This is Part 1 of a two-part series detailing how a major obstacle encountered during the OMEGA-T iOS automation research – an obscured WebView CAPTCHA – was diagnosed and ultimately overcome. This article focuses on the diagnostic phase using Frida.

By Neverlow512
10 April 2025
Date of original case study: 02 April 2025

Purpose & Context: This article details the diagnostic phase using Frida, undertaken for research, technical exploration, and methodology demonstration related to analyzing obscured mobile components and advanced anti-bot mechanisms.

Responsible Disclosure: Findings are based on research conducted approximately six months prior to publication to mitigate immediate risks. This work is shared for educational purposes and defensive awareness; very specific details will not be disclosed for obvious reasons. Please use the information gathered from my article or study ethically and legally.

Full Technical Details: The complete Frida diagnostic case study is on GitHub: Full Frida iOS WebView Investigation Research on GitHub

The OMEGA-T Roadblock: An Obscured CAPTCHA 🧱

In my previous article on OMEGA-T, I detailed building a framework for advanced iOS automation that went beyond simple UI clicks by controlling the entire device environment (state, network, location, etc.). This allowed for scalable account generation research on a popular social networking app, bypassing many standard checks.

However, OMEGA-T eventually hit a significant wall: an advanced, interactive CAPTCHA (identified as Arkose Labs) presented during the onboarding flow. The real problem? This CAPTCHA was rendered inside a WKWebView that was completely opaque to standard automation tools like Appium/XCUITest. There was no DOM access, no way to find elements, no way to interact programmatically. Appium was effectively blind.

As a side note, this implementation of the obscured WebView was one of the toughest, most effective anti-automation measures I've encountered targeting standard iOS Apps. Its simplicity makes it quite effective against basic UI inspection. While it's not the first time I encountered this measure, Tinder and Arkose did an incredible job when securing it.

Before I could even think about an automated solution, I needed answers.

  • How was this "black box" WebView loading the CAPTCHA?
  • What kind of communication was happening?
  • And most importantly, how did a successful solution signal back to the native app or host to let the user proceed?

Standard automation couldn't tell me that, so I had to put my gloves on and look through the mess.

Shifting Gears: Why Frida? ⚙️

When Appium goes blind, you need a different set of eyes. So I decided to pivot to dynamic instrumentation using Frida.

For those unfamiliar, Frida is a powerful toolkit that allows you to inject code snippets into running processes, letting you intercept function calls, inspect memory, observe an application's internal behavior in real-time, and a bunch of other things.

Crucially, this kind of deep inspection on iOS typically requires a jailbroken device, which was already part of the OMEGA-T setup. My goal with Frida wasn't necessarily to find an immediate exploit or bypass, but to perform essential reconnaissance – to gain visibility inside the obscured WebView and understand its mechanics.

Some of you might wonder why I didn't choose Burp or Charles, for example. Well, while powerful on their own, none of them compare to Frida when it comes to injecting powerful scripts into running processes, and as you will go further in this article, you will understand why Frida is not just a simple network analysis tool.

The Toolkit: Frida Setup & Methodology 🔬

My diagnostic setup involved:

  • A jailbroken iOS device running the target application.
  • A host macOS VM machine running a Python script (frida_script_example.py) using the frida-python bindings to manage the session and collect data.
  • A custom Frida JavaScript agent (frida_script_example.js), injected into the target application's process via SSH and Frida's tools + Frida's tweak that allows this type of manipulation on iOS.

The core techniques employed in the Frida script were:

  • SSL Pinning Bypass: Essential first step. To see any HTTPS traffic related to the CAPTCHA (communication with Arkose Labs servers, etc.), I implemented standard bypass techniques by hooking functions within iOS's Security framework (like SecTrustEvaluate) to force the app to trust my interception proxy's certificate.

Note: Coming back to why Frida is so powerful, Tinder's security system, like other apps', might detect both Burp' and Charles' certificates. When writing a custom script for Frida, you can bypass these defensive measures. If it sounds like torture, it really is, until you find the right method, though.

  • WKWebView Hooks: This was critical for understanding the obscured content. I focused on hooking key methods within the WKWebView class, particularly evaluateJavaScript:completionHandler:, `loadHTMLString:baseURL:, and **loadRequest:`**.
    This allowed me to intercept and log the exact HTML content being loaded and any JavaScript being executed within that hidden WebView context.

  • Networking Hooks (NSURLSession and alike): To capture any direct communication initiated from the native side or potentially from the WebView itself, I also hooked standard iOS networking APIs like those in NSURLSession. This involved intercepting task creation methods to see outgoing requests and wrapping completion handlers to inspect incoming responses.

The Frida agent parsed this intercepted data, looked for keywords related to CAPTCHAs ("arkose", "funcaptcha"), and sent structured JSON messages back to the Python host script for logging and analysis. The full case study on GitHub includes conceptual pseudocode for these hooks.

Digging Through the Data: Key Findings 💡

This instrumentation quickly yielded vital information:

  1. Loading Mechanism Confirmed:

The loadHTMLString hook showed that the native app was indeed loading a standard HTML structure containing the Arkose Labs JavaScript API (api.js), likely passing configuration data like the public key and potentially a data blob directly into the WebView from the native side.

// Example Log Snippet: Arkose JS loading confirmed via Frida
    {
      "type": "webview_load_html",
      "source": "WKWebView_loadHTMLString",
      "html": ".........",
      "timestamp": 1728382713020
    }
  1. The Moment of Truth - messageHandlers:

Analyzing the network traffic (NSURLSession hooks) and the JavaScript executed (evaluateJavaScript hooks) was interesting, but the real breakthrough came from examining the content of the JavaScript being loaded into the WebView, specifically the configuration object passed to the Arkose api.js.

Within that configuration's callbacks, Frida revealed the crucial communication channel:

// The key finding from Frida logs - Arkose config callback:
    onCompleted: function(response) {
        // How the solved token gets back to native code!
        window.webkit.messageHandlers.AL_API.postMessage({"sessionToken" : response.token});
    }

This was it! The solved CAPTCHA token wasn't being sent back via a typical HTTP request that my network hooks would easily catch. Instead, the WebView's JavaScript was using the window.webkit.messageHandlers bridge – a standard iOS mechanism for JS-to-native communication. The script was calling postMessage on a native handler named AL_API, sending the sessionToken directly back to the Swift/Objective-C code of the main application.

Analogy break! Analogies help, right?:

Imagine the WebView is a guest (JavaScript) in a house (the native app). The guest wants to tell the homeowner (Swift/Objective-C code) something important (the solved token).

Instead of shouting out the window (making an uncontrolled HTTP request), they use an internal intercom system (messageHandlers) installed in the house. They press the specific button for the homeowner (AL_API) and speak their message (postMessage).

The homeowner, listening on that specific intercom channel, hears the message (the native delegate method executes) and receives the message (sessionToken). Only then might the homeowner decide to make an external phone call (a URLSession network request to the servers) to verify the token they just received internally.

This discovery was paramount because it pinpointed the internal intercom as the crucial communication channel, not a standard network call that tools like Burp might easily catch.

Implications & The Path Forward 🤔

This diagnostic phase led to clear conclusions:

  • Appium Blindness Explained: The Frida analysis confirmed the WKWebView was genuinely isolated from Appium's standard inspection capabilities. The obscurity was effective against that specific vector.

  • The Bridge is Critical: The messageHandlers.AL_API.postMessage call was identified as the definitive signal pathway for a successful CAPTCHA solution. This became the new target.

  • Interception Risks: While Frida could observe this postMessage call and the token, trying to intercept it within Frida and then replay it later seemed unreliable. Success might depend on native application state, token validity checks tied to the specific WebView session, or other anti-replay mechanisms that would be hard to replicate consistently.

  • New Strategy Defined: The most robust path forward wasn't interception, but emulation. If I could find a way to automate the visual interaction with the CAPTCHA puzzle, forcing the legitimate onCompleted callback to fire within the WebView, then the valid token would naturally pass through the messageHandlers bridge exactly as the application expected. Or in simpler terms, I could simply solve the captcha as any other user, avoiding the flagging of my accounts. (Although, analyzing the network and confirming the token was being sent/fetched on completion was still part of my plan)

Conclusion & Next Steps ✨

Dynamic instrumentation with Frida proved indispensable when standard UI automation hit the obscured WebView wall. While not the bypass tool itself, Frida provided the crucial visibility needed to understand the CAPTCHA's integration mechanism. By hooking into WKWebView, networking APIs, and bypassing SSL pinning, I was able to pinpoint the window.webkit.messageHandlers bridge as the key communication channel for the solved CAPTCHA token.

This reconnaissance dictated the subsequent research strategy. The next step was clear: develop a method to automate the visual solving process, thereby triggering the legitimate success signal through the identified native bridge.

To be clear, the solution was much simpler than it sounds, as it usually happens when you find the flaw in the system. Get ready tho, as its implementation gave me a lot of sleepless nights and a long-lasting headache.

In Part 2, I'll detail the "Orchestrated Visual Relay" technique developed to achieve exactly that. (not the headache tho, that was definitely not part of the initial plan, just to be clear)

Find Me & Full Research:

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