WebAssembly Fingerprinting vs. Anti-Detect Browsers: Why WASM Could Change the Game

Canvas, WebGL, WebRTC, AudioContext, User-Agent—for years, anti-fraud systems and anti-detection browser developers have been racing around these parameters. The user tries to appear like a normal device, the anti-detection system matches the characteristics visible to the website, and the anti-fraud system looks for discrepancies. Now this race has a new front, and for anti-detection systems, it's potentially much more unpleasant: WebAssembly Fingerprinting.

The problem here isn't just another parameter added to profile settings. WASM allows you to get closer to the characteristics of actual code execution on a specific device, which fundamentally changes the task itself. Modern anti-detection tools—Multilogin, AdsPower, Dolphin Anty, Octo Browser, GoLogin, Kameleo, Incogniton, Linken Sphere—already work reliably with a large number of classic fingerprint parameters. But independent research shows that even advanced products give themselves away through a combination of inconsistencies, browser tampering artifacts, and other signals. WebAssembly adds another layer to this.

What is WebAssembly and why is it so interesting for antifraud?

WebAssembly, or WASM, is a low-level binary instruction format for executing code in the browser. Its key advantages are high performance and the ability to compile code from C/C++ and other languages. To put it simply, JavaScript operates at a fairly high level of abstraction, while WebAssembly performs computational tasks much closer to native code. And this is where fingerprinting gets really interesting. A classic fingerprint typically asks the browser, "What are you telling me about yourself?" WASM allows you to add a second question: "How do you actually perform computations?"—and it's much harder to spoof the answer.

The Vektor T13 demo uses a compact, single-page WebAssembly Fingerprint Checker—just 447 lines including code. It's built on a combination of JavaScript and WebAssembly: JS calls a WASM function, WebAssembly calls back to JavaScript, and so on, over and over. The result is a kind of computational ping-pong—JavaScript → WASM → JavaScript → WASM—and the performance characteristics of these operations generate a set of measurements, from which the device identifier is then derived; in the demonstrated implementation, it's in SHA-256 format.

Why User-Agent is Almost Useless Here

Let's take the anti-detect profile. At the browser settings level, it reports: Windows, the required version of Chrome, specific hardware. The user agent matches the profile, Canvas appears plausible, WebGL is consistent, WebRTC is configured, and everything else is fine. But then the website runs a computational test. And if the code execution characteristics statistically inconsistently match the declared environment, a completely different signal class is generated.

That's why, when applied to this type of analysis, Vektor T13 considers standard User-Agent substitution a practically obsolete technique. Antifraud software no longer has to trust the browser's narrative—it can measure its behavior. This is the fundamental problem.

The worst part: WASM fingerprint may depend on the actual hardware

The webinar specifically emphasizes the dependence of results on hardware resources: despite the code running inside the browser, the computational characteristics depend on the environment in which the browser actually runs. This potentially allows for the construction of classifiers that differentiate between a real PC, a virtual machine, a VDS/VPS, and server hardware. Vektor T13 specifically focuses on server CPUs: their computational characteristics potentially allow for the differentiation of server infrastructure from a typical user computer.

It's important not to overestimate the method here. This doesn't mean WebAssembly issues a command like getRealCPUModel() to JavaScript. What's at issue is something else: indirect computational characteristics are collected, which are then statistically compared with accumulated profiles of known environments. And the more data the anti-fraud tool accumulates, the more effective this approach becomes.

Why this is especially annoying for virtual machines

Virtualization has long been used to separate environments, but the new approach introduces a caveat: a virtual machine must not only replicate the characteristics of a real computer but also behave similarly during computation. The demo claims that the WASM test can detect signs of a virtualized environment. However, Vektor T13 itself makes an important caveat: VM detection is not a guaranteed result—it explicitly states that it knows how to influence the outcome by allocating a peak amount of resources to the browser. So, there's no magic involved: WASM fingerprinting doesn't guarantee detection of any virtual machine. It's a new, highly informative signal that can be combined with other indicators—and it's precisely in this form that it's particularly interesting for antifraud.

What does this mean for Multilogin, AdsPower, Octo, and others?

This is where the industry's main problem begins. Anti-detection methods have long competed not on the number of interface switches, but on the quality of fingerprint matching: modern products control dozens of parameters, from Canvas and WebGL/WebGPU to AudioContext and hardware characteristics. But WASM poses a different question: is it possible to force real code execution to match the synthetic identity of a browser profile? And the answer may vary for different architectures.

Multilogin is traditionally considered one of the most technologically advanced products in its category—it uses its own browser engines. However, even a deep modification of Chromium or Firefox alone doesn't solve the problem of hardware-dependent measurements. If a profile is declared by one environment, but the WASM benchmark consistently shows the characteristics of another, the anti-fraud solution receives an additional consistency signal. The task then shifts from "correctly replacing a fingerprint" to "correctly reproducing the computing behavior of the device to which this fingerprint is supposed to correspond"—a significantly more difficult task.

The logic is the same with Chromium-based products—AdsPower, Dolphin Anty, Octo Browser, GoLogin, Incogniton, Kameleo. You can modify Canvas, align WebGL, and set hardware Concurrency, but if the measured behavior of the environment conflicts with the declared profile, the antifraud solution has grounds to raise the risk score, and the more independent WASM tests there are, the more difficult the task. And these aren't just abstract arguments: an independent study of anti-detection tools from 2026 already detected AdsPower and several competitors using combinations like prototype integrity and global-scope, and the academic study Browser Polygraph separately tested modified browsers—Linken Sphere, Incogniton, GoLogin, Octo Browser, AdsPower, and others—all without WASM. WebAssembly only expands the set of available characteristics even further. And the issue isn't that any particular anti-detection tool is "bad"—the problem is architectural, affecting the entire category.

Why one WASM test doesn't eliminate antidetects

This is a crucial caveat. Simply running a benchmark and getting "your WASM executed in 137 milliseconds" isn't a good indication. Performance depends on a host of factors: CPU load, power savings, temperature, background processes, the browser scheduler, the number of allocated resources, and dozens of other factors. The true strength of an approach comes not from a single number, but from a series of different tests, repeated measurements, statistical normalization, and a large base of reference devices.

This is precisely why Vektor T13 emphasizes the need to accumulate extensive statistics. The webinar claims the ability to distinguish users with an error rate of less than 1%, but this should be viewed as the result of the specific approach presented, not as a proven universal accuracy of any WASM fingerprinting on any website. The difference is fundamental.

JavaScript and WASM as a New Source of Entropy

The idea of ​​measuring more than just pure WASM performance is particularly intriguing. In the demo, the boundary between the two environments is constantly crossed—JS → WASM → JS → WASM—and each transition adds new execution characteristics. Different types of operations, the number of iterations, the time it takes to transition between contexts, computational characteristics, and the stability of results can be measured. Instead of a single parameter, a whole vector of characteristics is obtained—conventionally, F = {t1, t2, t3, … tn}—which, after normalization, is compared with known devices or hardware classes. Therefore, a promising WASM fingerprint is not just another Canvas hash, but rather a behavioral fingerprint of the computing environment.

От Canvas Fingerprinting к Computational Fingerprinting

This, perhaps, is the main shift. The first generation of fingerprinting asked: What does the browser report? The next generation asked: What does the browser draw? Canvas and WebGL became a way to measure the output of the graphics stack; WebGPU went deeper, toward the GPU. WASM allows us to ask the following question: How does the device compute? And this is much closer to the idea of ​​computational fingerprinting. In such a world, it's not enough for an anti-detect to replace navigator.userAgent, navigator.hardwareConcurrency, WebGLRenderer, Canvas, AudioContext, and a dozen other APIs—all these values ​​must be physically consistent with the observed system behavior.

New level of consistency checking

Let's imagine a profile: Windows 11, Chrome, Intel Core i5, a typical home PC, its own Canvas, WebGL, and WebGPU. But the WASM profile shows computing characteristics statistically typical of the server environment. This fact alone doesn't necessarily indicate fraud. However, anti-fraud software can now combine IP reputation, TLS fingerprint, browser fingerprint, Canvas, WebGL, WebGPU, WASM execution profile, and behavioral signals to produce a much stronger risk model. This is precisely the direction in which modern detection systems are developing: it is the combination of signals that is effective, not relying on a single "magic" fingerprint.

What about Cybersource, PerimeterX and Microsoft?

In the webinar, Vektor T13 asserts that similar technologies are already being used in the anti-fraud infrastructure of Cybersource and PerimeterX, with Cybersource being at a more mature stage of implementation. He cites another example, Microsoft Partner registration, where, according to his observations, WASM fingerprinting is used to filter out suspicious registrations, including virtual machines, VPS, and remote servers.

It's worth being careful here: these are statements and observations by the webinar's author, not publicly confirmed technical specifications from these companies. It would be wrong to turn them into a thesis that "Microsoft is definitely blocking VPSs using this algorithm" without further verification. But the trend itself is quite realistic: browser fraud detection already makes full use of multi-layered environment classification, and academic studies show that modified browsers are detected based on a combination of signals.

Will WebAssembly Fingerprinting Destroy Anti-Detections?

If we take "destroy" literally, no. But it is quite capable of destroying the old anti-detection model, which considered it sufficient to carefully replace a set of popular browser fingerprint parameters. And this is far more interesting. Multilogin, AdsPower, Dolphin Anty, Octo Browser, GoLogin, Linken Sphere, Incogniton, Kameleo, MoreLogin, Undetectable, and others will have to consider not only the values ​​the site sees but also whether the actual behavior of the environment matches these values. Essentially, a new stage in the race has begun: fingerprint spoofing → fingerprint consistency → hardware & computational consistency.

Canvas can be changed. User-Agent can be rewritten. WebGL can be harmonized. But making one computer behave computationally like another is a task of a fundamentally different order. Therefore, WebAssembly Fingerprinting should be viewed not as another entry in the vast anti-fraud grid, but as a potential transition from "what the browser says about itself" to "what the browser actually is." And if anti-fraud systems collect sufficiently large datasets of real devices, virtual machines, server CPUs, and various browser engines, this very difference could become one of the main challenges for the anti-detection industry in the coming years.

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