Antidetect Browsers: How AI Is Scaling Online Fraud and Account Takeovers

Online platforms rely on digital signals to identify suspicious accounts and activity. Antidetect browsers make those signals harder to trust by allowing operators to create and manage large numbers of browser profiles that appear to belong to different users, devices and locations.

While these tools have legitimate uses, Check Point Research found documented evidence of their use in criminal ecosystems for activities including account takeover and payment fraud. AI and automation can further increase the scale of these operations.

Key Findings

• Antidetect browsers allow operators to manage large numbers of isolated browser profiles that appear to come from different users, devices and locations.
• Check Point Research found documented use of antidetect browsers in criminal ecosystems for account takeover, payment fraud and other malicious activity.
• These accounts can also be used to coordinate influence operations, which becomes an even greater concern ahead of elections.
• Antidetect browsers such as AdsPower, MultiLogin and Kameleo combine browser fingerprint manipulation with proxy integration, automation and multi-account management.
• AI integrations and automation can further scale these operations by supporting content generation and activity across multiple isolated identities.
• Organizations should look beyond individual account behavior and monitor infrastructure, network and session signals that may reveal coordinated activity.

What Are Antidetect Browsers?

Every browser reveals information that websites can use to distinguish one user from another. This can include device screen resolution, time zone, browser type, fonts, WebGL information, hardware characteristics and other parameters. Together, these signals can form a browser fingerprint.

Antidetect browsers manipulate these signals, allowing each browser profile to appear as a different user or device. They are widely used for managing multiple online accounts, including across social media platforms such as Facebook, Instagram, TikTok, YouTube and X. The tools also support accounts across e-commerce, financial services and professional networks.

The result is a significant visibility challenge. One operator can control many profiles while each can appear to a platform as an independent user with its own device characteristics and network identity.

Check Point Research identified five layers through which antidetect browsers can obscure this connection, spanning device, network, session, behavior and content.

Figure 1 – The five layers through which antidetect browsers defeat platform detection methods

How Antidetect Browsers Are Used in Cybercrime

The significance of antidetect technology goes beyond managing multiple social media profiles.

Check Point Research found documented references across underground forums and criminal marketplaces to antidetect browsers being incorporated into broader attack workflows. These include account takeover operations in which stolen session cookies are loaded into browser profiles, payment card fraud, fraudulent loan applications and the bulk creation of artificial accounts.

One underground forum advertisement identified by researchers offered an adversary-in-the-middle engine with functionality to export harvested session credentials directly into antidetect browsers. Another recruitment post advertised infostealer malware compatible with multiple antidetect platforms for harvesting data including session cookies and authentication tokens.

This combination matters because attackers are not relying on a single evasion technique. Antidetect browsers can become one component of a larger infrastructure that combines stolen credentials, residential proxies and manipulated device identities.

A Commercial Ecosystem Built for Scale

The tools analyzed by Check Point Research illustrate how accessible these capabilities have become. AdsPower, for example, publishes pricing tiers supporting thousands of profiles and provides automation templates for activities including liking, commenting, direct messaging and follower scraping across multiple platforms. Its interface also allows operators to configure fingerprint parameters such as WebRTC, time zone, location and language for individual profiles.

Figure 2 – Comparison of popular antidetect tools.

MultiLogin similarly provides isolated browser profiles, automation support and mobile infrastructure, while Kameleo provides browser fingerprint manipulation and automation capabilities. The research also identified discussions and advertisements for these tools on Russian-language underground forums.

The result is an ecosystem packaged much like conventional commercial software, with subscriptions, documentation, automation, customer support and integrations.

How AI Changes the Scale of Antidetect Operations

AI adds another layer to this infrastructure. Previously, maintaining large numbers of identities required substantial manual work. Profiles had to be created, maintained and operated, while content had to be written and distributed.

The research shows how parts of that workflow can increasingly be automated. AdsPower advertises a dedicated Model Context Protocol server for integration with large language model workflows, alongside automation templates and tools for establishing activity histories for accounts before operational use.

Figure 3 – AdsPower’s RPA Marketplace lists pre-built automation templates for bulk account activity across major platforms, including automated liking, commenting, direct messaging, follower scraping, and AI-assisted engagement.

Why Organizations Need to Look Beyond Account Behavior

For security teams, the research highlights a broader challenge: detecting the account itself may not be enough.

Antidetect browsers are designed to manipulate many of the signals organizations use to establish whether a session, device or user appears legitimate. Check Point Research recommends increasing attention to infrastructure-level indicators, including residential proxy infrastructure at scale, WebRTC suppression patterns, unusual cookie injection sequences and antidetect browser signals in web traffic.

Organizations should also review whether session-monitoring systems can identify pre-authenticated session anomalies and assess fraud controls that depend heavily on device consistency or session continuity.

How Organizations Can Respond to Antidetect Browser Activity

As antidetect browsers make individual accounts harder to assess in isolation, organizations should strengthen detection across the broader infrastructure and session level. Check Point Research recommends focusing on several areas:

• Monitor infrastructure signals: Look for antidetect ect browser indicators, large-scale use of residential proxies and WebRTC suppression patterns.
• Strengthen session monitoring: Identify anomalies involving pre-authenticated sessions and unusual cookie injection sequences.
• Review fraud detection controls: Assess controls that rely heavily on device consistency or session continuity, as these signals can be manipulated.
• Look for connected activity: Evaluate suspicious behavior across accounts rather than relying only on the activity of an individual profile.

Antidetect browsers have legitimate applications, but the same capabilities can also support malicious operations. The combination of identity spoofing, automation, proxy infrastructure and AI-assisted content generation can make it easier to operate large numbers of apparently independent online identities.

As these capabilities evolve, organizations will need to look beyond individual accounts and consider the infrastructure and signals connecting them. The key question is increasingly: are thousands of seemingly independent accounts actually independent at all?

To read the full technical findings, access the Check Point Research publication here.