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<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Docker Setup — Rikune</title>
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name="description"
content="Docker deployment guide for Rikune analyzer/runtime split deployments, static-only Linux analyzers, and hybrid Windows runtime setups."
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<div class="breadcrumb"><a href="index.html">Rikune</a> / Docker</div>
<h1><span data-lang-en>Docker Deployment</span><span data-lang-zh>Docker 部署</span></h1>
<p>
<span data-lang-en
>Run Rikune as a Linux analyzer plane in Docker, then keep real sample execution on a
separate Windows runtime when you intentionally opt in.</span
>
<span data-lang-zh
>将 Rikune 作为 Linux 分析面运行在 Docker 中，并在你明确开启时把真实样本执行交给独立的
Windows 运行时面。</span
>
</p>
</div>
</div>
<!-- Content -->
<div class="doc-layout">
<aside class="doc-sidebar">
<div class="doc-sidebar__title">
<span data-lang-en>On this page</span><span data-lang-zh>本页目录</span>
</div>
<a href="#prerequisites">Prerequisites</a>
<a href="#quickstart">Quick Start</a>
<a href="#topologies"
><span data-lang-en>Deployment Topologies</span><span data-lang-zh>部署拓扑</span></a
>
<a href="#plugin-gen"
><span data-lang-en>Plugin-Driven Generation</span
><span data-lang-zh>插件驱动生成</span></a
>
<a href="#services">Services Overview</a>
<a href="#production">Production Mode</a>
<a href="#compose">Docker Compose</a>
<a href="#mcp-client">MCP Client</a>
<a href="#env-vars">Environment Variables</a>
<a href="#dashboard">Web Dashboard</a>
<a href="#bundled">Bundled Tools</a>
<a href="#troubleshooting">Troubleshooting</a>
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<!-- Prerequisites -->
<section id="prerequisites" class="reveal">
<h2><span data-lang-en>Prerequisites</span><span data-lang-zh>前置要求</span></h2>
<ul>
<li>Docker 20.10+</li>
<li data-lang-en>At least 10 GB free disk space</li>
<li data-lang-zh>至少 10 GB 可用磁盘空间</li>
<li data-lang-en>8 GB+ RAM recommended</li>
<li data-lang-zh>建议 8 GB+ 内存</li>
</ul>
</section>
<!-- Quick Start -->
<section id="quickstart" class="reveal">
<h2><span data-lang-en>Quick Start</span><span data-lang-zh>快速开始</span></h2>
<h3>
<span data-lang-en>Guided installer</span
><span data-lang-zh>向导式安装</span>
</h3>
<div class="code-block">
<div class="code-block__header">
<span>Terminal</span><button class="code-block__copy">Copy</button>
</div>
<pre>.\rikune.ps1</pre>
</div>
<div class="code-block">
<div class="code-block__header">
<span>bash</span><button class="code-block__copy">Copy</button>
</div>
<pre>./rikune.sh</pre>
</div>
<p>
<span data-lang-en
>The top-level scripts are the normal entry point. They install, start, stop, show
status, stream logs, run health checks, regenerate Docker files, and bootstrap the
Windows runtime when you choose the hybrid path.</span
>
<span data-lang-zh
>顶层脚本是普通用户的入口。它们负责安装、启动、停止、状态查询、日志、健康检查、
Docker 文件生成，以及在选择 hybrid 方案时初始化 Windows 运行时。</span
>
</p>
<table class="doc-table">
<thead>
<tr>
<th><span data-lang-en>Windows menu</span><span data-lang-zh>Windows 菜单</span></th>
<th><span data-lang-en>Meaning</span><span data-lang-zh>含义</span></th>
</tr>
</thead>
<tbody>
<tr>
<td><code>1</code></td>
<td>
<span data-lang-en>Install static Docker analyzer</span
><span data-lang-zh>安装纯静态 Docker analyzer</span>
</td>
</tr>
<tr>
<td><code>2</code></td>
<td>
<span data-lang-en
>Install Docker analyzer, Windows Host Agent, and Windows Sandbox support on
the same host</span
><span data-lang-zh
>在同一台 Windows 主机上安装 Docker analyzer、Windows Host Agent 和 Windows
Sandbox 支持</span
>
</td>
</tr>
<tr>
<td><code>3</code></td>
<td>
<span data-lang-en>Install the full Linux Docker image</span
><span data-lang-zh>安装全量 Linux Docker 镜像</span>
</td>
</tr>
<tr>
<td><code>4</code>-<code>9</code></td>
<td>
<span data-lang-en>Start, status/health, logs, stop, doctor, runtime status</span
><span data-lang-zh>启动、状态/健康检查、日志、停止、诊断、运行时状态</span>
</td>
</tr>
</tbody>
</table>
<h3>
<span data-lang-en>Static-only analyzer (safe default)</span
><span data-lang-zh>纯静态 analyzer（安全默认）</span>
</h3>
<div class="code-block">
<div class="code-block__header">
<span>Terminal</span><button class="code-block__copy">Copy</button>
</div>
<pre>.\rikune.ps1 install -Profile static -DataRoot "D:\Docker\rikune"</pre>
</div>
<h3>
<span data-lang-en>Hybrid analyzer + Windows runtime plane</span
><span data-lang-zh>混合 analyzer + Windows 运行时面</span>
</h3>
<div class="code-block">
<div class="code-block__header">
<span>Terminal</span><button class="code-block__copy">Copy</button>
</div>
<pre><span class="comment"># Single Windows host</span>
.\rikune.ps1 install -Profile hybrid -InstallRuntime
<span class="comment"># Hyper-V VM runtime backend for debugging</span>
.\rikune.ps1 install -Profile hybrid -InstallRuntime `
-RuntimeBackend hyperv-vm `
-HyperVVmName "rikune-runtime" `
-HyperVSnapshotName "clean-runtime" `
-HyperVRuntimeEndpoint "http://192.168.1.50:18081" `
-HyperVRestoreOnRelease
<span class="comment"># Linux analyzer + remote Windows Host Agent</span>
./rikune.sh install --profile hybrid --windows-host <windows-host> --windows-user <windows-user></pre>
</div>
<p>
<span data-lang-en
>In the single-host Windows hybrid path, the script installs or starts the Host
Agent, checks the selected runtime backend, writes <code>.docker-runtime.env</code>,
builds the analyzer image, and starts <code>rikune-analyzer</code>. The runtime is
launched on demand by dynamic/sandbox tools, not as a permanently open GUI during
installation. The default Windows Sandbox backend must run from a logged-on user
session; the Host Agent binds to <code>0.0.0.0</code> by default and the installer adds
best-effort Hyper-V firewall rules so Docker can reach it through
<code>host.docker.internal:18082</code> and the runtime portproxy range; the Hyper-V VM backend can be used for checkpoint-based debugging.</span
>
<span data-lang-zh
>在单机 Windows hybrid 方案中，脚本会安装或启动 Host Agent、检查所选运行时后端、写入
<code>.docker-runtime.env</code>、构建 analyzer 镜像并启动
<code>rikune-analyzer</code>。运行时由动态/沙箱工具按需启动，安装时不会要求一直打开一个
Sandbox 窗口。默认 Windows Sandbox 后端必须运行在已登录用户会话里；Host Agent
默认绑定 <code>0.0.0.0</code>，并 best-effort 添加 Hyper-V firewall 规则，让 Docker 可以通过 <code>host.docker.internal:18082</code>
和 runtime portproxy 端口访问；Hyper-V VM
后端可用于基于 checkpoint 的调试，并可选择退出时回滚、关机保留或保留脏现场。</span
>
</p>
<h3>
<span data-lang-en>Full Linux toolchain image</span
><span data-lang-zh>全量 Linux 工具链镜像</span>
</h3>
<div class="code-block">
<div class="code-block__header">
<span>Terminal</span><button class="code-block__copy">Copy</button>
</div>
<pre>.\rikune.ps1 install -Profile full -DataRoot "D:\Docker\rikune"</pre>
</div>
<h3>
<span data-lang-en>Windows runtime bootstrap</span
><span data-lang-zh>Windows 运行时面初始化</span>
</h3>
<div class="code-block">
<div class="code-block__header">
<span>PowerShell</span><button class="code-block__copy">Copy</button>
</div>
<pre>.\install-runtime-windows.ps1 -Headless -ApiKey <host-agent-key>
<span class="comment"># Hyper-V VM backend</span>
.\install-runtime-windows.ps1 -Headless -ApiKey <host-agent-key> `
-RuntimeBackend hyperv-vm `
-HyperVVmName "rikune-runtime" `
-HyperVSnapshotName "clean-runtime" `
-HyperVRuntimeEndpoint "http://192.168.1.50:18081" `
-HyperVRestoreOnRelease</pre>
</div>
<blockquote>
<span data-lang-en
><strong>Auth split:</strong> use <code>RUNTIME_HOST_AGENT_API_KEY</code> for Analyzer
→ Host Agent control requests, and use <code>RUNTIME_API_KEY</code> only when the
Windows Runtime Node itself requires a separate API key.</span
>
<span data-lang-zh
><strong>鉴权分层：</strong><code>RUNTIME_HOST_AGENT_API_KEY</code> 仅用于 Analyzer →
Host Agent 控制请求；<code>RUNTIME_API_KEY</code> 仅在 Windows Runtime Node
自身需要单独鉴权时使用。</span
>
</blockquote>
<blockquote>
<span data-lang-en
><strong>Runtime debug flow:</strong> MCP clients should call
<code>dynamic.runtime.status</code> first to inspect configured Runtime Node and Host Agent capabilities,
<code>dynamic.toolkit.status</code> to inspect runtime-side CDB/ProcDump/ProcMon/Sysmon/TTD/x64dbg/dnSpy/Frida/dotnet readiness,
<code>dynamic.deep_plan</code> to pick a bounded behavior/debugger/memory/network/.NET profile,
<code>debug.network.plan</code>, <code>debug.managed.plan</code>, and <code>debug.gui.handoff</code> for network lab, .NET runtime, and manual GUI handoff detail,
<code>dynamic.persona.plan</code> to prepare a planning-only Sandbox/Hyper-V persona checklist,
<code>dynamic.behavior.diff</code>, <code>analysis.evidence.graph</code>, and
<code>crypto.lifecycle.graph</code> to correlate runtime observations with static expectations,
<code>runtime.hyperv.control</code> when a Hyper-V VM checkpoint must be listed, created, restored, or stopped,
<code>runtime.debug.session.start</code> to start or attach a Windows runtime and choose
<code>hyperv_retention_policy</code> as <code>clean_rollback</code>, <code>stop_only</code>, or <code>preserve_dirty</code>,
<code>runtime.debug.command</code> to dispatch <code>debug.session.*</code>,
<code>sandbox.execute</code>, <code>dynamic.behavior.capture</code>, telemetry, ProcDump, managed safe-run, or dump work, then
<code>runtime.debug.session.stop</code> to release the backend.</span
>
<span data-lang-zh
><strong>运行时调试流程：</strong>MCP 客户端应先调用
<code>dynamic.runtime.status</code> 检查 Runtime Node 和 Host Agent 能力，用
<code>dynamic.toolkit.status</code> 查询 runtime 内 CDB/ProcDump/ProcMon/Sysmon/TTD/x64dbg/dnSpy/Frida/dotnet 就绪状态，用
<code>dynamic.deep_plan</code> 选择受限的行为、调试器、内存、网络或 .NET profile，用
<code>debug.network.plan</code>、<code>debug.managed.plan</code> 和 <code>debug.gui.handoff</code> 查看网络实验、.NET runtime 和手动 GUI handoff 细节，用
<code>dynamic.persona.plan</code> 准备只规划不启动的 Sandbox/Hyper-V persona 清单，用
<code>dynamic.behavior.diff</code>、<code>analysis.evidence.graph</code> 和
<code>crypto.lifecycle.graph</code> 把运行时观察关联回静态预期，再调用
<code>runtime.hyperv.control</code> 列出、创建、恢复或停止 Hyper-V VM checkpoint，然后调用
<code>runtime.debug.session.start</code> 启动或附着 Windows runtime，并用
<code>hyperv_retention_policy</code> 选择 <code>clean_rollback</code>、<code>stop_only</code> 或
<code>preserve_dirty</code>，再用
<code>runtime.debug.command</code> 分发 <code>debug.session.*</code>、
<code>sandbox.execute</code>、<code>dynamic.behavior.capture</code>、遥测、ProcDump、managed safe-run 或转储任务，最后用
<code>runtime.debug.session.stop</code> 释放后端。</span
>
</blockquote>
</section>
<section id="topologies" class="reveal">
<h2><span data-lang-en>Deployment Topologies</span><span data-lang-zh>部署拓扑</span></h2>
<p>
<span data-lang-en
>Rikune now supports a split analyzer/runtime model. Pick the deployment shape that
matches your safety and automation goals:</span
>
<span data-lang-zh
>Rikune 现在支持 analyzer/runtime
拆分模型。请根据你的安全目标和自动化需求选择部署方式：</span
>
</p>
<table class="doc-table">
<thead>
<tr>
<th><span data-lang-en>Topology</span><span data-lang-zh>拓扑</span></th>
<th><span data-lang-en>Analyzer Plane</span><span data-lang-zh>分析面</span></th>
<th><span data-lang-en>Runtime Plane</span><span data-lang-zh>运行时面</span></th>
<th><span data-lang-en>Recommended Use</span><span data-lang-zh>推荐用途</span></th>
</tr>
</thead>
<tbody>
<tr>
<td>
<strong
><span data-lang-en>Static-only Docker</span
><span data-lang-zh>纯静态 Docker</span></strong
>
</td>
<td>
<code>docker-compose.analyzer.yml</code> with <code>RUNTIME_MODE=disabled</code>
</td>
<td><span data-lang-en>None</span><span data-lang-zh>无</span></td>
<td>
<span data-lang-en>Safe Linux analysis without executing samples</span
><span data-lang-zh>不运行样本的安全 Linux 分析</span>
</td>
</tr>
<tr>
<td>
<strong
><span data-lang-en>Hybrid Docker + Windows Runtime</span
><span data-lang-zh>混合 Docker + Windows Runtime</span></strong
>
</td>
<td>
<span data-lang-en>Linux Docker analyzer</span
><span data-lang-zh>Linux Docker analyzer</span>
</td>
<td>
<span data-lang-en>Windows Host Agent + Windows Sandbox</span
><span data-lang-zh>Windows Host Agent + Windows Sandbox</span>
</td>
<td>
<span data-lang-en>Split static analysis from real sample execution</span
><span data-lang-zh>将静态分析与真实样本执行拆分</span>
</td>
</tr>
<tr>
<td>
<strong
><span data-lang-en>Full Docker</span
><span data-lang-zh>全量 Docker</span></strong
>
</td>
<td>
<code>docker-compose.yml</code> with <code>rikune</code>
</td>
<td><span data-lang-en>None by default</span><span data-lang-zh>默认无</span></td>
<td>
<span data-lang-en>Heavy Linux-side toolchain experiments</span
><span data-lang-zh>需要完整 Linux 工具链的实验场景</span>
</td>
</tr>
<tr>
<td>
<strong
><span data-lang-en>Windows Native + Auto Sandbox</span
><span data-lang-zh>Windows 原生 + Auto Sandbox</span></strong
>
</td>
<td>
<span data-lang-en>Windows-native analyzer</span
><span data-lang-zh>Windows 原生 analyzer</span>
</td>
<td>
<span data-lang-en>Local Windows Sandbox</span
><span data-lang-zh>本机 Windows Sandbox</span>
</td>
<td>
<span data-lang-en>Single-host dynamic execution with explicit sandboxing</span
><span data-lang-zh>单机显式沙箱动态执行</span>
</td>
</tr>
</tbody>
</table>
<blockquote>
<span data-lang-en
><strong>Important:</strong> Docker or WSL analyzers cannot use
<code>auto-sandbox</code>. For Linux-side analyzers, use the <code>hybrid</code>
profile when you need a Windows runtime.</span
>
<span data-lang-zh
><strong>重要：</strong>Docker 或 WSL 中的 analyzer 不能使用
<code>auto-sandbox</code>。如果 analyzer 在 Linux 一侧，需要 Windows 运行时时请使用
<code>hybrid</code> profile。</span
>
</blockquote>
</section>
<!-- Plugin-Driven Docker Generation -->
<section id="plugin-gen" class="reveal">
<h2>
<span data-lang-en>Plugin-Driven Docker Generation</span
><span data-lang-zh>插件驱动的 Docker 生成</span>
</h2>
<p>
<span data-lang-en>
Rikune does <strong>not</strong> maintain one hand-written Dockerfile. Each plugin
declares exactly what system-level dependencies it needs, and the generator produces
profile-specific outputs: <code>static</code>, <code>hybrid</code>, and
<code>full</code>. Plugins remain the <em>single source of truth</em>.
</span>
<span data-lang-zh>
Rikune <strong>不</strong>手动维护单一 Dockerfile。
每个插件在代码中声明自己所需的系统级依赖， 生成器自动读取所有插件声明并产出 Dockerfile
和 Compose 文件，并按 <code>static</code>、<code>hybrid</code>、<code>full</code>
三个 profile 分流。插件是<em>唯一真相源</em>。
</span>
</p>
<h3><span data-lang-en>How It Works</span><span data-lang-zh>工作原理</span></h3>
<!-- Architecture Diagram -->
<div
style="
background: rgba(15, 23, 42, 0.6);
border: 1px solid var(--border);
border-radius: 12px;
padding: 28px 24px;
margin: 20px 0 28px;
font-family: var(--mono);
font-size: 0.82rem;
line-height: 1.8;
overflow-x: auto;
"
>
<pre style="margin: 0; color: var(--text-muted)">
<span style="color:var(--accent);">┌─────────────────────────────────────────────────────────────┐</span>
<span style="color:var(--accent);">│</span> <strong style="color:#fff;">Plugin SDK</strong> <span style="color:var(--text-dim);">(src/plugins/sdk.ts)</span> <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> <span style="color:var(--green);">PluginSystemDep</span> { <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> name, type, envVar, dockerDefault, <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> <strong style="color:var(--amber);">dockerFeature?</strong> → controls # @if blocks <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> <strong style="color:var(--amber);">aptPackages?</strong> → merged into apt-get install <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> <strong style="color:var(--amber);">dockerValidation?</strong> → merged into validation step <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">│</span> } <span style="color:var(--accent);">│</span>
<span style="color:var(--accent);">└──────────────────────┬──────────────────────────────────────┘</span>
│ <span style="color:var(--text-dim);">each plugin implements</span>
▼
<span style="color:var(--green);">┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐</span>
<span style="color:var(--green);">│</span> ghidra <span style="color:var(--green);">│</span> <span style="color:var(--green);">│</span> frida <span style="color:var(--green);">│</span> <span style="color:var(--green);">│</span> malware <span style="color:var(--green);">│</span> <span style="color:var(--green);">│</span> ...×32 <span style="color:var(--green);">│</span>
<span style="color:var(--green);">│</span> systemDeps<span style="color:var(--green);">│</span> <span style="color:var(--green);">│</span> systemDeps<span style="color:var(--green);">│</span> <span style="color:var(--green);">│</span> systemDeps<span style="color:var(--green);">│</span> <span style="color:var(--green);">│</span> systemDeps<span style="color:var(--green);">│</span>
<span style="color:var(--green);">└─────┬────┘ └─────┬────┘ └─────┬────┘ └─────┬────┘</span>
│ │ │ │
└──────────────┴──────┬───────┴──────────────┘
│ <span style="color:var(--text-dim);">dynamic import at build time</span>
▼
<span style="color:var(--amber);">┌─────────────────────────────────────────────────────────────┐</span>
<span style="color:var(--amber);">│</span> <strong style="color:#fff;">Generator</strong> <span style="color:var(--text-dim);">(scripts/generate-docker.mjs)</span> <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">│</span> <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">│</span> 1. discoverPluginIds() → scan dist/plugins/ <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">│</span> 2. loadPluginMetadata() → import deps + execution domain <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">│</span> 3. collectRequirements() → features, apt, env, validation <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">│</span> 4. processTemplate() → conditional Dockerfile <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">│</span> 5. generateCompose() → profile-specific Compose files <span style="color:var(--amber);">│</span>
<span style="color:var(--amber);">└──────────────┬──────────────────────────────┬───────────────┘</span>
│ │
▼ ▼
<span style="color:var(--accent);">📄 Dockerfile(s)</span> <span style="color:var(--accent);">📄 Compose file(s)</span>
<span style="color:var(--text-dim);"> (multi-stage, only</span> <span style="color:var(--text-dim);"> (volumes, env, args</span>
<span style="color:var(--text-dim);"> needed stages)</span> <span style="color:var(--text-dim);"> matching features)</span>
</pre>
</div>
<h3>
<span data-lang-en>Step 1 — Plugin Declares Dependencies</span
><span data-lang-zh>步骤 1 — 插件声明依赖</span>
</h3>
<p>
<span data-lang-en
>Each plugin's <code>systemDeps</code> array describes what it needs. Three fields
control Docker generation:</span
>
<span data-lang-zh
>每个插件的 <code>systemDeps</code> 数组描述其所需依赖，三个字段控制 Docker
生成：</span
>
</p>
<div class="code-block">
<div class="code-block__header">
<span>TypeScript — src/plugins/ghidra/index.ts</span
><button class="code-block__copy">Copy</button>
</div>
<pre><span class="kw">systemDeps</span>: [
{
type: <span class="str">'binary'</span>,
name: <span class="str">'analyzeHeadless'</span>,
envVar: <span class="str">'GHIDRA_INSTALL_DIR'</span>,
dockerDefault: <span class="str">'/opt/ghidra'</span>,
<span class="comment">// ── Docker-specific fields ──</span>
<strong>dockerFeature: <span class="str">'ghidra'</span></strong>, <span class="comment">// activates # @if ghidra blocks</span>
<strong>dockerValidation:</strong> [
<span class="str">'test -f /opt/ghidra/support/analyzeHeadless'</span>
]
},
{
type: <span class="str">'binary'</span>,
name: <span class="str">'java'</span>,
dockerDefault: <span class="str">'/opt/java/openjdk/bin/java'</span>,
<strong>dockerFeature: <span class="str">'ghidra'</span></strong>, <span class="comment">// same feature group</span>
}
]</pre>
</div>
<div class="code-block" style="margin-top: 16px">
<div class="code-block__header">
<span>TypeScript — src/plugins/debug-session/index.ts</span
><button class="code-block__copy">Copy</button>
</div>
<pre><span class="kw">systemDeps</span>: [
{
type: <span class="str">'binary'</span>,
name: <span class="str">'gdb'</span>,
<strong>dockerFeature: <span class="str">'gdb'</span></strong>,
<strong>aptPackages: [<span class="str">'gdb'</span>, <span class="str">'ltrace'</span>, <span class="str">'strace'</span>]</strong>, <span class="comment">// merged into apt-get</span>
}
]</pre>
</div>
<h3>
<span data-lang-en>Step 2 — Template Uses Conditionals</span
><span data-lang-zh>步骤 2 — 模板使用条件块</span>
</h3>
<p>
<span data-lang-en
>The Dockerfile template (<code>docker/Dockerfile.template</code>) wraps optional
stages in <code># @if</code> / <code># @endif</code> markers:</span
>
<span data-lang-zh
>Dockerfile 模板（<code>docker/Dockerfile.template</code>）用 <code># @if</code> /
<code># @endif</code> 标记包裹可选阶段：</span
>
</p>
<div class="code-block">
<div class="code-block__header">
<span>Dockerfile.template</span><button class="code-block__copy">Copy</button>
</div>
<pre><span class="comment"># @if ghidra</span>
FROM eclipse-temurin:21-jdk AS ghidra-stage
ARG GHIDRA_VERSION=11.3.2
RUN curl -L ... | unzip ...
<span class="comment"># @endif ghidra</span>
<span class="comment"># @if angr</span>
RUN python -m venv /opt/angr-venv && \
/opt/angr-venv/bin/pip install angr
<span class="comment"># @endif angr</span></pre>
</div>
<h3>
<span data-lang-en>Step 3 — Generator Reads Plugins & Produces Output</span
><span data-lang-zh>步骤 3 — 生成器读取插件并输出</span>
</h3>
<p>
<span data-lang-en
>The generator (<code>scripts/generate-docker.mjs</code>) performs 5 steps with
<strong>zero hardcoded mappings</strong>:</span
>
<span data-lang-zh
>生成器（<code>scripts/generate-docker.mjs</code>）执行 5
个步骤，<strong>没有任何硬编码映射</strong>：</span
>
</p>
<table class="doc-table">
<thead>
<tr>
<th>#</th>
<th><span data-lang-en>Step</span><span data-lang-zh>步骤</span></th>
<th><span data-lang-en>What it does</span><span data-lang-zh>做了什么</span></th>
</tr>
</thead>
<tbody>
<tr>
<td><span class="badge badge--blue">1</span></td>
<td><code>discoverPluginIds()</code></td>
<td>
<span data-lang-en
>Scans <code>dist/plugins/</code> for all plugin directories</span
><span data-lang-zh>扫描 <code>dist/plugins/</code> 中的所有插件目录</span>
</td>
</tr>
<tr>
<td><span class="badge badge--blue">2</span></td>
<td><code>loadPluginMetadata()</code></td>
<td>
<span data-lang-en
>Dynamic-imports each plugin and reads its <code>systemDeps</code> plus runtime
execution domain</span
><span data-lang-zh>动态导入每个插件并读取其 <code>systemDeps</code> 与运行域</span>
</td>
</tr>
<tr>
<td><span class="badge badge--blue">3</span></td>
<td><code>collectRequirements()</code></td>
<td>
<span data-lang-en
>Merges all <code>dockerFeature</code>, <code>aptPackages</code>,
<code>envVar</code>+<code>dockerDefault</code>,
<code>dockerValidation</code></span
><span data-lang-zh
>合并所有
<code>dockerFeature</code
>、<code>aptPackages</code>、<code>envVar</code>+<code>dockerDefault</code>、<code
>dockerValidation</code
></span
>
</td>
</tr>
<tr>
<td><span class="badge badge--blue">4</span></td>
<td><code>processTemplate()</code></td>
<td>
<span data-lang-en
>Evaluates <code># @if</code> blocks, substitutes
<code>{{PLACEHOLDERS}}</code></span
><span data-lang-zh
>评估 <code># @if</code> 条件块，替换 <code>{{PLACEHOLDERS}}</code></span
>
</td>
</tr>
<tr>
<td><span class="badge badge--blue">5</span></td>
<td><code>generateCompose()</code></td>
<td>
<span data-lang-en
>Emits the selected profile's Compose file with matching volumes, env,
build args, runtime mode, and plugin list</span
><span data-lang-zh
>输出所选 profile 的 Compose 文件，包含匹配的卷、环境变量、构建参数、运行时模式和插件列表</span
>
</td>
</tr>
</tbody>
</table>
<h3><span data-lang-en>CLI Usage</span><span data-lang-zh>CLI 用法</span></h3>
<div class="code-block">
<div class="code-block__header">
<span>bash</span><button class="code-block__copy">Copy</button>
</div>
<pre><span class="comment"># Full Linux image</span>
node scripts/generate-docker.mjs --profile=full
<span class="comment"># Static-only analyzer image</span>
node scripts/generate-docker.mjs --profile=static
<span class="comment"># Hybrid analyzer with remote Windows runtime</span>
node scripts/generate-docker.mjs --profile=hybrid
<span class="comment"># Generate all profiles</span>
node scripts/generate-docker.mjs --all-profiles
<span class="comment"># Preview without writing files</span>
node scripts/generate-docker.mjs --profile=static --dry-run</pre>
</div>
<blockquote>
<span data-lang-en
>💡 <strong>Tip:</strong> <code>rikune.ps1</code>, <code>rikune.sh</code>, and
<code>install-docker.ps1</code> run the generator automatically before Compose
build. They discover all plugins, write the selected profile files, then build the
selected service — no manual step needed.</span
>
<span data-lang-zh
>💡 <strong>提示</strong>：<code>rikune.ps1</code>、<code>rikune.sh</code> 和
<code>install-docker.ps1</code> 会在 Compose 构建前自动运行生成器。它们发现所有插件、写入所选
profile 文件、然后构建所选服务 — 无需手动操作。</span
>
</blockquote>
<h3>
<span data-lang-en>Adding Docker Support to a New Plugin</span
><span data-lang-zh>为新插件添加 Docker 支持</span>
</h3>
<p>
<span data-lang-en
>If your plugin needs a system tool in the Docker image, just add the Docker fields to
its <code>systemDeps</code>:</span
>
<span data-lang-zh
>如果你的新插件需要 Docker 镜像中的系统工具，只需在其 <code>systemDeps</code> 中添加
Docker 字段：</span
>
</p>
<div class="code-block">
<div class="code-block__header">
<span>TypeScript — your-plugin/index.ts</span
><button class="code-block__copy">Copy</button>
</div>
<pre>systemDeps: [{
type: <span class="str">'binary'</span>,
name: <span class="str">'mytool'</span>,
envVar: <span class="str">'MYTOOL_PATH'</span>,
dockerDefault: <span class="str">'/usr/bin/mytool'</span>,
<span class="comment">// Add these for Docker:</span>
<strong>dockerFeature: <span class="str">'mytool'</span></strong>,
<strong>aptPackages: [<span class="str">'mytool'</span>]</strong>,
<strong>dockerValidation: [<span class="str">'mytool --version'</span>]</strong>,
}]</pre>
</div>
<p>
<span data-lang-en
>Then add a corresponding <code># @if mytool</code> block in
<code>docker/Dockerfile.template</code> if it needs a dedicated build stage. For
simple apt packages, just the <code>aptPackages</code> field is enough — they are
automatically merged into the runtime stage.</span
>
<span data-lang-zh
>然后如果需要独立构建阶段，在 <code>docker/Dockerfile.template</code> 中添加对应的
<code># @if mytool</code> 块。对于简单的 apt 包，只需
<code>aptPackages</code> 字段即可 — 它们会自动合并到运行时阶段。</span
>
</p>
</section>
<!-- Services Overview -->
<section id="services" class="reveal">
<h2><span data-lang-en>Services Overview</span><span data-lang-zh>服务概览</span></h2>
<p>
<span data-lang-en
>The analyzer container runs the control-plane services. When you configure a Windows
Runtime Node or Host Agent, real sample execution is delegated over HTTP instead of
happening inside the Linux analyzer container.</span
>
<span data-lang-zh
>analyzer 容器运行控制面服务。配置 Windows Runtime Node 或 Host Agent
后，真实样本执行会通过 HTTP 委派出去，而不是在 Linux analyzer 容器内进行。</span
>
</p>
<table class="doc-table">
<thead>
<tr>
<th><span data-lang-en>Service</span><span data-lang-zh>服务</span></th>
<th><span data-lang-en>Protocol</span><span data-lang-zh>协议</span></th>
<th><span data-lang-en>Description</span><span data-lang-zh>描述</span></th>
</tr>
</thead>
<tbody>
<tr>
<td><span class="badge badge--blue">MCP Server</span></td>
<td>stdio</td>
<td>
<span data-lang-en>243 tools, 3 prompts, 16 resources</span
><span data-lang-zh>243 个工具、3 个提示、16 个资源</span>
</td>
</tr>
<tr>
<td><span class="badge badge--green">HTTP API</span></td>
<td>:18080</td>
<td>
<span data-lang-en>Sample upload, artifact download, health/readiness, SSE</span
><span data-lang-zh>样本上传、产物下载、健康/就绪检查、SSE 推送</span>
</td>
</tr>
<tr>
<td><span class="badge badge--purple">Dashboard</span></td>
<td>:18080/dashboard</td>
<td>
<span data-lang-en>Dark-theme web UI — tools, plugins, samples, config</span
><span data-lang-zh>暗色主题看板 — 工具、插件、样本、配置</span>
</td>
</tr>
<tr>
<td><span class="badge badge--amber">Task Runner</span></td>
<td><span data-lang-en>Internal</span><span data-lang-zh>内部</span></td>
<td>
<span data-lang-en>Background async job queue with Python worker pool</span
><span data-lang-zh>后台异步任务队列与 Python 工作进程池</span>
</td>
</tr>
</tbody>
</table>
<p>
<span data-lang-en
><code>/api/v1/health</code> is a liveness check. <code>/api/v1/ready</code> is
profile-aware readiness and checks only dependencies required by enabled plugins.</span
><span data-lang-zh
><code>/api/v1/health</code> 是进程存活检查；<code>/api/v1/ready</code> 是
profile-aware readiness，只检查当前启用插件真正需要的依赖。</span
>
</p>
</section>
<!-- Production Mode -->
<section id="production" class="reveal">
<h2><span data-lang-en>Production Mode</span><span data-lang-zh>生产模式</span></h2>
<div class="code-block">
<div class="code-block__header">
<span>Terminal</span><button class="code-block__copy">Copy</button>
</div>
<pre><span class="comment"># Recommended production default</span>
.\rikune.ps1 install -Profile static -DataRoot "D:\Docker\rikune"
<span class="comment"># Single Windows host hybrid</span>
.\rikune.ps1 install -Profile hybrid -InstallRuntime
<span class="comment"># Hybrid with Hyper-V VM runtime backend</span>
.\rikune.ps1 install -Profile hybrid -InstallRuntime `
-RuntimeBackend hyperv-vm `
-HyperVVmName "rikune-runtime" `
-HyperVSnapshotName "clean-runtime" `
-HyperVRuntimeEndpoint "http://192.168.1.50:18081" `
-HyperVRestoreOnRelease
<span class="comment"># Linux analyzer + remote Windows Host Agent</span>
./rikune.sh install --profile hybrid --windows-host <windows-host> --windows-user <windows-user></pre
>
</div>
</section>
<!-- Docker Compose -->
<section id="compose" class="reveal">
<h2>Docker Compose</h2>
<div class="code-block">
<div class="code-block__header">
<span>bash</span><button class="code-block__copy">Copy</button>
</div>
<pre><span class="comment"># Generate all profile files</span>
npm run docker:generate:all
<span class="comment"># Static-only Linux analyzer (safe default)</span>
docker compose --env-file .docker-runtime.env -f docker-compose.analyzer.yml up -d --build analyzer
<span class="comment"># Hybrid Linux analyzer + Windows runtime plane</span>
docker compose --env-file .docker-runtime.env -f docker-compose.hybrid.yml up -d --build analyzer
<span class="comment"># Full Linux toolchain image</span>
docker compose --env-file .docker-runtime.env -f docker-compose.yml up -d --build mcp-server
<span class="comment"># View analyzer logs</span>
docker compose --env-file .docker-runtime.env -f docker-compose.hybrid.yml logs -f analyzer
<span class="comment"># Stop the selected stack</span>
docker compose --env-file .docker-runtime.env -f docker-compose.hybrid.yml down</pre>
</div>
<blockquote>
<span data-lang-en
><strong>Note:</strong> <code>static</code> and <code>hybrid</code> use the analyzer
Dockerfile and exclude local dynamic execution dependencies. Use
<code>full</code> only when you intentionally want the heavier all-in-one Linux
toolchain image.</span
>
<span data-lang-zh
><strong>注意：</strong><code>static</code> 和 <code>hybrid</code> 使用 analyzer
Dockerfile，不在本地安装动态执行依赖。只有在明确需要更重的全量 Linux 工具链时才使用
<code>full</code>。</span
>
</blockquote>
</section>
<!-- MCP Client -->
<section id="mcp-client" class="reveal">
<h2>
<span data-lang-en>MCP Client Configuration</span
><span data-lang-zh>MCP 客户端配置</span>
</h2>
<p>
<span data-lang-en
>Docker profiles run a long-lived analyzer container for the dashboard and HTTP API.
MCP clients should start a separate stdio process inside that running container with
<code>docker exec -i</code>.</span
>
<span data-lang-zh
>Docker profile 会先运行一个长期存在的 analyzer 容器，用于 Dashboard 和 HTTP API。
MCP 客户端应通过 <code>docker exec -i</code> 在这个已运行容器中启动独立的 stdio
进程。</span
>
</p>
<h3>
<span data-lang-en>JSON clients</span><span data-lang-zh>JSON 客户端</span>
</h3>
<div class="code-block">
<div class="code-block__header">
<span>mcp.json</span><button class="code-block__copy">Copy</button>
</div>
<pre>{
<span class="str">"mcpServers"</span>: {
<span class="str">"rikune"</span>: {
<span class="str">"command"</span>: <span class="str">"docker"</span>,
<span class="str">"args"</span>: [
<span class="str">"exec"</span>,
<span class="str">"-i"</span>,
<span class="str">"-e"</span>, <span class="str">"API_ENABLED=false"</span>,
<span class="str">"-e"</span>, <span class="str">"NODE_ENV=production"</span>,
<span class="str">"-e"</span>, <span class="str">"PYTHONUNBUFFERED=1"</span>,
<span class="str">"rikune-analyzer"</span>,
<span class="str">"node"</span>,
<span class="str">"dist/index.js"</span>
],
<span class="str">"env"</span>: {
<span class="str">"NODE_ENV"</span>: <span class="str">"production"</span>,
<span class="str">"PYTHONUNBUFFERED"</span>: <span class="str">"1"</span>
},
<span class="str">"timeout"</span>: 300000
}
}
}</pre>
</div>
<h3>Codex</h3>
<div class="code-block">
<div class="code-block__header">
<span>%USERPROFILE%\.codex\config.toml</span
><button class="code-block__copy">Copy</button>
</div>
<pre>[mcp_servers.rikune]
command = "docker"
startup_timeout_sec = 180
args = [
"exec",
"-i",
"-e", "API_ENABLED=false",
"-e", "NODE_ENV=production",
"-e", "PYTHONUNBUFFERED=1",
"rikune-analyzer",
"node",
"dist/index.js"
]
[mcp_servers.rikune.env]
NODE_ENV = "production"
PYTHONUNBUFFERED = "1"</pre>
</div>
<blockquote>
<span data-lang-en
><code>API_ENABLED=false</code> only applies to the short-lived MCP stdio child
process. The already running analyzer container still serves the dashboard at
<code>http://localhost:18080/dashboard</code>. For the <code>full</code> profile,
change the container name from <code>rikune-analyzer</code> to <code>rikune</code>.
Keep <code>startup_timeout_sec</code> at 180 or higher because the Docker MCP child
loads the plugin graph on startup.</span
>
<span data-lang-zh
><code>API_ENABLED=false</code> 只作用于短生命周期的 MCP stdio 子进程。已经运行的
analyzer 容器仍然会在 <code>http://localhost:18080/dashboard</code> 提供 Dashboard。
如果使用 <code>full</code> profile，请把容器名从 <code>rikune-analyzer</code> 改为
<code>rikune</code>。请将 <code>startup_timeout_sec</code> 保持在 180 或更高，因为
Docker MCP 子进程启动时需要加载插件图。</span