Java Frontend Audit logo

Java Frontend Audit

OrganizationPopular
wgpsec
java-frontend-audit

Java 源码前端安全类漏洞审计。当在 Java 白盒审计中需要检测前端安全漏洞时触发。 覆盖 5 类风险: XSS(JSP/Thymeleaf/FreeMarker 输出转义)、CSRF(Spring Security/自定义 Token)、 开放重定向(sendRedirect/forward)、CRLF 注入(Header/Cookie)、Session 管理(固定/超时/并发)。 需要 java-audit-pipeline 提供的数据流证据。

Overview

Publisherwgpsec
RepositoryAboutSecurity
Skill namejava-frontend-audit
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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.

  • 1 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 Frontend Audit 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-frontend-audit .claude/skills/java-frontend-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Java Frontend Audit 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 Frontend Audit 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 Frontend Audit 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 前端安全类漏洞源码审计

本 skill 聚焦源码层面判断"前端安全漏洞是否成立"，核心是验证用户输入在输出到 HTTP 响应（HTML/Header/Cookie/重定向）过程中的过滤与转义是否充分。构造 payload、绕 WAF 等运行时利用技术属于对应黑盒 exploit skill 范畴。

深入参考

5 类前端安全漏洞速查表

类型典型 Sink危险模式严重度
XSSJSP <%= %>, th:utext, ${var?no_esc}, out.println()用户输入未经转义直接输出到 HTML/JS 上下文High-Medium
CSRF状态变更 POST/PUT/DELETE 接口csrf().disable() 或缺少 Token 校验的写操作端点High-Medium
开放重定向sendRedirect(), return "redirect:" + url用户可控 URL 直接传入重定向函数Medium
CRLF 注入setHeader(), addHeader(), addCookie()用户输入未过滤 \r\n 直接写入 HTTP 头Medium-High
Session 管理getSession(), Cookie 构造, session-timeout登录后未重建 Session / Cookie 缺少安全属性Medium-High

通用审计流程（4 步）

Step 1 -- 确认 EVID 证据点: 从 java-audit-pipeline Phase 3 产出的 EVID_* 证据中，筛选前端安全类条目（EVID_XSS_、EVID_CSRF_、EVID_REDIR_、EVID_CRLF_、EVID_SESSION_*）。没有 EVID 证据的 Sink 只能标"待验证"。

Step 2 -- 判断过滤有效性: 追踪 Source->Sink 路径上每一步过滤/转义操作，评估其对当前漏洞类型是否有效。常见陷阱: <c:out> 安全但 <%= %> 不转义、th:text 安全但 th:utext 不转义、URL 白名单可被 //evil.com 绕过。

Step 3 -- 评估绕过可能性: 过滤存在但不充分时，分析具体绕过路径（上下文切换、编码差异、URL 解析差异等）。能给出绕过思路则标"已确认"，否则标"待验证"并记录已知过滤方式。

Step 4 -- 确定严重度: 使用 java-audit-pipeline 的三维度评分公式 Score = R*0.40 + I*0.35 + C*0.25。XSS 存储型 I=2-3，反射型 I=1-2；CSRF 取决于被保护操作的影响。

XSS 审计要点

  • JSP 输出: <%= request.getParameter("x") %> 和 <% out.println(input); %> 均不转义，直接输出到 HTML 即反射型 XSS；安全写法: <c:out value="${param.x}"/> 或 ${fn:escapeXml(param.x)}，JSTL 默认转义 HTML 实体
  • Thymeleaf: th:utext="${userInput}" 不转义（原始 HTML 输出）——危险；th:text="${userInput}" 自动 HTML 转义——安全
  • FreeMarker: 全局配置 output_format=HTMLOutputFormat + auto_escaping_policy=ENABLE_IF_SUPPORTED 后默认转义；${var?no_esc} 显式跳过转义——危险；未配置全局转义时所有 ${} 均危险
  • 上下文差异: HTML body 转义 < > & " ' 即可；HTML attribute 需额外处理引号闭合；JavaScript 上下文需 JS 编码而非 HTML 转义；URL 参数需 URL 编码——不同上下文使用错误的转义函数等于无防御
  • DOM-based XSS: 后端返回 JSON 被前端 innerHTML / document.write() / eval() 渲染，后端审计需确认 JSON 响应的 Content-Type 是否为 application/json 而非 text/html
  • 存储型 XSS: 数据从数据库取出后未转义直接输出——追踪入库点是否做了过滤，出库点是否做了转义
  • 富文本场景: 需使用白名单过滤库如 OWASP Java HTML Sanitizer (PolicyFactory) 或 Jsoup.clean(html, Safelist.basic())

CSRF 审计要点

  • Spring Security 默认: CSRF Token 默认开启，但 REST API 项目常见 .csrf().disable()（旧版）或 http.csrf(csrf -> csrf.disable())（Lambda DSL）——全局关闭后所有状态变更端点均暴露
  • 自定义 Token: 评估生成强度（SecureRandom 安全 vs Math.random() 可预测）、是否绑定 Session、校验时机（Controller 层 vs Filter 层）
  • Token 传递: 表单用 <input type="hidden" name="_csrf" value="..."/>；AJAX 用 X-CSRF-TOKEN 请求头配合 <meta name="_csrf"> 读取——缺失任一传递方式则该场景无防护
  • CORS 与 CSRF 的关系: 宽松 CORS（Access-Control-Allow-Origin: * + Allow-Credentials: true）可被跨域 JS 读取响应中的 CSRF Token，间接绕过 CSRF 防护
  • GET 请求状态变更: GET 执行写操作（如 /deleteUser?id=1）天然无 CSRF 防护——<img src=""> 即可触发

开放重定向审计要点

  • Servlet 直接重定向: response.sendRedirect(request.getParameter("url")) / response.sendRedirect(request.getParameter("next")) ——用户完全可控
  • Spring MVC: return "redirect:" + target 或 RedirectView(url) 中 target 来自请求参数
  • 绕过手法: //evil.com（协议相对 URL）、/\evil.com（部分解析器视 \ 为路径分隔符）、URL 编码 %2F%2Fevil.com、@ 符号 http://trusted.com@evil.com、unicode 同形异义字
  • 安全模式: 白名单域名校验 + 限制仅相对路径（以 / 开头且不以 // 开头）；使用 UriComponentsBuilder 解析后校验 host

CRLF 注入审计要点

  • HTTP Header 注入: response.setHeader("X-Custom", userInput) 或 response.addHeader("Location", "/path?lang=" + input) ——注入 \r\nSet-Cookie: admin=1 可设置任意头
  • Servlet 容器差异: Tomcat 7.0.67+ / Jetty 9.2.15+ 默认拒绝 Header 值中的 \r\n；旧版本或自定义 HTTP 框架（如 Netty 原始 API）可能无此保护——确认容器版本是关键
  • Cookie 值注入: new Cookie("lang", userInput) ——注入 \r\nSet-Cookie: session=evil 可劫持 Session
  • 安全模式: 过滤或拒绝值中的 \r、\n、\0 控制字符；升级到较新版本 Servlet 容器

Session 管理审计要点

  • Session 固定: 登录前后 Session ID 不变——攻击者诱导受害者使用已知 Session ID；安全模式: 登录成功后 request.getSession().invalidate() + request.getSession(true) 重建；Spring Security SessionFixationProtection.migrateSession 默认启用
  • Cookie 属性: HttpOnly 防 XSS 窃取 Cookie（cookie.setHttpOnly(true)）；Secure 限制仅 HTTPS 传输（cookie.setSecure(true)）；SameSite=Lax/Strict 防 CSRF（Servlet 4.0+ 或 Spring server.servlet.session.cookie.same-site）
  • 超时配置: web.xml 中 <session-timeout>30</session-timeout>（分钟）；Spring Boot server.servlet.session.timeout=30m；过长超时增加 Session 劫持窗口
  • 并发登录控制: Spring Security sessionManagement().maximumSessions(1).maxSessionsPreventsLogin(true) ——未配置时同一账户可多点登录，被盗凭据难以发现

检测清单

  • 所有前端安全类 EVID_* 证据点已逐一审查
  • JSP <%= %> / out.println() 输出点均已确认是否转义，Thymeleaf th:utext 已全部标记
  • CSRF 保护状态已确认（是否 disable、Token 传递方式、GET 写操作）
  • 重定向 Sink 的 URL 来源和白名单校验已确认
  • HTTP Header/Cookie 写入点的用户输入过滤已检查，容器版本已记录
  • Session 生命周期（固定/超时/并发/Cookie 属性）已审查
  • 过滤不充分的点已给出具体绕过思路或标"待验证"
  • 严重度评分使用了统一公式，与 pipeline 一致

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 Frontend Audit AI skill do?

Java 源码前端安全类漏洞审计。当在 Java 白盒审计中需要检测前端安全漏洞时触发。 覆盖 5 类风险: XSS(JSP/Thymeleaf/FreeMarker 输出转义)、CSRF(Spring Security/自定义 Token)、 开放重定向(sendRedirect/forward)、CRLF 注入(Header/Cookie)、Session 管理(固定/超时/并发)。 需要 java-audit-pipeline 提供的数据流证据。

Why use Java Frontend Audit on TypingMind?

Because you install it once and use it with any model. Java Frontend Audit 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 Frontend Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wgpsec/AboutSecurity/tree/master/skills/code-audit/java/java-frontend-audit. 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 Frontend Audit?

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 Frontend Audit?

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

Is the Java Frontend Audit 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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