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安全漏洞赏金猎人

寻找仓库中可利用的、符合赏金标准的安全问题。专注于远程可到达的漏洞,这些漏洞有资格获得真实报告,而非嘈杂的本地问题。用于在仓库中搜寻可报告的、远程可到达的漏洞。

安全漏洞赏金猎人 是什么?

该技能旨在帮助开发者和安全研究人员在代码仓库中高效发现实际可利用的安全漏洞,以用于负责任披露或漏洞赏金提交。它专注于远程可到达、用户可控的攻击路径,并自动过滤掉那些赏金平台通常会标记为“仅供参考”或“超出范围”的模式。支持平台包括 Huntr、HackerOne 等主流漏洞赏金计划。技能内部定义了一组高影响力的漏洞模式(如 SSRF、认证绕过、远程反序列化、SQL 注入、命令注入、路径遍历和自动触发 XSS),并提供了从范围检查、入口点发现、静态工具辅助到最终报告撰写的完整工作流程,确保发现的每个漏洞都有实际价值。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/security-bounty-hunter

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

安全漏洞赏金猎人 是做什么的?

当目标是实际漏洞发现用于负责任披露或漏洞赏金提交时使用,而非进行广泛的最佳实践审查。

使用场景

  • 扫描仓库以查找可利用的漏洞
  • 准备 Huntr、HackerOne 或类似漏洞赏金提交
  • 判定“这实际上有价值吗?”而非“这在理论上是否不安全?”的紧急程度

工作原理

倾向于远程可到达的、用户可控的攻击路径,并丢弃那些平台通常会标记为“仅供参考”或“超出范围”的模式。

在作用域内的模式

以下是持续重要的几种问题类型:

模式CWE典型影响
通过用户可控 URL 的 SSRFCWE-918内部网络访问、云元数据窃取
中间件或 API 防护中对认证的绕过CWE-287未经授权的账户或数据访问
远程反序列化或上传到 RCE 路径CWE-502代码执行
在可达端点中的 SQL 注入CWE-89数据窃取、认证绕过、数据破坏
在请求处理程序中的命令注入CWE-78代码执行
在文件服务路径中的路径遍历CWE-22任意文件读取或写入
自动触发的 XSSCWE-79会话窃取、管理员权限滥用

跳过这些

以下通常是低信号或超出赏金范围的,除非项目另有说明:

  • 仅本地的 pickle.loads、torch.load 或类似功能,且无远程路径
  • CLI 工具中的 eval() 或 exec()
  • 完全硬编码命令上的 shell=True
  • 仅凭缺失安全头
  • 没有利用影响的通用速率限制投诉
  • 需要受害者手动粘贴代码的自 XSS
  • 不在目标项目范围内的 CI/CD 注入
  • 演示、示例或仅测试代码

工作流程

  1. 首先检查范围:项目规则、SECURITY.md、披露渠道和排除项。
  2. 找到真实入口点:HTTP 处理程序、上传、后台任务、Webhook、解析器和集成端点。
  3. 在有用时运行静态工具,但仅将其视为分类输入。
  4. 从头到尾阅读实际代码路径。
  5. 证明用户控制能够到达一个有意义的接收点(sink)。
  6. 用最小的安全 PoC 确认可利用性和影响。
  7. 在起草报告前检查是否有重复。

示例分类循环

semgrep --config=auto --severity=ERROR --severity=WARNING --json

然后手动过滤:

  • 丢弃测试、演示、测试夹具、供应商代码
  • 丢弃仅本地或不可到达的路径
  • 仅保留具有明确网络或用户控制途径的发现

报告结构

## 描述
[漏洞是什么及其重要性]

## 易受攻击的代码
[文件路径、行范围以及一个小片段]

## 概念验证
[最小工作请求或脚本]

## 影响
[攻击者可以实现什么]

## 受影响版本
[测试的版本、提交或部署目标]

质量关卡

在提交之前:

  • 代码路径从真实用户或网络边界可达
  • 输入是真正用户可控的
  • 接收点(sink)有意义且可利用
  • PoC 有效
  • 该问题尚未被任何公告、CVE 或开放工单覆盖
  • 目标实际上在赏金计划的范围内

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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