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資安漏洞獵人

搜尋可被利用且值得申請漏洞獎金的資安問題,專注於可遠端觸發的漏洞,而非擾人的本地端問題。適用於尋找可供回報的遠端可觸發漏洞。

資安漏洞獵人 是什麼?

這是專為負責任揭露或漏洞獎金申請而設計的高效資安技能,目標是找出真正「可付費」的漏洞,而非進行廣泛的最佳實務檢討。它會優先掃描可遠端觸發、用戶可控的攻擊路徑,並自動剔除獎金平台常認為「資訊性質」或「超出範圍」的發現。此技能支援 Huntr、HackerOne 等主流獎金平台,並適用於任何需要評估漏洞實際可回報性的程式庫。其工作原理是透過結構化工作流程:先確認專案範圍(如 SECURITY.md 文件與計畫規則),再尋找真實入口點(如 HTTP 處理器、上傳功能、Webhook),接著結合靜態工具與手動程式碼審查,最後以最小安全概念驗證(PoC)來證明漏洞的可利用性與影響。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

資安漏洞獵人 是做什麼的?

當目標是進行負責任揭露或漏洞獎金提交的實用漏洞發現時,請使用此技能,而非進行廣泛的最佳實務審查。

使用時機

  • 掃描程式庫以尋找可被利用的漏洞
  • 準備提交至 Huntr、HackerOne 或類似平台的漏洞獎金
  • 進行分類判斷,重點是「這個漏洞真的能換到錢嗎?」而非「理論上是否不安全?」

運作方式

偏向尋找可遠端觸發、用戶可控的攻擊路徑,並剔除獎金平台常因其為資訊性質或超出範圍而拒絕的發現。

適用的漏洞類型

以下類型的問題通常具有重要影響:

模式CWE典型影響
用戶可控 URL 的 SSRFCWE-918內部網路存取、雲端元資料竊取
中介軟體或 API 防護的認證繞過CWE-287未經授權的帳戶或資料存取
遠端反序列化或上傳至 RCE 路徑CWE-502程式碼執行
可觸達端點的 SQL 注入CWE-89資料外洩、認證繞過、資料破壞
請求處理器中的命令注入CWE-78程式碼執行
檔案服務路徑的路徑遍歷CWE-22任意檔案讀取或寫入
自動觸發的 XSSCWE-79工作階段竊取、管理員帳號淪陷

應跳過的項目

除非計畫另有說明,否則以下項目通常屬於低訊號或超出漏洞獎金範圍:

  • 僅限本機使用的 pickle.loads、torch.load 或類似操作(無遠端路徑)
  • CLI 工具中的 eval() 或 exec()
  • 完全硬編碼命令上的 shell=True
  • 僅缺少資安標頭
  • 無利用影響的通用速率限制抱怨
  • 需要受害者手動貼上程式碼的自我 XSS
  • 非目標計畫範圍內的 CI/CD 注入
  • 示範、範例或僅供測試的程式碼

工作流程

  1. 首先確認範圍:計畫規則、SECURITY.md、揭露管道與排除項目。
  2. 尋找真實入口點:HTTP 處理器、上傳功能、背景任務、Webhook、解析器與整合端點。
  3. 在協助下執行靜態工具,但僅將其視為分類輸入。
  4. 從頭到尾閱讀實際的程式碼路徑。
  5. 證明用戶控制可到達有意義的接收器。
  6. 以最小安全的概念驗證(PoC)確認可利用性與影響。
  7. 在撰寫報告前檢查是否有重複發現。

範例分類迴圈

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

然後手動過濾:

  • 刪除測試、示範、固定程式碼、廠商程式碼
  • 刪除僅限本機或無法觸達的路徑
  • 僅保留具有明確網路或用戶可控路由的發現

報告結構

## 描述
[漏洞是什麼及其重要性]

## 弱點程式碼
[檔案路徑、行號範圍與小段程式碼]

## 概念驗證
[最小的有效請求或腳本]

## 影響
[攻擊者可達到的結果]

## 受影響版本
[測試過的版本、提交或部署目標]

品質關卡

提交前請確認:

  • 程式碼路徑可從真實用戶或網路邊界觸達
  • 輸入確實由用戶控制
  • 接收器有意義且可利用
  • PoC 可運作
  • 問題尚未被公告、CVE 或公開問題單涵蓋
  • 目標確實屬於漏洞獎金計畫的範圍

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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