Fake account creation has always been a persistent challenge for platforms with user registration workflows. Unlike login pages or transaction flows that require existing credentials, account creation workflows are intentionally open and accessible, making them ideal targets for bot attacks. But now the nature of threats has fundamentally shifted. Where previously bots used to follow predictable patterns, input fake-looking data for creating new accounts, and often trigger conventional security measures, today’s bot attacks leverage AI to look and behave like genuine users, creating accounts at scale by bypassing traditional security controls.
Where previously brute-force natured volumetric attacks on account signup pages used to be the standard attack approach, the tactics used in modern fake account creation – AI-generated identities, distributed patterns that mimic organic growth, reconnaissance to map platform vulnerabilities – are all designed specifically to exploit registration workflow characteristics.
Impact of The Gen AI Revolution
The rising adoption of Gen AI has fundamentally changed the landscape of fake account creation bot attacks, dramatically increasing both the volume and sophistication of these attacks.
- AI-Powered Attack Tools
Building effective fake account creation-focused bots previously required significant technical expertise to write automation scripts that could work through the registration process. Gen AI has exponentially lowered these barriers. AI-powered attack tools allow attackers to describe their requirements in natural language and receive functional attack scripts within seconds. LLM-powered browser automation frameworks can accept high-level instructions and handle technical execution automatically. This has expanded the pool of potential attackers rapidly, eliminating the technical barriers that were present earlier.
- AI-Generated Identities
One of the most significant impacts of Gen AI on fake account creation attacks has been the ability to generate unlimited synthetic identities that can pass validation checks during new account registrations. This eliminates one of the traditional identifiers or bottlenecks of these attacks, as previously, bot attacks struggled to pass these validation checks consistently, thereby driving down attacker motivation.
The capability to generate realistic-looking synthetic identities at scale has empowered attackers to confidently launch large-scale attacks that target registration endpoints, with validation rules or basic protections no longer effectively filtering out bot-created accounts, ensuring a good ROI for their efforts.
- Bypass Challenges with AI
Gen AI has also revolutionized how bots bypass mitigation challenges during new account registrations. CAPTCHAs, a traditionally relied-upon mechanism to deter bots from automating account sign-ups, are bypassed through fully automated solving services. AI models that leverage computer vision can solve image recognition challenges with high accuracy rates compared to previously relied-upon human CAPTCHA farms. Text-based CAPTCHAs are decoded by models that use OCR (Optical Character Recognition) and are trained on distorted characters and complex patterns.
Distributed Attacks
Platforms have long relied on detecting unusual registration request patterns, such as sudden spikes in account creation, multiple registrations from the same IP address, or traffic patterns that do not match normal operations, to catch fake account creation attacks.
Sophisticated bots that target fake account creation now distribute their attacks across infrastructure to make bot traffic indistinguishable from legitimate user registration patterns. Modern attacks also route bot traffic through residential proxy networks that provide access to millions of residential IP addresses, ensuring registration attempts come from different legitimate-looking IP addresses rather than obvious data center IPs. This means attack attempts can blend in with organic new user base growth, originating from different residential IPs distributed geographically across regions where the platform has users, and avoid geography-based detections. Sophisticated attacks can also follow a low and slow attack approach that spreads registration attempts over extended periods to avoid volumetric detections and stay under rate limiting thresholds.
Attack Optimization with Intelligent Reconnaissance
Modern attacks begin with reconnaissance to identify optimal attack strategies before launching them, unlike earlier attacks that used generic tactics across all platforms. Earlier, the same bot configuration targeted every signup form the same way, regardless of specific security controls. This resulted in high failure rates, but now attackers use bots to systematically probe registration workflows to identify their structure and weaknesses, and plan optimal tactics before launching attacks. If a platform uses strict rate limiting, the attack operation focuses on IP rotation and traffic distribution. If basic CAPTCHAs is the primary defense, attacks leverage CAPTCHA farms and CAPTCHA solver services. Based on this reconnaissance, attackers design customized strategies that achieve dramatically higher success rates.
The New Reality
Conventional security measures such as rate limiting, CAPTCHAs, and data validation rules are no longer enough to protect new account creation workflows in the face of these advanced attacks. Protecting against these sophisticated attacks requires security approaches designed specifically for this context. Organizations need to adopt advanced bot management solutions that enable real-time behavioral-based detection, mitigation mechanisms that don’t sacrifice conversion rates, and multi-layered bot protection that doesn’t rely on rule-based systems or a single defense tactic.
Contact us to learn more about the advanced, AI-powered bot protection capabilities of the Radware Bot Manager, to fight against these sophisticated attacks.