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

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

如果你克隆时未包含子模块，请初始化它们：```bash
git submodule update --init --recursive

`.env.example` 已提交并随项目分发，所有凭据键均为空。`.env` 本身**绝不能**提交——它已被 gitignore 忽略，连同其常见的备份命名变体，并且 hate_crack 始终以 `0600` 模式（仅属主可读写）创建它。`.env.example` 由 schema 生成；修改 `hate_crack/config_schema.py` 后，请使用 `uv run python -m hate_crack.config_writer` 重新生成。

### 3. 安装依赖与 hate_crack

最简单的方法是运行 `make`（或 `make install`），它会自动检测你的操作系统并安装：
- 外部依赖（p7zip、transmission-daemon / transmission-remote）
- 构建子模块（hashcat-utils、princeprocessor、pcfg_cracker，以及可选的 omen）
- 通过 uv 安装 Python 依赖，并在 `~/.local/bin/hate_crack` 处提供 CLI 启动器```bash
make

**或者手动安装依赖：**

### 外部依赖
以下是某些下载/解压流程所必需的：

- `7z`/`7za` (p7zip) — 用于解压 `.7z` 压缩包。
- `transmission-daemon` / `transmission-remote` — 用于下载 Weakpass 种子。

手动安装命令：

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

然后安装 Python 依赖和 CLI shim:```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

或者，通过 `uv` 运行：```bash
uv run hate_crack.py <hash_file> <hash_type>

`make install` 命令会在 `~/.local/bin/hate_crack` 创建一个 bash shim，该 shim 从仓库目录运行，因此无论当前工作目录在哪里，配置和资源文件总能被找到。

配置也会在以下位置搜索：
- 仓库根目录和包目录
- `~/.hate_crack`

**注意：** `config.json` 中的 `hcatPath` 仅用于指定 hashcat 二进制文件的位置（如果 hashcat 已在 PATH 中，则可选）。Hate_crack 的资源（hashcat-utils、princeprocessor、pcfg_cracker、omen）从仓库目录加载，并由 `make install` 自动捆绑。

### 作为脚本运行
脚本使用 `uv` shebang。使其可执行并运行：```bash
chmod +x hate_crack.py
./hate_crack.py

### 非交互式/脚本化使用

为了自动化，你可以直接发起一次攻击，跳过菜单。攻击名称作为第一个参数，后跟哈希文件和 hashcat 哈希类型。预处理提示（计算机账户过滤、LM 优先暴力破解、重复账户去重）在此模式下会自动接受其默认值。进程在成功时退出 `0`，出错时退出非零值（缺少哈希文件、非数字哈希类型、缺少字典文件或未知规则文件名）。```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

这会影响 2026 年 7 月之前创建的克隆。发布的历史记录被重写，
以移除一些绝不应该提交的文件，这使
每个提交都获得了新的 ID；因此，旧克隆的标签指向了这个
仓库不再包含的对象，git 拒绝移动它已有的标签。
你的检出没有问题，也没有任何破解数据面临风险。

通过一次性重置即可恢复。这会丢弃本地提交和检出中的编辑，
因此，如果你自定义了任何由 git 跟踪的内容（相对于
`config.json`，它不被跟踪），请先将其提交到一个分支：```bash
cd /path/to/hate_crack
git fetch --tags --force origin
git checkout -B main origin/main
make install

这意味着 hate_crack 资源未被打包到已安装的程序包中。

**理解路径：**
- config.json 中的 `hcatPath` → 指向 **hashcat 二进制文件位置**（可选，也可位于 PATH 中）
- `hashcat-utils/` 和 `princeprocessor/` → 由 `make install` 打包进程序

**解决方案：**
使用 Makefile 重新安装，它会构建子模块并安装该工具：```bash
cd /path/to/hate_crack  # the repository checkout
make install

**配置加载：**
- 每个键的优先级：`os.environ` > 该键对应的主目录文件（`.env` 或 `config.json`）> 内置默认值
- 缺失的键回退到内置默认值；`config.json.example` 记录了每个 `config.json` 键
- 两个文件会彼此独立地按以下顺序搜索：**仓库根目录**，然后是**已安装的包目录**，最后是**`~/.hate_crack`**。先匹配到的生效；两个文件来自不同目录是正常现象。
- 首次运行时，两个文件都会被创建——`config.json` 从 `config.json.example` 生成，`.env` 从内置默认值生成。如果旧的 `config.json` 中仍包含集成密钥，它们会被复制到新的 `.env` 中，并且 hate_crack 会告诉你应从 `config.json` 中删除哪些密钥；它从不自行编辑该文件。
- 每次运行时，hate_crack 都会打印它实际加载的两个文件：  ```
  [*] config.json: /home/you/.hate_crack/config.json
  [*] .env:        /home/you/.hate_crack/.env

默认分支已从 `master` 重命名为 `main`。修复方法：```bash
git remote set-head origin -a
git branch -m master main
git branch --set-upstream-to=origin/main main
git pull

这是幂等的——它会跳过已安装的工具。

**强制干净重装：**```bash
make reinstall

**卸载** - 移除操作系统依赖和工具：```bash
make uninstall

**运行测试** - 在需要时自动处理 HATE_CRACK_SKIP_INIT：```bash
make test

**清理构建/测试产物：**```bash
make clean

### 运行 Linter 和类型检查

在推送更改之前，请在本地运行这些检查。使用 `make lint` 一次性完成所有检查，或单独运行各项检查：

**Ruff（代码检查与格式化）：**```bash
make ruff
# or manually:
uv run ruff check hate_crack tests tools packaging hate_crack.py

**ty (类型检查):**```bash
make ty
# or manually:
uv run ty check hate_crack

### 运行测试

测试会自动检测子模块未构建的情况，并自动设置 `HATE_CRACK_SKIP_INIT=1`。```bash
make test

覆盖范围：```bash
make coverage

### Git 钩子（prek）

Git 钩子由 [prek](https://github.com/j178/prek)（v0.3.3+）管理。使用以下命令安装钩子：```bash
prek install --hook-type pre-push --hook-type pre-commit

菜单选项：
- **(1) Upload Cracked Hashes** - 将当前会话中已破解的结果上传到 Hashview
- **(2) Upload Wordlist** - 将字典文件上传到 Hashview
- **(3) Download Wordlist** - 从 Hashview 下载字典
- **Download Rule** - 从 Hashview 下载规则文件（解压为纯文本，可直接用于 `hashcat -r`）
- **(4) Download Left Hashes** - 下载剩余未破解的哈希（提示切换以进行破解）
- **(5) Download Found Hashes** - 下载已破解的哈希及明文密码（供参考/分析）
- **(6) Upload Hashfile and Create Job** - 上传新的哈希文件并创建破解任务
- **(99) Back to Main Menu** - 返回主菜单

**重要提示：Download Found 与 Download Left 的区别**
- **Download Left Hashes (4)**：下载需要破解的未破解哈希。如果存在已找到的哈希，会自动合并，并提示切换到此哈希文件进行破解。
- **Download Found Hashes (5)**：以 hash:cleartext 格式下载已破解的哈希。这些哈希仅供参考，无法继续破解。不显示切换提示。

#### 命令行界面

Hashview 操作也可以通过命令行执行：

上传已破解的哈希：```bash
hate_crack.py --hashview upload-cracked --file <output_file>.out --hash-type 1000

下载规则文件（已解压保存，可直接用于 `hashcat -r`）：```bash
hate_crack.py --hashview download-rules --rules-id 4 --output best64.rule

下载已找到的哈希 (已破解的哈希及其明文):```bash
hate_crack.py --hashview download-found --customer-id 1 --hashfile-id 123

#### 配置

在 `.env` 中设置 Hashview 凭据（这些是集成设置，因此它们不存放在 `config.json` 中）：```
HASHVIEW_URL=https://hashview.example.com
HASHVIEW_API_KEY=your-api-key-here

- **`OLLAMA_MODEL`** — 用于候选口令生成的 Ollama 模型（默认：`qwen2.5:32b`）。LLM 攻击使用结构化（JSON）输出，因此请选择对工具/JSON 支持良好的模型。
- **`OLLAMA_NUM_CTX`** — 模型的上下文窗口大小（默认：`8192`）。在引入语料统计之前，此值为 `2048`，太小而无法容纳所给出的提示词：500 条抽样明文在系统提示词和回复之前大约占用 2,000–3,500 个 token，因此 Ollama 会静默截断抽样器精心散布在文件中的部分样本。
- **`OLLAMA_TIMEOUT`** — 放弃之前等待生成响应的秒数（默认：`300`）。如果大型模型在首次请求时仍在加载到显存中，请调高此值，否则可能会超过超时；`hate_crack` 在触发时会打印已消耗的超时时间和此设置的名称。
- **`OLLAMA_MAX_SAMPLE_LINES`** — 低于此阈值时，LLM 模式也会将字面明文粘贴到提示词中（默认：`500`）。≤ 0 的值按 500 处理。

  基于语料的模式（**Wordlist**、**Cracked passwords**、**Pattern rules**）始终以统计方式描述*整个*语料——基础词占比、掩码、大小写、长度、尾随数字和符号、年份——而非粘贴其中的片段。聚合是有界的，因此 120,000 条口令的转储与 500 行的转储所占提示词空间大致相同。当整个语料低于此阈值时，原始明文也会一并包含，因为向模型隐藏小型语料毫无收益。

  这取代了之前粘贴最多 `ollamaMaxSampleLines` 条均匀间隔口令样本的行为。大型转储的样本完全无法传达频率信息：模型无法区分被组织中 8% 的人使用的基础词与仅被一个人使用的基础词，而这恰恰是让一次猜测值得运行的关键信号。
- **`OLLAMA_NO_CLOUD`** — 当为 `true` 时，拒绝向 Ollama *云端*模型发送任何内容。Ollama 通过本地模型使用的同一本地端点，将带 `-cloud` 标签的模型（`gpt-oss:120b-cloud`、`deepseek-v3.1:671b-cloud`）代理到 ollama.com，因此请求看起来毫无不同——但 `hate_crack` 的提示词携带已恢复的明文、语料统计，以及客户名称、行业和位置。设置此选项后，在构建任何请求之前就会拒绝云端模型名称。默认值为 `false`，因此特意配置的云端模型仍可正常工作；在客户数据不得离开主机的项目中，请开启此选项。
- **`OLLAMA_AUTO_RESEARCH`** — 当为 `true`（默认）时，**Target info** 模式会在您刚输入公司名称后立即让本地模型建议行业和位置，并将其作为可编辑的提示词默认值提供。设置为 `false` 则始终获得空白提示词（在模型较慢时很有用，因为调研会在攻击开始前额外花费一次往返）。
- **`OLLAMA_HOST`** — Ollama 监听的地址。接受裸 `host:port`（`theplague.lan:11434`）或带协议的完整 URL（`https://ollama.example.com`）；无论哪种方式，基础 URL 都会在使用前被规范化。默认为 `localhost:11434`。在 `.env` 中设置，或将其导出为真实的环境变量，以便在单次运行中覆盖该值——这与 Ollama 自身 CLI 读取的变量名相同。
- 使用 LLM Attack 之前，请确保 Ollama 正在运行且模型已拉取（`ollama pull qwen2.5:32b`）——`hate_crack` 不再自动拉取缺失的模型。

该攻击提供三种生成模式：

1. **Target info** — 公司 / 行业 / 位置；模型根据这些详细信息推导出候选口令。

  输入公司名称后，`hate_crack` 会询问同一个本地模型它已经了解该组织的哪些信息，并将答案预填到 **行业** 和 **位置** 提示框中，以括号形式显示：   ```
   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):

研究失败 — 超时、Ollama 未运行、空答案 — 绝不会阻止攻击；它只会回退为空白提示。将 `ollamaAutoResearch` 设为 `false` 可完全跳过研究。
2. **Wordlist** — 从样本单词列表中推导基础词。
3. **已破解的密码** — 将本次会话已恢复的明文（`<hashfile>.out`）反馈给模型，使其能推断目标组织自身的密码惯例（基础词、季节、年份、后缀、leet 拼写），并生成同样风格的*新*候选。只有至少破解出一个哈希后，该选项才会列出；整个文件与 Wordlist 模式一样进行统计分析（参见上文 `ollamaMaxSampleLines`）。

#### PCFG 配置

PCFG 攻击（选项 20）和 PRINCE-LING 攻击（选项 21）使用 `pcfg_cracker` 子模块。在 `config.json` 中配置它们：```json
{
"pcfgRuleset": "DEFAULT",
"pcfgMaxCandidates": 50000000,
"pcfgPrinceLingMaxCandidates": 10000000
}

- **`check_for_updates`** — 在启动时启用自动版本检查（默认：`true`）。
- 启用后，hate_crack 会从 GitHub 获取最新发布信息，并在有可用更新时显示通知。
- 该检查异步运行，不会阻塞启动。网络错误会被静默忽略。

##### 更新通道

| Channel | Flag | Source | What you get |
|---------|------|--------|--------------|
| 发布版 | `--update` | `main` | 最新的正式发布版本。这是默认选项，也是启动检查所提供的内容。 |
| 夜间版 | `--nightly` | `nightly-dev` | 已通过 CI 但尚未发布的代码。 |

版本遵循普通的语义化版本规范（semver），版本号的提升由批次中的实际内容决定。**仅当包含功能时**，第二个组成部分（次版本号）才会递增：含有任意 `feat` 提交的周期将面向 `X.(Y+1).0`，而仅包含修复、文档和杂务的周期将面向 `X.Y.(Z+1)`。

`nightly-dev` 会为批次所面向的版本打上发布候选标签——`v2.20.1rc1`、`v2.20.1rc2`、……——合并到 `main` 后，该目标会被提升为最终正式版本。候选版本是真正的 PEP 440 预发布版本，因此它们在两端都能正确排序：

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

目标在周期中可能会改变：第一个合入的 `feat` 会将其从 `X.Y.(Z+1)` 移至 `X.(Y+1).0`，并且候选版本的编号会针对新目标重新开始。该编号始终标示该批次当下发布时会使用的版本。

主版本号永远不会被自动提升——`!` 主题或 `BREAKING CHANGE:` 脚注会被视为功能变更，因为自动提升主版本号意味着一个敲错的提交主题就可能导致一次不可逆的正式发布。主版本号的提升是明确的人工操作：手动打标签并推送。

该策略位于 `tools/next_version.py` 中，由两个打标签工作流共用，并在 `tests/test_next_version.py` 中进行了单元测试。

启动检查只会提供正式发布版本，因为夜间版构建根本不会发布 GitHub release，而该检查读取的是 GitHub 的 "latest release" 端点——因此启用 `check_for_updates` 永远也不会把你带到夜间版。目前有两个因素将两个通道区分开来：其一是这一点；其二是候选版本是真正的 PEP 440 预发布版本，因此任何按原始版本号排序的工具也会将其视为比它最终变成的正式版更旧。

任意一个标志都会先将你的检出（checkout）切换到对应分支（如果你有未提交的更改，则会拒绝切换）。如果你正在运行夜间版并希望回到已发布代码，`--update` 会将你移回 `main`。

#### 自动合并已破解哈希（仅下载左侧哈希时）

下载左侧哈希（未破解哈希）时，hate_crack 会自动：
1. 尝试从 Hashview 下载任何已找到（已破解）的哈希，作为辅助操作
2. 将已找到的哈希与本地 `.out` 文件合并（例如，pwdump 格式的 `left_1_123.txt.out` 或 `left_1_123.nt.txt.out`）
3. 移除重复条目
4. 合并后清理临时分片文件

这可以确保在处理未破解哈希时，你本地的破解结果与 Hashview 的集中式数据库保持同步。

**注意：** download-found 选项会单独下载已破解的哈希以供参考，不会执行任何合并操作，也不会提示进行破解。

通过运行 `hashcat --help` 可获取 <hash_type>。

示例哈希：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

## 测试

测试套件大部分为离线运行，并使用模拟对象（mocks）和固定数据（fixtures）。实时网络检查和系统依赖检查可通过环境变量选择启用。

### 在本地运行测试```bash
# Run all tests
uv run pytest -v

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

### 针对本地 Docker 堆栈的实时 Hashview 测试

除了将实时测试指向远程 Hashview 服务器之外，您还可以让测试套件启动一个本地 [Hashview](https://github.com/hashview/hashview) Docker 堆栈，为其填充种子数据，对其运行实时测试，然后将其拆除。设置 `HASHVIEW_TEST_LOCAL=1`，并将 `HASHVIEW_REPO` 指向 Hashview 的检出目录：```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

基于 Docker 的端到端安装/运行（通过 `Dockerfile.test` 缓存）：```bash
HATE_CRACK_RUN_DOCKER_TESTS=1 uv run pytest tests/test_docker_script_install.py -v

测试虚拟机自动配置所有 Linux 依赖项（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

您想运行哪些规则？
(1) best64.rule
(2) d3ad0ne.rule
(3) T0XlC.rule
(4) dive.rule
(99) YOLO...运行所有规则
输入您要运行的规则列表，以逗号分隔。要链式运行规则，请使用 + 符号。
例如，1+1 将链式运行 best64.rule 两次，1,2 将依次运行 best64.rule 和 d3ad0ne.rule。
请慎重选择：```




#### 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).