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Java Audit Pipeline

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wgpsec
java-audit-pipeline

Java 白盒源码安全审计总方法论。当需要对 Java 项目进行完整的源码安全审计、 或需要系统化的白盒漏洞挖掘流程时触发。 覆盖 5 阶段审计流水线: 路由映射→权限建模→数据流追踪→分类漏洞审计→利用链组装。 核心机制: 证据合约系统(EVID_*)防止 AI 幻觉误报，所有漏洞结论必须有数据流证据支撑。

Overview

Publisherwgpsec
RepositoryAboutSecurity
Skill namejava-audit-pipeline
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1.7K
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Bundled files
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Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by wgpsec on GitHub. Read the source before you install it.

Installation

Install the Java Audit Pipeline AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/wgpsec/AboutSecurity.git /tmp/AboutSecurity
mkdir -p .claude/skills
cp -r /tmp/AboutSecurity/skills/code-audit/java/java-audit-pipeline .claude/skills/java-audit-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Java Audit Pipeline in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Java Audit Pipeline on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Java Audit Pipeline is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Java 白盒审计总方法论

白盒审计在源码层面发现漏洞，关注"代码为什么不安全"。发现漏洞后的实际利用技术（构造 payload、绕过 WAF、Gadget Chain 武器化）属于黑盒 exploit skill 范畴。Java 项目常以编译后的 .class / JAR / WAR 形式交付，需先完成反编译还原源码（详见 decompile-strategy.md）。

深入参考

审计 5 阶段概览

阶段名称核心任务产出物
P1路由映射解析所有入口点及其参数路由清单
P2权限建模分析认证/授权，标记裸露路由权限矩阵
P3数据流追踪Source→Sink 完整路径追踪EVID_* 证据集
P4分类审计按 Sink 类型深入检查漏洞清单
P5报告组装评分 + 利用链编排审计报告

Phase 1: 路由映射与入口点识别

解析框架路由配置，建立完整的攻击面清单:

  • Spring MVC: @RequestMapping, @GetMapping, @PostMapping 等组合注解，扫描所有 @Controller / @RestController 类
  • Servlet: web.xml 中的 <servlet-mapping> 以及 @WebServlet 注解注册的 Servlet
  • JAX-RS: @Path, @GET, @POST 等注解，检查 Application 子类注册的资源
  • Struts2: struts.xml 中的 <action> 映射和 ActionMapping 通配符规则
  • WebService: CXF / Axis 的 @WebService 端点和 WSDL 发布路径

产出: 路由清单，每条记录包含 URL 路径、Handler 方法签名、参数绑定方式（@RequestParam / @PathVariable / @RequestBody）、是否需认证。

Phase 2: 权限建模与认证审查

分析安全框架的过滤链配置，找出缺少认证保护的路由:

  • Spring Security: SecurityFilterChain / WebSecurityConfigurerAdapter 中的 antMatchers / requestMatchers 规则
  • Apache Shiro: shiroFilterChainDefinition 中的 URL-Filter 映射（anon / authc / perms）
  • 自定义拦截器: HandlerInterceptor.preHandle() 的注册范围和排除路径
  • 检查 JWT / Session / OAuth2 Token 的签发与校验逻辑、密码存储方式（BCrypt / 明文）

Phase 3: 数据流追踪（Source → Sink）

每条潜在漏洞路径都要产出 EVID_* 证据点（详见 evidence-contract.md）。

三层分析法:

  1. 面 — 全局关键字扫描: 搜索 Sink 函数（参考 sink-reference.md），快速定位危险代码区域
  2. 线 — 逐行追踪变量流: 从 Sink 反向追溯到 Source，记录每一步的变量传递和过滤操作
  3. 点 — 验证利用条件: 确认过滤是否可绕过、参数是否可控、执行路径是否可达

Java 特有关注点:

  • 注解驱动的参数绑定（@RequestParam 自动 trim、@RequestBody JSON 反序列化）隐式转换可能吞掉恶意输入或引入类型混淆
  • AOP 切面（@Around / @Before）可能在切面层执行全局过滤或日志记录，需确认切面是否生效
  • 反射调用（Method.invoke、Class.forName）会打断静态数据流追踪，需人工跟进
  • 多态分派 — 接口/抽象类的实际实现类需逐一排查，不能仅看接口声明

当无法追踪到完整的 Source→Sink 路径时，只能标注为"待验证"。

Phase 4: 分类漏洞审计

按 Sink 类型分派到对应子 skill 进行深入审计:

  • 注入类（SQL / CMD / LDAP / SpEL / OGNL / EL）
  • 文件类（读取 / 上传 / 写入 / 路径穿越）
  • 前端类（XSS / CSRF / 重定向）
  • 序列化类（Java 原生反序列化 / FastJSON / Jackson / XXE）
  • 认证配置类（越权 / 弱加密 / 信息泄露 / Actuator 暴露）
  • 框架特定漏洞（已知 CVE、Spring / Struts2 / Shiro 配置缺陷）

Phase 5: 报告与利用链组装

严重度评分: Score = R * 0.40 + I * 0.35 + C * 0.25（R=可达性, I=影响范围, C=利用复杂度，各 0-3 分）

将同一目标上的多个漏洞组合为利用链（如: Actuator 信息泄露→Shiro 认证绕过→SpEL 注入→RCE）。

审计质量检查清单

  • 所有公开路由均已纳入路由清单（含 Servlet / Filter 注册的隐式入口）
  • 未认证路由已全部标记并优先审计
  • 每个"已确认"漏洞都有完整的 EVID_* 证据链
  • AOP 切面和全局 Filter 的实际覆盖范围已验证
  • 反射调用和动态代理的数据流已人工跟进
  • 框架版本及依赖组件版本已确认，已知 CVE 已交叉比对
  • 漏洞评分使用了统一公式，等级划分一致
  • 利用链可行性已在源码层面验证

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Java Audit Pipeline AI skill do?

Java 白盒源码安全审计总方法论。当需要对 Java 项目进行完整的源码安全审计、 或需要系统化的白盒漏洞挖掘流程时触发。 覆盖 5 阶段审计流水线: 路由映射→权限建模→数据流追踪→分类漏洞审计→利用链组装。 核心机制: 证据合约系统(EVID_*)防止 AI 幻觉误报，所有漏洞结论必须有数据流证据支撑。

Why use Java Audit Pipeline on TypingMind?

Because you install it once and use it with any model. Java Audit Pipeline is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Java Audit Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wgpsec/AboutSecurity/tree/master/skills/code-audit/java/java-audit-pipeline. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Java Audit Pipeline?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Java Audit Pipeline?

As many as you like. As long as a model supports skills, you can use Java Audit Pipeline with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Java Audit Pipeline AI skill free?

It is published on GitHub by wgpsec. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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