How AI Hallucinations Create New Security Risks for Users

[ssingh29]

By:  Suriti Singh, Senior Product Manager, Palo Alto Networks 

The rapid adoption of Generative AI has fundamentally changed how we interact with information. Today, AI-generated outputs are increasingly embedded into enterprise workflows, including research, software development, customer support, and autonomous agent tasks. There is a high degree of implicit trust placed in the outputs generated by these models.

But a hidden security gap is emerging within this new layer of productivity. Attackers have found a way to turn our trust in artificial intelligence against us by exploiting a well-known AI flaw: hallucinations.

This emerging threat vector is called Phantom Squatting. It represents a shift in how cybercriminals engineer phishing attacks, moving away from guessing what users might click and moving toward using AI-hallucinated URLs.

 

What is Phantom Squatting?

Large Language Models (LLMs) are designed to be fluidly conversational and highly persuasive. However, many LLM-driven interactions rely on probabilistic pattern generation rather than validating referenced resources in real time. When asked a specific question such as requesting a customer support link, a corporate portal login, or a software download page, an LLM doesn't verify if a link actually exists before sharing it. Instead, it predicts what a plausible URL would look like based on patterns in its training data.

Often, these models generate perfectly structured, highly convincing URLs that point to domains that have never been registered before.

Cybercriminals have recognized this pattern. In a Phantom Squatting attack, adversaries systematically probe AI models to discover the exact "phantom" domains the AI tends to hallucinate for specific brands. The attacker then purchases these non-existent domains for a few dollars, sets up malicious infrastructure, and waits. When an unsuspecting user or an autonomous AI agent follows the AI's recommendation, they walk straight into a trap.

 

The New Risk Surface: Humans and Autonomous Agents

Phantom Squatting expands the corporate risk surface in two primary ways:

• The Human Element: Users are trained to spot typos, strange characters, or mismatched domains in phishing emails. But when an AI assistant provides a link that perfectly mirrors a trusted brand’s structure (e.g., matching subdomains or exact product paths), user awareness defenses are bypassed. The trust we have built in AI tools creates a direct path for credential theft and malware delivery.
• The Agentic Element: As enterprises begin deploying autonomous AI agents to automate workflows, these systems are increasingly designed to fetch URLs and interact with the web dynamically. If an AI agent processes a hallucinated link without validation, it can inadvertently pull malicious payloads or instructions into secure corporate systems, all without any human ever clicking a link.

 

Why Reactive Security Controls Fall Short

Most traditional web security approaches still rely heavily on historical signals, reputation databases, web crawling and observed web activity. When a new domain is identified, it is categorized based on what is currently hosted there.

This creates a critical "Moment of Hallucination" blind spot:

• No Initial Reputation: When an LLM generates a phantom URL, the underlying domain might not even be registered yet. It has zero reputation score: neither good nor bad.
• Invisible to Brand Monitoring: Traditional brand protection tools look for active infringements. They cannot protect against domains that do not yet exist on the internet but exist within the predictive matrix of an AI model.

By the time a traditional security feed flags a domain as malicious, the attacker has already registered it, deployed their phishing kit, and captured sensitive user credentials. To defeat an AI-driven threat, security teams must move from a reactive posture to a proactive one.

 

Proactive Defense: Advanced URL Filtering

To neutralize the risk of weaponized AI hallucinations, organizations need security capabilities that can identify hallucinated domains before attackers can weaponize them. This requires integrated, real-time, advanced URL filtering designed specifically for the modern AI ecosystem.

A proactive defense model works by staying one step ahead of the adversary through automated, continuous analysis:

1. Predicting the Hallucination: Our Advanced Phantom Squatting Detectors can simulate thousands of natural interactions across various AI model families to harvest high-probability hallucinated URLs across industries.
2. Continuous Infrastructure Watch: Once these non-existent phantom domains are mapped, they are placed on a specialized, proactive monitoring list. When threat actors attempt to register one of these monitored domains, security teams are alerted to investigate and respond.
3. Inline Real-Time Blocking: By closing the gap between domain registration and weaponization, advanced URL filtering can dynamically block access to these domains before an attacker has time to fully stage a phishing campaign or malware landing page.

 

Real-Time Protection with Palo Alto Networks

At Palo Alto Networks, we are committed to securing the future of AI-driven workflows. By combining industry-leading cloud threat intelligence with advanced enforcement layers across our SASE platform, Next-Generation Firewalls, and Prisma Access Browser, we deliver real-time protection against zero-day threats like Phantom Squatting.

With Advanced URL Filtering (AURL), organizations can move from defensive reaction to coordinated prevention, protecting employees, protecting autonomous agents, and ensuring that the adoption of Generative AI does not come at the cost of enterprise security.

Want to learn more about how to protect your organization from emerging AI-driven threats? Contact your Palo Alto Networks representative today to schedule a demo of Advanced URL Filtering.