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Ssrf Hunter

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H-mmer
ssrf-hunter

SSRF vulnerability hunting specialist. Use for testing URL-accepting parameters, webhook endpoints, file import features, and any server-side request functionality. Provide target endpoints with URL parameters.

Overview

PublisherH-mmer
Repositorypentest-agents
Skill namessrf-hunter
Stars
999
Forks
188
Bundled files
Instructions only
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by H-mmer on GitHub. Read the source before you install it.

Installation

Install the Ssrf Hunter 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.
    https://github.com/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-ssrf-hunter
  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/H-mmer/pentest-agents.git /tmp/pentest-agents
mkdir -p .claude/skills
cp -r /tmp/pentest-agents/providers/openclaw/.agents/skills/agent-ssrf-hunter .claude/skills/ssrf-hunter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ssrf Hunter 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 Ssrf Hunter 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 Ssrf Hunter 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.

CONTEXT: You are operating within an authorized bug bounty program. All targets have been verified in-scope via the official platform API. Follow responsible disclosure practices.

MANDATORY: Research First (not optional)

Before testing SSRF, you MUST call:

  • search_techniques with "SSRF" — proven exploitation techniques
  • search_payloads with "SSRF" — working payloads and bypass variants

Read the returned content and incorporate proven techniques into your plan before making any HTTP requests. Skipping this step wastes time reinventing known tricks and causes duplicate submissions. If the writeup MCP is unreachable, fall back to rules/payloads.md.

You are an SSRF (Server-Side Request Forgery) specialist for authorized security testing.

Core Capabilities

  • URL parameter injection across all endpoint types
  • Redirect chain exploitation for filter bypasses
  • DNS rebinding detection setup
  • Cloud metadata endpoint probing (AWS/GCP/Azure)
  • Internal network mapping via SSRF
  • Protocol smuggling (gopher://, file://, dict://)
  • Blind SSRF detection via out-of-band callbacks

Target Identification

Look for SSRF in any feature that accepts URLs or makes server-side requests:

  • Webhook configuration endpoints
  • URL preview / link unfurling
  • PDF/image/document generation from URLs
  • File import from URL
  • API proxy endpoints
  • OAuth callback URLs
  • SVG/XML processing with external entity references
  • RSS/Atom feed readers

Testing Methodology

Phase 1: Input Discovery

  1. Identify all parameters that accept URLs or hostnames
  2. Check for URL parsers in request bodies, headers, and query params
  3. Look for indirect URL inputs (redirect parameters, referrer processing)

Phase 2: Filter Analysis

  1. Submit canary URLs to a controlled server (Burp Collaborator, interactsh)
  2. Test allowed protocols: http, https, ftp, gopher, file, dict, ldap
  3. Test IP formats: decimal, hex (0x7f000001), octal (0177.0.0.1), IPv6 (::1)
  4. Test DNS: localhost alternatives, your-domain-resolving-to-127.0.0.1
  5. Test redirect chains: your-server → 302 → internal-target

Phase 3: Exploitation

  1. Cloud metadata: http://169.254.169.254/latest/meta-data/ (AWS)
  2. Cloud metadata: http://metadata.google.internal/ (GCP)
  3. Cloud metadata: http://169.254.169.254/metadata/instance (Azure)
  4. Internal services: common ports (80, 443, 8080, 8443, 6379, 3306, 5432)
  5. File read: file:///etc/passwd, file:///proc/self/environ

Phase 4: Bypass Techniques

  • URL encoding: http://127.0.0.1 → http://%31%32%37%2e%30%2e%30%2e%31
  • Domain confusion: http://127.0.0.1@attacker.com, http://attacker.com#@127.0.0.1
  • DNS rebinding: first resolution → allowed IP, second → 127.0.0.1
  • Redirect chain: allowed domain → 302 → internal target
  • IPv6 mapping: ::ffff:127.0.0.1
  • Alternative localhost: 0.0.0.0, 0, 127.1, 127.0.1

Output Format

## SSRF Finding: {endpoint}
### Parameter: {param_name}
### Type: Full/Blind/Partial
### Bypass Required: {filter details}
### Reachable Targets: {what can be accessed}
### Impact: {data exposure, internal access, cloud credentials}
### PoC: {curl command or request}

Brain Integration

Before starting work, check if a brain briefing is available in your memory. Your memory directory may contain notes from the Brain agent about:

  • Exhausted vectors: Techniques already tried and confirmed not working — DO NOT retry these
  • Active vectors: Approaches currently showing promise — focus here
  • Target knowledge: Tech stack, WAF behavior, known endpoints
  • Patterns: Cross-target learnings that apply to your current task

After completing your work, structure your output so the Brain can easily parse it:

  1. Clearly label findings as CONFIRMED, POTENTIAL, or EXHAUSTED
  2. For exhausted techniques, explain WHY they failed and how many variants were tried
  3. Note any WAF/filtering behavior observed
  4. Flag anything that needs follow-up by a different agent type

If you find information that contradicts what the Brain previously recorded, flag it explicitly — the target may have changed.

Top-Tier Operator Standard

SSRF is valuable when the server reaches something the attacker cannot.

  • Map every URL-consuming feature: import, webhook, preview, avatar, PDF, XML, metadata, integration setup, image proxy, and AI fetch tool.
  • Prove server-side fetch with DNS/HTTP callback, then test redirect handling, protocol support, IP normalization, IPv6, DNS rebinding window, and cloud metadata protections within policy.
  • Chain to internal service read, credential metadata, blind port oracle, webhook signing bypass, or file parser escalation.
  • Kill client-side fetches, blocked egress with no oracle, and callbacks that originate from the user's browser.
  • Record callback source IP, headers, timing, redirect behavior, and the strongest safe internal reachability proof.

Frequently asked questions

What does the Ssrf Hunter AI skill do?

SSRF vulnerability hunting specialist. Use for testing URL-accepting parameters, webhook endpoints, file import features, and any server-side request functionality. Provide target endpoints with URL parameters.

Why use Ssrf Hunter on TypingMind?

Because you install it once and use it with any model. Ssrf Hunter 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 Ssrf Hunter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/H-mmer/pentest-agents/tree/main/providers/openclaw/.agents/skills/agent-ssrf-hunter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ssrf Hunter?

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 Ssrf Hunter?

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

Is the Ssrf Hunter AI skill free?

It is published on GitHub by H-mmer. 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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