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Hunt Http Smuggling

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elementalsouls
hunt-http-smuggling

Hunt HTTP request smuggling (CL.TE, TE.CL, H2.CL, H2.TE). Cause: front-end proxy and back-end server disagree on where one request ends and the next begins (Content-Length vs Transfer-Encoding header parsing inconsistency). CL.TE: front-end uses CL, back uses TE → smuggle by sending TE: chunked but with body that fits CL count. TE.CL: opposite. H2.CL: HTTP/2 downgrade, smuggle CL into HTTP/1.1 back-end. Detection tools: Burp HTTP Request Smuggler extension, smuggler.py, h2csmuggler. Confirm: time-delay technique (smuggled GET with 30s timeout) — if front-end returns slow on next victim request, smuggling works. Validate: cache poisoning chain (smuggle request that gets cached for victim), credential theft (smuggle X-Forwarded-For override that captures next user's cookies), bypass auth (smuggled internal-path request). Real paid examples from major CDN deployments. Use when hunting H1 paid programs running CDN+origin stacks, when targeting load balancer / WAF bypass.

Overview

Publisherelementalsouls
RepositoryClaude-BugHunter
Skill namehunt-http-smuggling
Stars
4.5K
Forks
678
Bundled files
Instructions only
LicenseMIT
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 elementalsouls on GitHub. Read the source before you install it.

Installation

Install the Hunt Http Smuggling 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/elementalsouls/Claude-BugHunter.git /tmp/Claude-BugHunter
mkdir -p .claude/skills
cp -r /tmp/Claude-BugHunter/skills/hunt-http-smuggling .claude/skills/hunt-http-smuggling
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hunt Http Smuggling 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 Hunt Http Smuggling 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 Hunt Http Smuggling 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.

17. HTTP REQUEST SMUGGLING

Lowest dup rate. $5K–$30K. PortSwigger research by James Kettle.

CL.TE (Content-Length front, Transfer-Encoding back)

http
POST / HTTP/1.1
Content-Length: 13
Transfer-Encoding: chunked

0

SMUGGLED

Detection

1. Burp extension: HTTP Request Smuggler
2. Right-click request → Extensions → HTTP Request Smuggler → Smuggle probe
3. Manual timing: CL.TE probe + ~10s delay = backend waiting for rest of body

Impact Chain

Poison next request → access admin as victim
Steal credentials → capture victim's session
Cache poisoning → stored XSS at scale

Target-Suitability Matrix (2026 reality check)

The classic CL.TE / TE.CL payloads are NOT universally exploitable in 2026. Modern proxies are RFC 9112 strict by default. Fingerprint the front-end BEFORE investing time.

Front-endCL.TETE.CLH2.CLH2.TENotes
Nginx ≥ 1.21NONOpartial (H2 ingress)partialRFC-strict; rejects CL+TE with HTTP 400. Verified locally on Nginx 1.27 — all 9 documented variants killed by front-end (docs/verification/phase2h-smuggling-cachepoison.md).
Caddy 2.xNONOHardened by default
Envoy ≥ 1.20NONOpartialpartialHardened in most paths
HAProxy ≤ 2.4Vulnerable, see CVE-2021-40346
AWS ALB + specific upstreampartialpartialSeveral disclosed-paid reports 2022-2024
Cloudflare → S3 / Lambda chainsH2-downgrade attacks remain viable
Older F5 BIG-IP (TMM < 16)Vendor advisories
Citrix ADC / NetScaler (older firmware)Disclosed in 2020-2022
Squid 3.xOlder deployments
Apache Traffic Server (older)PortSwigger research
Apache mod_proxy_ajp → TomcatCross-protocol HTTP→AJP desync (CVE-2022-26377); smuggled request is opaque to the WAF and reaches internal AJP admin/status paths that lack the external auth controls
Custom Python / Go proxiesFrequently miss RFC enforcement

Operator fingerprint quick-check

bash
curl -sI https://target/ | grep -i "Server:"
  • nginx/1.21+, Caddy, envoy → CL/TE classic is dead — pivot to H2.CL/H2.TE if the front-end speaks HTTP/2, or look for legacy proxies upstream
  • HAProxy, header points to AWS/CDN → run the full payload matrix
  • No Server header → assume hardened, but run a single quick space-before-colon probe; if it doesn't 400, dig deeper

H2.CL / H2.TE (the modern dominant vector)

H2-downgrade smuggling attacks rely on the front-end speaking HTTP/2 to the client and HTTP/1.1 to origin. The downgrade introduces CL/TE confusion because HTTP/2's frame-length headers don't survive the conversion cleanly. Most CDN+origin chains in 2024-2026 use this exact topology.

Tools that send HTTP/2 raw frames (Burp Pro's HTTP Request Smuggler extension, h2csmuggler, smuggler.py) are the right starting point against CDN-fronted targets. Avoid HTTP/1.1-only test clients (curl, raw sockets) against H2-front-ended targets — you'll send the wrong protocol entirely.

Mass credential harvesting — the "collector gadget"

The highest-impact smuggling outcome needs no per-victim interaction. Instead of blindly poisoning the queue, smuggle a request aimed at a back-end handler that echoes the full request — a search endpoint that reflects headers, or a redirect that mirrors the request line. The next victim's headers (Cookie, Authorization, X-Access-Token) get attributed to your smuggled request, and the reflecting handler returns them in a response you read. Repeated on a busy keep-alive socket, this harvests live credentials from arbitrary users at scale — and it works even through a CDN (Akamai/Cloudflare) when the CDN↔origin hop desyncs. Chains to hunt-ato.


Related Skills & Chains

  • hunt-cache-poison — Smuggling + cache is the canonical critical chain; one smuggled request becomes the cached response for every subsequent victim. Chain primitive: CL.TE smuggle a request whose response body contains attacker HTML/JS → front-end cache stores it under a popular URL (/, /login) → de-sync poisoning where the smuggled request becomes the cached response for the next N victims, persisting for the cache TTL.
  • hunt-auth-bypass — Smuggling reaches internal-only routes that the front-end WAF/auth-proxy filters out. Chain primitive: smuggle GET /admin/users HTTP/1.1 past the front-end ACL that blocks external /admin/* → backend processes the smuggled request as if from a trusted internal source → bypass front-end auth by smuggling internal-routed request → admin data in the response queue.
  • hunt-idor — Smuggling attaches the NEXT user's session cookies to an attacker-controlled request path. Chain primitive: smuggle GET /api/me HTTP/1.1 with no cookies → backend pairs it with the next legitimate user's incoming connection cookies → victim's session cookie attached to attacker's smuggled request → attacker reads the response containing victim's PII/tokens.
  • hunt-xss — Smuggling injects XSS payloads into the response stream of the next victim without ever appearing in a URL parameter. Chain primitive: smuggled request body contains reflected payload that the backend renders into the next response in the queue → next visitor to / receives attacker HTML inline → reflected XSS at every visitor without any URL parameter visible to them or to logs.
  • security-arsenal — Reach for the smuggling payload bank (CL.TE / TE.CL / TE.TE obfuscations, H2.CL downgrade probes, h2csmuggler one-liners, Burp HTTP Request Smuggler extension config) and the time-delay confirmation template before manual hex-editing.
  • triage-validation — Run the Pre-Severity Gate before claiming Critical: the smuggled-request effect MUST land on a request issued by a different client/session, not your own follow-up. A timing delta in your own browser alone is parser disagreement, not exploitable smuggling.

Frequently asked questions

What does the Hunt Http Smuggling AI skill do?

Hunt HTTP request smuggling (CL.TE, TE.CL, H2.CL, H2.TE). Cause: front-end proxy and back-end server disagree on where one request ends and the next begins (Content-Length vs Transfer-Encoding header parsing inconsistency). CL.TE: front-end uses CL, back uses TE → smuggle by sending TE: chunked but with body that fits CL count. TE.CL: opposite. H2.CL: HTTP/2 downgrade, smuggle CL into HTTP/1.1 back-end. Detection tools: Burp HTTP Request Smuggler extension, smuggler.py, h2csmuggler. Confirm: time-delay technique (smuggled GET with 30s timeout) — if front-end returns slow on next victim requ...

Why use Hunt Http Smuggling on TypingMind?

Because you install it once and use it with any model. Hunt Http Smuggling 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 Hunt Http Smuggling in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/elementalsouls/Claude-BugHunter/tree/main/skills/hunt-http-smuggling. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hunt Http Smuggling?

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 Hunt Http Smuggling?

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

Is the Hunt Http Smuggling AI skill free?

Yes. It is published on GitHub by elementalsouls under the MIT license. 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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