Tls Fingerprint Impersonation logo

Tls Fingerprint Impersonation

CommunityPopular
uphiago
tls-fingerprint-impersonation

Spoof TLS ClientHello and JA4 fingerprints for browser impersonation.

Overview

Publisheruphiago
Repositoryrecon-skills
Skill nametls-fingerprint-impersonation
Stars
1.3K
Forks
213
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 uphiago on GitHub. Read the source before you install it.

Installation

Install the Tls Fingerprint Impersonation 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/uphiago/recon-skills.git /tmp/recon-skills
mkdir -p .claude/skills
cp -r /tmp/recon-skills/recon/tls-fingerprint-impersonation .claude/skills/tls-fingerprint-impersonation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tls Fingerprint Impersonation 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 Tls Fingerprint Impersonation 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 Tls Fingerprint Impersonation 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.

TLS Fingerprint Impersonation

Spoof TLS ClientHello parameters — cipher suites, key exchange groups, signature algorithms, and extension order — to match real browsers at the JA3/JA4 fingerprint level. Uses patched rustls to rebuild the TLS layer with browser-identical configurations. Bypasses TLS fingerprinting detection (Cloudflare, Akamai, F5) that flags non-browser TLS stacks. Supports 20 browser profiles including Chrome 100-142, Firefox 128-144, Safari iOS 18, and OkHttp 3-5 (Android).

When to Use

  • Target returns 403/blocked on curl/httpx even with correct User-Agent headers.
  • Cloudflare or Akamai is fingerprinting TLS ClientHello (JA3/JA4 mismatch with browser).
  • API probing requires mobile-app impersonation (OkHttp fingerprint for Android).
  • Need high-throughput HTTP requests that pass TLS fingerprint checks without running a full browser.
  • Target shows different behavior based on TLS fingerprint (mobile vs desktop endpoints).

Prerequisites

  • terminal with python3.
  • Python: pip install impit (wraps the Rust library via PyO3).
  • Or Node.js: npm install impit (native binding).
  • Or Rust: impit crate with patched dependencies in Cargo.toml.

Quick Detection

bash
# Check if a target blocks non-browser TLS
curl --max-time 30 --connect-timeout 10 -sk https://target.com | head -1
# If 403, test with browser impersonation:
python3 -c "
from impit import Impit
impit = Impit.builder().with_fingerprint('chrome142').build()
r = impit.get('https://target.com').text
print(r[:200])
"

Procedure

Phase 1 — Browser Profile Selection

Choose the right fingerprint for your target:

ProfileUse caseKey differentiator
chrome142Modern desktopLatest Chrome, post-quantum KEX (X25519MLKEM768), GREASE
chrome100Legacy systemsOlder cipher suites, no GREASE in key exchange
firefox144Firefox desktopDifferent pseudo-header order, FFDHE groups, SHA-1 signatures
safari_ios18iOS mobile3DES ciphers, duplicate signature algorithm, no session tickets
okhttp4Android appsBoringSSL profile, no GREASE, no ECH, simpler cipher suites
chrome124Common defaultGood balance of modern compatibility and detection pass rate
python
from impit import Impit

# Chrome 142 (latest)
client = Impit.builder().with_fingerprint("chrome142").build()

# Firefox 144
client = Impit.builder().with_fingerprint("firefox144").build()

# Safari iOS 18 (mobile API endpoints)
client = Impit.builder().with_fingerprint("ios18").build()

# OkHttp 4 (Android app impersonation)
client = Impit.builder().with_fingerprint("okhttp4").build()

Phase 2 — Basic Request

python
from impit import Impit

impit = (
    Impit.builder()
    .with_fingerprint("chrome142")
    .with_ignore_tls_errors(True)  # for self-signed certs during recon
    .with_http3()                   # HTTP/3 support
    .with_fallback_to_vanilla(True) # retry without fingerprint if blocked
    .build()
)

response = impit.get("https://target.com")
print(response.status_code)
print(response.headers)
print(response.text()[:500])

Phase 3 — With Proxy

python
impit = (
    Impit.builder()
    .with_fingerprint("chrome142")
    .with_proxy("http://user:pass@residential-proxy:8080")
    .with_default_timeout(15_000)  # 15 seconds in milliseconds
    .build()
)

response = impit.get("https://target.com/api/endpoint")

Phase 4 — Custom Headers

python
# Custom headers are merged with fingerprint defaults
# Fingerprint headers have lower priority — custom headers win on conflict
response = impit.get(
    "https://target.com/api/v1",
    headers={
        "X-Forwarded-For": "[REDACTED_IP]",
        "Authorization": "Bearer token",
    }
)

Phase 5 — Cookie Session

python
from impit.cookie import Jar

impit = (
    Impit.builder()
    .with_fingerprint("chrome142")
    .with_cookie_store(Jar())  # persistent cookie jar
    .build()
)

# Login
impit.post("https://target.com/login", json={
    "username": "admin", "password": "admin"
})

# Authenticated request — cookies preserved automatically
response = impit.get("https://target.com/dashboard")

Phase 6 — Fingerprint Selection Logic

Choose based on target characteristics:

python
def select_fingerprint(target_url):
    """Auto-select browser fingerprint based on target."""
    if "mobile" in target_url or "api/v2" in target_url:
        return "ios18"
    elif "android" in target_url or "play.google" in target_url:
        return "okhttp4"
    elif target_url.startswith("https://"):
        return "chrome142"  # default for modern HTTPS
    return "chrome124"

Phase 7 — JA4 Hash Validation

Verify your TLS fingerprint is working correctly:

bash
# Test against a site that returns JA4 hash in response headers
python3 -c "
from impit import Impit
impit = Impit.builder().with_fingerprint('chrome142').build()
r = impit.get('https://cloudflare.com/cdn-cgi/trace')
print(r.text())
# Look for JA4 hash in trace output or response headers
"

Browser Fingerprint Reference

TLS Configuration per Browser

FeatureChrome 142Firefox 144Safari iOS 18OkHttp 4
TLS versions1.3 + 1.21.3 + 1.21.3 + 1.21.3 + 1.2
GREASE cipher✅ (pos 1)
GREASE key exchange✅ (pos 1)
Post-quantum (MLKEM768)
FFDHE groups✅ (2048/3072)
SHA-1 signatures✅ (legacy)✅ (RSA only)
ECH GREASE
3DES ciphers
Certificate compressionBrotliZlib+Brotli+ZstdZlib
Delegated credentials
Session tickets
Duplicate signatures✅ (RsaPssRsaSha384)

HTTP/2 Settings per Browser

BrowserStream WindowConnection WindowPseudo-Header Order
Chrome6,291,45615,663,105:method :authority :scheme :path
Firefox131,07212,517,377:method :path :authority :scheme
Safari iOS2,097,15210,485,760:method :scheme :authority :path
OkHttp16,777,21616,777,216:method :path :authority :scheme

Multipart Boundary Format

BrowserFormatExample
Chrome----WebKitFormBoundary + 16 alphanumeric----WebKitFormBoundaryx8fH3kLm9pQr2sTv
Firefox----geckoformboundary + hex u64 values----geckoformboundary3fa8c10e5d6b2904
OkHttpUUID v4550e8400-e29b-41d4-a716-446655440000

Pitfalls

  • Not all sites use TLS fingerprinting. Test with curl first — if it works, TLS fingerprinting is not the blocker.
  • Fingerprint must match User-Agent. Using Chrome TLS with Firefox UA headers will be detected.
  • HTTP/1.1-only sites don't use HTTP/2 impersonation. The with_http3() flag only matters for sites that support it.
  • OkHttp 3 is TLS 1.2 only. Some modern servers reject TLS 1.2 connections.
  • TLS fingerprint caching means first request is slowest. CryptoProvider instances are cached per fingerprint — subsequent requests are fast.
  • Vanilla fallback may leak your real TLS fingerprint. Disable vanilla_fallback if stealth is critical.

Verification

  1. Confirm TLS fingerprint matches target browser using https://www.howsmyssl.com/ or Cloudflare trace endpoint.
  2. Check cf-ja4 response header when hitting Cloudflare-protected sites.
  3. Verify response status changes from 403 → 200 when using browser fingerprint vs vanilla curl.
  4. Test with multiple browser profiles to find the one that passes the target's detection.

Related Skills

  • http2-header-impersonation — HTTP/2 pseudo-header ordering and SETTINGS frame matching.
  • stealth-browser-launch — Full browser automation with C++ fingerprint patches for JS-heavy targets.
  • humanize-automation — Human-like interaction patterns for behavioral detection bypass.

Frequently asked questions

What does the Tls Fingerprint Impersonation AI skill do?

Spoof TLS ClientHello and JA4 fingerprints for browser impersonation.

Why use Tls Fingerprint Impersonation on TypingMind?

Because you install it once and use it with any model. Tls Fingerprint Impersonation 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 Tls Fingerprint Impersonation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/uphiago/recon-skills/tree/main/recon/tls-fingerprint-impersonation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tls Fingerprint Impersonation?

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 Tls Fingerprint Impersonation?

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

Is the Tls Fingerprint Impersonation AI skill free?

Yes. It is published on GitHub by uphiago 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇