, alongside the other ephemeral wordlists: `basewords.txt`, `rules.full.rule`, the capped rule files, and `coverage.txt` with per-milestone rule counts. Derivation is skipped on later runs of the same hash file unless the corpus has been modified since, and the directory is removed on exit by the temp-file cleanup
* Derivation is bounded in memory. Both counters would otherwise grow for the whole read with nothing written until the end, so a corpus large enough to exhaust RAM lost the entire pass to an OOM kill and produced no output; a measured run against a 31 GB corpus reached 14.1 GB resident at 11% of the file and was still accelerating. Each counter is now capped at 20 million distinct keys (about 1.6 GB apiece), and the lowest-frequency keys are discarded once it is exceeded. If that happens, the run says so on the console and in `coverage.txt`, the output reconstructs the retained keys rather than 100% of the corpus, and the coverage percentages are relative to those. Corpora below the cap are unaffected
* Passwords that cannot be expressed as a rule are written verbatim as their own baseword with a `:` no-op, so coverage stays complete. This covers two hashcat limits: rule positions cannot address past index 35, and hashcat rejects any rule with more than 31 functions — silently, when valid rules share the file
* The derivation self-checks every password by reconstructing it in-process, and reports any failures rather than reporting success
* Corpus lines may carry a hash in front of the password, as cracked output does. A leading field is dropped only when it has the shape of a hash (a hex digest at a known length, or a crypt-style `$id$` string), so `hash:salt:plain` is handled while a plaintext or wordlist entry containing a colon survives intact. `$HEX[...]` plaintexts are decoded. If most lines look like an uncracked dump rather than cracked output, `coverage.txt` records the count and the attack warns — the derived basewords and rules would otherwise be meaningless without any error being raised

#### Rosetta Attack
Mines hashcat `--debug-mode 5` logs for the basewords and rules that already cracked something, then runs their full cross product. Powered by [HashcatRosetta](https://github.com/bandrel/HashcatRosetta), the same library behind [Analyze Hashcat Rules](#analyze-hashcat-rules-rule-file-tools-option-5).

No setup is needed to feed it: `_add_debug_mode_for_rules` appends `--debug-mode 5 --debug-file` to every rule-based hashcat invocation hate_crack makes, so the logs accumulate in `hcatDebugLogPath` (`~/.hate_crack/hashcat_debug` by default, one file per session) as a side effect of normal use. A mode 5 log records only candidates that cracked a hash, in the form `baseword:rule:candidate:wordlist`, which is what makes both halves known-productive against this target population; the trailing wordlist field also shows which list is earning its keep on a multi-wordlist run. HashcatRosetta parses mode 4 and mode 5 alike, so logs written before the switch are still read.

The value is in the cross product rather than the recorded pairs. A pair present in a log has already cracked its hash and will not crack another, but a rule that worked on one baseword has usually never been tried against the others — so N basewords and M rules yield close to N x M untried candidates.

The menu first asks how to rank rules — choices 1-3 below, plus a fourth, unrelated mode:

* Rules can be ranked by application frequency, by how many distinct basewords each one worked on, or by how many unique candidates each one generated. Frequency is the default; baseword spread is the better choice when the goal is a rule set that generalizes past the specific words it was learned from
* Only after one of those three is picked does hate_crack list the logs found in `hcatDebugLogPath` newest-first with their sizes; pick one, pick all of them (up to 20), or type a path to a log from elsewhere
* Prompts for how many top rules to keep (default 100) and how many top basewords (default all). Zero means unlimited for either. The keyspace is the product of the two and is printed before hashcat starts
* Output is written beside the hash file in `<hash file>.rosetta/` as `basewords.txt` and `rules.rule`, alongside the other ephemeral wordlists, and the directory is removed on exit by the temp-file cleanup
* Reading stops at 1,000,000 debug lines, since the analyzer needs the whole batch in memory at once. Truncation is reported on the console rather than assumed harmless — logs from a long run routinely exceed this, in which case the newest log is the one worth selecting
* **LLM Mask Attack** (4) - a different mode entirely, and the only one that needs no debug logs. Prompts for a natural-language description of the passwords you expect (length, character patterns, symbols, etc.), sends it to the locally configured Ollama model, writes the returned masks to `<hash file>.hcmask`, and runs a `-a 3` hashcat mask attack against them

#### Wordlist Tools (option 80)
A submenu of wordlist preprocessing utilities using hashcat-utils binaries. All tools read from and write to files on disk. All file and directory path prompts support tab completion.

| Key | Tool | Description |
|-----|------|-------------|
| 1 | Filter by Length | Keep only words between a min and max length (`len.bin`) |
| 2 | Require Char Classes | Keep words that include all char classes in mask (`req-include.bin`). Mask: 1=lower, 2=upper, 4=digit, 8=symbol (additive) |
| 3 | Exclude Char Classes | Remove words containing any char class in mask (`req-exclude.bin`). Same mask encoding |
| 4 | Extract Substring | Cut bytes from each word at a given offset and optional length (`cutb.bin`) |
| 5 | Split by Length | Create per-length files in an output directory (`splitlen.bin`) |
| 6 | Subtract Wordlist | Remove lines from a wordlist that appear in one or more remove files. Mode 1 uses `rli2.bin` (single file); mode 2 uses `rli.bin` (multiple files) |
| 7 | Shard Wordlist | Split a wordlist into N equal, interleaved parts in one run, written as `base.001`…`base.00N` for distributed cracking (`gate.bin`) |
| 8 | Optimize Wordlists | Dedupe and split the selected wordlists into per-length files under an output directory |
| 9 | Download from Hashmob.net | Browse and download wordlists from Hashmob.net into the configured wordlist directory |
| 10 | Download from Weakpass | Browse and download Weakpass wordlist torrents, with automatic extraction |

All binaries are in `hate_crack/hashcat-utils/bin/`.

#### Rule File Tools (option 81)
Preprocesses hashcat rule files using `cleanup-rules.bin` and `rules_optimize.bin` from hashcat-utils, and downloads rule files from Hashmob.net.

* **Clean** (1) - removes invalid syntax and duplicate rules using `cleanup-rules.bin`. Useful after combining rule files or downloading rules from external sources.
* **Optimize** (2) - consolidates redundant operations using `rules_optimize.bin`. Reduces rule file size and improves cracking speed.
* **Clean and optimize** (3) - runs both operations in sequence via a temporary file, then writes the final result.
* **Download rules from Hashmob.net** (4) - fetches rule files into the configured `rulesDirectory`.
* **Analyze Hashcat rules** (5) - opcode frequency analysis of a rule file, powered by HashcatRosetta.

The three preprocessing operations read from an input file and write to a separate output file (original is never modified).

#### Download Rules from Hashmob.net (Rule File Tools option 4)
Downloads the latest rule files from Hashmob.net's rule repository. These rules are curated and optimized for password cracking and can be used with the Quick Crack and Loopback Attack modes.

* Downloads rule sets in parallel using a thread pool (up to 4 concurrent downloads)
* Skips rules already downloaded locally
* Reports download summary with success/failure counts
* Stores rules in the configured rules directory

#### Analyze Hashcat Rules (Rule File Tools option 5)
Powered by HashcatRosetta (https://github.com/bandrel/HashcatRosetta), this feature analyzes hashcat rule files to provide detailed insights into rule composition and complexity.

* Prompts for a rule file path
* Displays frequency analysis of rule opcodes (operations)
* Helps understand what transformations a rule set performs
* Useful for rule debugging and optimization

#### Download Wordlists from Hashmob.net (Wordlist Tools option 9)
Downloads wordlists from Hashmob.net's collection of cracked passwords and commonly used wordlists.

* Interactive menu for browsing available wordlists
* Progress tracking for large downloads
* Stores wordlists in configured wordlist directory

#### Weakpass Wordlist Menu (Wordlist Tools option 10)
Interactive menu for downloading and managing wordlists from Weakpass.com via BitTorrent.

* Browse available Weakpass wordlist torrents
* Download specific wordlists or entire collections
* Automatic extraction of compressed archives
* Progress tracking for torrent downloads

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### Version History

The full, per-release changelog now lives in [CHANGELOG.md](https://github.com/trustedsec/hate_crack/blob/HEAD/CHANGELOG.md).