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

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

Se hai clonato senza i sottomoduli, inizializzali:```bash
git submodule update --init --recursive

`.env.example` viene distribuito con ogni chiave di credenziale vuota. `.env` stesso non deve **mai** essere committato — è in gitignore, insieme alle sue solite varianti di backup, e hate_crack lo crea sempre con permessi `0600` (solo lettura/scrittura del proprietario). `.env.example` è generato dallo schema; rigeneralo dopo aver modificato `hate_crack/config_schema.py` con `uv run python -m hate_crack.config_writer`.

### 3. Installare le dipendenze e hate_crack

Il modo più semplice è eseguire `make` (o `make install`), che rileva automaticamente il tuo sistema operativo e installa:
- Dipendenze esterne (p7zip, transmission-daemon / transmission-remote)
- Compila i sottomoduli (hashcat-utils, princeprocessor, pcfg_cracker e opzionalmente omen)
- Dipendenze Python tramite uv e uno shim CLI in `~/.local/bin/hate_crack````bash
make

**Oppure installa le dipendenze manualmente:**

### Dipendenze esterne
Queste sono richieste per determinati flussi di download/estrazione:

- `7z`/`7za` (p7zip) — usati per estrarre archivi `.7z`.
- `transmission-daemon` / `transmission-remote` — usati per scaricare i torrent da Weakpass.

Comandi di installazione manuale:

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

Quindi installa le dipendenze Python e lo shim della 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

In alternativa, esegui tramite `uv`:```bash
uv run hate_crack.py <hash_file> <hash_type>

Il comando `make install` crea uno shim bash in `~/.local/bin/hate_crack` che viene eseguito dalla directory del repository, così config e asset vengono sempre trovati indipendentemente dalla directory di lavoro corrente.

La config viene cercata anche in:
- La radice del repository e la directory del pacchetto
- `~/.hate_crack`

**Nota:** `hcatPath` in `config.json` serve solo per indicare la posizione del binario di hashcat (facoltativo se hashcat è nel PATH). Gli asset di Hate_crack (hashcat-utils, princeprocessor, pcfg_cracker, omen) vengono caricati dalla directory del repository e inclusi automaticamente da `make install`.

### Esecuzione come script
Lo script usa uno shebang `uv`. Rendilo eseguibile ed esegui:```bash
chmod +x hate_crack.py
./hate_crack.py

### Utilizzo non interattivo / scriptato

Per l'automazione puoi avviare un singolo attacco direttamente, bypassando il menu. Il nome dell'attacco è il primo argomento, seguito dal file di hash e dal tipo di hash di hashcat. I prompt di pre-elaborazione (filtro degli account computer, brute force LM-first, deduplicazione degli account duplicati) accettano automaticamente i loro valori predefiniti in questa modalità. Il processo termina con `0` in caso di successo e con un codice non zero in caso di errore (file di hash mancante, tipo di hash non numerico, wordlist mancante o nome di regola sconosciuto).```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

Ciò riguarda i cloni creati prima di luglio 2026. La cronologia pubblicata è stata poi riscritta per rimuovere alcuni file che non avrebbero mai dovuto essere committati, il che ha assegnato a ogni commit un nuovo ID; di conseguenza, i tag di un clone più vecchio puntano a oggetti che questo repository non contiene più, e git rifiuta di spostare un tag che possiede già. Non c'è nulla di sbagliato nel tuo checkout e nessun dato di cracking è a rischio.

Recupera con un reset una tantum. Questo scarta i commit locali e le modifiche nel checkout, quindi se hai personalizzato qualcosa tracciato da git (a differenza di `config.json`, che non è tracciato), committalo prima su un branch:```bash
cd /path/to/hate_crack
git fetch --tags --force origin
git checkout -B main origin/main
make install

Ciò significa che gli asset di `hate_crack` non sono stati inclusi nel pacchetto installato.

**Comprendere i percorsi:**
- `hcatPath` in config.json → punta alla **posizione del binario hashcat** (opzionale, può essere nel PATH)
- `hashcat-utils/` e `princeprocessor/` → inclusi nel pacchetto tramite `make install`

**Soluzione:**
Reinstallare usando il Makefile, che compila i sottomoduli e installa lo strumento:```bash
cd /path/to/hate_crack  # the repository checkout
make install

**Caricamento della configurazione:**
- Precedenza per ogni chiave: `os.environ` > il file home di quella chiave (`.env` o `config.json`) > valore predefinito integrato
- Le chiavi mancanti ricadono sui valori predefiniti integrati; `config.json.example` documenta ogni chiave di `config.json`
- Entrambi i file vengono cercati, indipendentemente l'uno dall'altro, in questo ordine: **root del repository**, poi la **directory del pacchetto installato**, poi **`~/.hate_crack`**. Vince la prima corrispondenza; è normale che i due file provengano da directory diverse.
- Al primo avvio, vengono creati entrambi — `config.json` da `config.json.example`, `.env` dai valori predefiniti integrati. Se un `config.json` meno recente contiene ancora chiavi di integrazione, queste vengono copiate nel nuovo `.env` e hate_crack ti dice quali eliminare da `config.json`; non modifica mai direttamente quel file.
- A ogni esecuzione, hate_crack stampa i due file che ha effettivamente caricato:  ```
  [*] config.json: /home/you/.hate_crack/config.json
  [*] .env:        /home/you/.hate_crack/.env

Il branch predefinito è stato rinominato da `master` a `main`. Correggi con:```bash
git remote set-head origin -a
git branch -m master main
git branch --set-upstream-to=origin/main main
git pull

Questo è idempotente - salta gli strumenti già installati.

**Forza la reinstallazione pulita:**```bash
make reinstall

**Disinstallazione** - rimuove le dipendenze del sistema operativo e lo strumento:```bash
make uninstall

**Esegui i test** - gestisce automaticamente HATE_CRACK_SKIP_INIT quando necessario:```bash
make test

**Pulisci gli artefatti di build/test:**```bash
make clean

### Esecuzione dei linter e dei controlli di tipo

Prima di eseguire il push delle modifiche, esegui questi controlli localmente. Usa `make lint` per tutto, oppure esegui i singoli controlli:

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

**ty (controllo dei tipi):**```bash
make ty
# or manually:
uv run ty check hate_crack

### Esecuzione dei test

I test rilevano automaticamente quando i sottomoduli non sono compilati e impostano `HATE_CRACK_SKIP_INIT=1` automaticamente.```bash
make test

Con copertura:```bash
make coverage

### Hook di Git (prek)

Gli hook di Git sono gestiti da [prek](https://github.com/j178/prek) (v0.3.3+). Installa gli hook con:```bash
prek install --hook-type pre-push --hook-type pre-commit

Opzioni del menu:
- **(1) Carica hash craccati** - Carica i risultati craccati dalla sessione corrente su Hashview
- **(2) Carica wordlist** - Carica un file wordlist su Hashview
- **(3) Scarica wordlist** - Scarica una wordlist da Hashview
- **Scarica regola** - Scarica un file di regole da Hashview (decompresso in testo in chiaro, pronto per `hashcat -r`)
- **(4) Scarica hash rimanenti** - Scarica gli hash non ancora craccati (chiede di passare a questi per il cracking)
- **(5) Scarica hash trovati** - Scarica gli hash già craccati con password in chiaro (per riferimento/analisi)
- **(6) Carica hashfile e crea job** - Carica un nuovo hashfile e crea un job di cracking
- **(99) Torna al menu principale** - Torna al menu principale

**Importante: Scarica trovati vs Scarica rimanenti**
- **Scarica hash rimanenti (4)**: Scarica gli hash non craccati che devono essere craccati. Unisce automaticamente gli eventuali hash trovati, se disponibili, e chiede di passare a questo hashfile per il cracking.
- **Scarica hash trovati (5)**: Scarica gli hash già craccati nel formato hash:cleartext. Questi sono solo di riferimento e non possono essere ulteriormente craccati. Nessuna richiesta di cambio viene mostrata.

#### Interfaccia a riga di comando

Le operazioni di Hashview possono essere eseguite anche tramite riga di comando:

Carica hash craccati:```bash
hate_crack.py --hashview upload-cracked --file <output_file>.out --hash-type 1000

Scarica un file di regole (salvato decompresso, pronto per `hashcat -r`):```bash
hate_crack.py --hashview download-rules --rules-id 4 --output best64.rule

Scarica gli hash trovati (hash già craccati con testo in chiaro):```bash
hate_crack.py --hashview download-found --customer-id 1 --hashfile-id 123

#### Configurazione

Imposta le credenziali di Hashview in `.env` (sono impostazioni di integrazione, quindi non vivono in `config.json`):```
HASHVIEW_URL=https://hashview.example.com
HASHVIEW_API_KEY=your-api-key-here

- **`OLLAMA_MODEL`** — Il modello Ollama usato per la generazione dei candidati (default: `qwen2.5:32b`). L'attacco LLM usa output strutturato (JSON), quindi scegli un modello con un buon supporto per tool/JSON.
- **`OLLAMA_NUM_CTX`** — Dimensione della finestra di contesto per il modello (default: `8192`). Prima dell'introduzione delle statistiche sul corpus era `2048`, troppo piccola per contenere il prompt che veniva fornito: 500 plaintext campionati occupano all'incirca 2.000–3.500 token prima del system prompt e della risposta, quindi Ollama troncava silenziosamente parte del campione che il sampler aveva accuratamente distribuito nel file.
- **`OLLAMA_TIMEOUT`** — Secondi di attesa per una risposta di generazione prima di arrendersi (default: `300`). Alza questo valore se un modello grande sta ancora caricando in VRAM alla prima richiesta, cosa che altrimenti può superare il timeout; hate_crack stampa il timeout trascorso e il nome di questa impostazione quando scatta.
- **`OLLAMA_MAX_SAMPLE_LINES`** — La soglia al di sotto della quale le modalità LLM includono anche i plaintext letterali nel prompt (default: `500`). Valori ≤ 0 vengono trattati come 500.

  Le modalità derivate dal corpus (**Wordlist**, **Cracked passwords**, **Pattern rules**) descrivono sempre l'*intero* corpus in modo statistico — quote delle baseword, maschere, maiuscole/minuscole, lunghezze, cifre e simboli finali, anni — invece di incollarne una porzione. L'aggregazione è limitata, quindi un dump di 120.000 password costa più o meno lo stesso spazio nel prompt di uno da 500 righe. Quando l'intero corpus rientra in questa soglia, vengono inclusi anche i plaintext grezzi, dato che non c'è alcun vantaggio nel nascondere un corpus piccolo al modello.

  Questo sostituisce il precedente comportamento che incollava un campione equidistante di massimo `ollamaMaxSampleLines` password. Un campione di un dump grande non trasmetteva alcuna informazione sulla frequenza: il modello non poteva distinguere una baseword usata dall'8% dell'organizzazione da una usata da una singola persona, che è proprio il segnale che rende utile provare una supposizione.
- **`OLLAMA_NO_CLOUD`** — Quando è `true`, rifiuta di inviare qualsiasi cosa a un modello *cloud* di Ollama. Ollama inoltra un modello taggato `-cloud` (`gpt-oss:120b-cloud`, `deepseek-v3.1:671b-cloud`) a ollama.com attraverso lo stesso endpoint locale usato da un modello locale, quindi nulla nella richiesta appare diverso — ma i prompt di hate_crack trasportano plaintext recuperati, statistiche sul corpus e il nome, il settore e la località del cliente. Con questa impostazione, un nome di modello cloud viene rifiutato prima che qualsiasi richiesta venga costruita. Il default è `false`, quindi un modello cloud configurato deliberatamente continua a funzionare; attivalo per incarichi in cui i dati del cliente non devono lasciare l'host.
- **`OLLAMA_AUTO_RESEARCH`** — Quando è `true` (default), la modalità **Target info** chiede al modello locale di suggerire il settore e la località appena hai digitato il nome dell'azienda, e li offre come valori predefiniti modificabili nei prompt. Impostalo su `false` per ottenere sempre prompt vuoti (utile con un modello lento, poiché la ricerca costa un round-trip extra prima che l'attacco inizi).
- **`OLLAMA_HOST`** — Dove Ollama è in ascolto. Accetta un semplice `host:port` (`theplague.lan:11434`) o un URL completo con schema (`https://ollama.example.com`); in entrambi i casi l'URL di base viene normalizzato prima dell'uso. Default: `localhost:11434`. Impostalo in `.env`, oppure esportalo come variabile d'ambiente reale per sovrascriverlo per una singola esecuzione — è lo stesso nome di variabile che legge la CLI di Ollama.
- Assicurati che Ollama sia in esecuzione e che il modello sia scaricato (`ollama pull qwen2.5:32b`) prima di usare l'LLM Attack — hate_crack non scarica automaticamente più i modelli mancanti.

L'attacco offre tre modalità di generazione:

1. **Target info** — azienda / settore / località; il modello deriva i candidati da questi dettagli.

   Dopo che digiti il nome dell'azienda, hate_crack chiede allo stesso modello locale cosa sa già di quell'organizzazione e precompila i prompt **Industry** e **Location** con le risposte, mostrate tra parentesi:   ```
   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 errore di ricerca — timeout, Ollama non in esecuzione, risposta vuota — non blocca mai l'attacco; si limita a ripiegare su prompt vuoti. Imposta `ollamaAutoResearch` a `false` per saltare del tutto la ricerca.
2. **Wordlist** — deriva le parole base da una wordlist di esempio.
3. **Password crackate** — fornisci al modello i testi in chiaro già recuperati in questa sessione (`<hashfile>.out`) così che possa dedurre le convenzioni password dell'organizzazione bersaglio (parole base, stagioni, anni, suffissi, leetspeak) e generare *nuovi* candidati nello stesso stile. Questa opzione viene elencata solo dopo che almeno un hash è stato crackato; l'intero file viene analizzato statisticamente esattamente come nella modalità Wordlist (vedi `ollamaMaxSampleLines` sopra).

#### Configurazione PCFG

L'attacco PCFG (opzione 20) e l'attacco PRINCE-LING (opzione 21) usano il sottomodulo `pcfg_cracker`. Configurali in `config.json`:```json
{
"pcfgRuleset": "DEFAULT",
"pcfgMaxCandidates": 50000000,
"pcfgPrinceLingMaxCandidates": 10000000
}

- **`check_for_updates`** — Abilita i controlli automatici di versione all'avvio (default: `true`).
- Quando abilitato, hate_crack recupera le ultime informazioni di release da GitHub e mostra un avviso se è disponibile un aggiornamento.
- Il controllo viene eseguito in modo asincrono e non blocca l'avvio. Gli errori di rete vengono ignorati silenziosamente.

##### Canali di aggiornamento

| Canale | Flag | Origine | Cosa ottieni |
|---------|------|--------|--------------|
| Release | `--update` | `main` | L'ultima release pubblicata. Questa è l'impostazione predefinita ed è ciò che offre il controllo all'avvio. |
| Nightly | `--nightly` | `nightly-dev` | Lavoro che ha superato la CI ma non è ancora stato rilasciato. |

Le versioni seguono il normale semver, con l'incremento derivato da ciò che è effettivamente presente nel lotto. La seconda componente si muove **solo per le funzionalità**: un ciclo che contiene un commit `feat` è destinato a `X.(Y+1).0`, mentre un ciclo fatto solo di correzioni, documentazione e attività di manutenzione è destinato a `X.Y.(Z+1)`.

`nightly-dev` tagga i release candidate per la versione verso cui il lotto è diretto — `v2.20.1rc1`, `v2.20.1rc2`, … — e il merge su `main` promuove quello stesso obiettivo alla release finale. I candidate sono vere pre-release PEP 440, quindi si ordinano correttamente a entrambe le estremità:

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

L'obiettivo può cambiare a metà ciclo: il primo `feat` che arriva lo sposta da `X.Y.(Z+1)` a `X.(Y+1).0` e la numerazione dei candidate riparte per il nuovo obiettivo. Il numero indica sempre ciò che il lotto rilascerebbe oggi.

La componente major non viene mai incrementata automaticamente — un soggetto `!` o un footer `BREAKING CHANGE:` conta come funzionalità, perché un major automatico è a una riga di soggetto digitata male di distanza da una release pubblicata irreversibile. Un major è un atto umano esplicito: taggalo e pubblicalo manualmente.

La politica si trova in `tools/next_version.py`, condivisa da entrambi i workflow di tagging e coperta da unit test in `tests/test_next_version.py`.

Il controllo all'avvio offre solo release, perché le build nightly non pubblicano alcuna release GitHub e il controllo legge l'endpoint "latest release" di GitHub — quindi abilitare `check_for_updates` non ti porterà mai su una nightly. Due cose tengono separati i canali: questo, e il fatto che un candidate è una vera pre-release PEP 440, quindi anche uno strumento che ordina numeri di versione grezzi lo considera più vecchio della release in cui si trasforma.

Entrambi i flag portano prima il tuo checkout sul ramo corrispondente (e rifiutano di farlo se hai modifiche non committate). Se stai eseguendo una nightly e vuoi tornare al codice rilasciato, `--update` ti riporta su `main`.

#### Unione automatica degli hash trovati (solo Download Left)

Quando scarichi gli hash left (hash non craccati), hate_crack automaticamente:
1. Tenta di scaricare gli hash trovati (craccati) da Hashview come operazione ausiliaria
2. Unisce gli hash trovati con i file `.out` locali (ad es. `left_1_123.txt.out` o `left_1_123.nt.txt.out` per il formato pwdump)
3. Rimuove le voci duplicate
4. Pulisce i file temporanei suddivisi dopo l'unione

Questo garantisce che i tuoi risultati di cracking locali rimangano sincronizzati con il database centralizzato di Hashview quando lavori con hash non craccati.

**Nota:** L'opzione download-found scarica separatamente gli hash già craccati a scopo di riferimento e non esegue alcuna unione né richiede di craccare.

L'`<hash_type>` si ottiene eseguendo `hashcat --help`

Hash di esempio: 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

## Test

La suite di test è per lo più offline e utilizza mock/fixture. I controlli di rete live e
i controlli delle dipendenze di sistema sono opt-in tramite variabili d'ambiente.

### Eseguire i test in locale```bash
# Run all tests
uv run pytest -v

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

### Test Hashview dal Vivo Contro uno Stack Docker Locale

Invece di indirizzare i test dal vivo verso un server Hashview remoto, puoi
far avviare alla suite uno stack Docker locale di [Hashview](https://github.com/hashview/hashview),
popolarlo, eseguire i test dal vivo contro di esso e infine smontarlo. Imposta
`HASHVIEW_TEST_LOCAL=1` e punta `HASHVIEW_REPO` a un checkout di 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

Installazione/esecuzione end-to-end basata su Docker (con cache tramite `Dockerfile.test`):```bash
HATE_CRACK_RUN_DOCKER_TESTS=1 uv run pytest tests/test_docker_script_install.py -v

The test VM provisions automatically with all Linux dependencies (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

Quali regole desideri eseguire?
(1) best64.rule
(2) d3ad0ne.rule
(3) T0XlC.rule
(4) dive.rule
(99) YOLO...esegui tutte le regole
Inserisci un elenco di regole separate da virgole che desideri eseguire. Per eseguire regole concatenate usa il simbolo +.
Ad esempio 1+1 eseguirà best64.rule concatenata due volte e 1,2 eseguirà best64.rule e poi d3ad0ne.rule in sequenza.
Scegli con saggezza:```




#### 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).