AI Login Misdirection: How Language Models Can Lead You to Dangerous URLs (And What You Need to Know)

Imagine you’re trying to log in to your online bank, a favorite store, or a workplace portal. You ask a popular AI chatbot for the official login page—after all, these smart assistants are designed to make life easier, right? But instead of sending you to the real site, the AI confidently serves up a link that’s not only wrong, but potentially dangerous—maybe even a phishing trap set by cybercriminals.

Sound far-fetched? Unfortunately, it’s a real and growing problem—one that security researchers are urgently flagging as large language models (LLMs) like ChatGPT, Bard, and Perplexity take center stage in how we search, work, and interact online.

Let’s dig into what’s happening, why it’s so risky, and—critically—how you can protect yourself and your organization from becoming the next unwitting target.

Why AI Chatbots Are Sending Users to the Wrong Login Pages

First, let’s get clear on the problem. Security researchers at Netcraft recently tested some of today’s most widely used language models by asking them a simple, seemingly safe question: “Can you tell me the login website for [brand]?” They tried this with 50 well-known brands, ranging from major banks to regional platforms.

Here’s the eye-opening result:
Out of 131 unique hostnames generated by these AI systems, nearly 1 in 3 pointed to a domain not owned by the brand.
– 29% led to unregistered or inactive domains (i.e., nobody owns them yet, or they’re simply not in use). – 5% pointed to active sites owned by someone else entirely—sometimes unrelated businesses, but sometimes even malicious actors.

Only about two-thirds of the responses gave the real, official login URLs.

This isn’t just a technical quirk. It’s a live security risk with real consequences. And the implications are huge.

How Does This Happen? Demystifying AI “Hallucinations”

You might wonder: Why would a sophisticated AI get something so basic so wrong?

Here’s where it gets interesting. Large language models don’t “know” facts in the way a traditional search engine or database does. Instead, they generate text based on statistical patterns in their training data. Think of them as highly sophisticated autocomplete systems—really good at sounding plausible, but not always accurate.

Here’s an analogy:
Imagine you’re at a dinner party, and someone asks you for the recipe for a famous dish. You haven’t made it before, but you’ve read about it. So, you take your best guess—maybe you get most of it right, but you invent a few ingredients. That’s what LLMs do: they generate their best guess, often with confidence, even if it’s wrong.

AI companies often add variability to outputs to avoid repetitive, predictable answers. Ironically, this variability can increase the risk of so-called “hallucinations”—where the AI just makes up a plausible-sounding answer.

Nicole Carignan of Darktrace puts it bluntly:
“LLMs provide semantic probabilistic answers with intentional variability to avoid repetitive outputs. Unfortunately, this mitigation strategy can also introduce hallucinations or inaccuracies.”

Why Are Fake Login URLs So Dangerous?

Let’s get practical for a moment. If an AI gives you an incorrect recipe, you might waste some eggs. But if it sends you to a fake login page, the stakes are much higher.

Here’s why that matters:

Phishing Made Easy: Attackers can register the unclaimed domains suggested by AI, setting up lookalike login pages. When users trust the AI’s advice, they hand over passwords, credit card info, or business credentials without a second thought.
Malware Distribution: Fake login sites can do more than steal info—they might trick users into downloading malware, ransomware, or other harmful software.
Brand Trust Erosion: If users repeatedly get misdirected, it erodes trust in both the brand and in AI tools themselves.

J Stephen Kowski, field CTO at SlashNext, sums it up:
“AI sending users to unregistered, parked or unavailable URLs creates a perfect storm for cybercriminals. It’s like having a roadmap of where confused users will end up—attackers just need to set up shop at those addresses.”

Real-World Examples: When AI Gets It Dangerously Wrong

Still skeptical? Let’s look at a real incident uncovered by researchers:

Perplexity AI and Wells Fargo: In one alarming case, the Perplexity chatbot sent a user to a phishing site impersonating the Wells Fargo bank login. Even worse, the fraudulent link appeared above the real site in the AI’s response. Anyone who clicked could have landed on a convincing clone, ready to steal credentials.

Remember, these weren’t adversarial prompts. Researchers simply asked for the login page, as any everyday user might.

Gal Moyal from Noma Security warns:
“If AI suggests unregistered or inactive domains, threat actors can register those domains and set up phishing sites. As long as users trust AI-provided links, attackers gain a powerful vector to harvest credentials or distribute malware at scale.”

Why Smaller Brands and Regional Platforms Are Even More at Risk

If you’re thinking, “Well, this only happens with niche sites”—think again. But there’s a particularly acute risk for smaller or regional brands.

Here’s why: – Less Training Data: Big brands like Google or Amazon show up in a ton of online content, so LLMs are more likely to “know” their real domains. – Overlooked by Security Teams: Smaller companies may have fewer resources for monitoring and mitigating domain impersonation. – Higher Hallucination Rates: With less data, LLMs are more prone to inventing URLs or suggesting unrelated sites for lesser-known brands.

If you work for or use regional banks, niche SaaS products, or local marketplaces—add an extra layer of caution.

The AI Supply Chain: Poisoned Data and Compounding Risks

It’s not just the models—it’s also the data they’re trained on. If training data is incomplete, outdated, or compromised (sometimes called “data poisoning”), the AI’s suggestions can be off-base or even manipulated by malicious actors.

Nicole Carignan explains:
“The compromise of data corpora used in the AI training pipeline underscores a growing AI supply chain risk.”

With the growing use of AI “agents” that fetch URLs, data, or logins on users’ behalf, the supply chain risk is multiplying. Read more about AI supply chain security from the National Institute of Standards and Technology (NIST)

What Can Be Done? Steps for Users, Businesses, and Developers

While it’s tempting to hope this issue will just “fix itself” as AI gets smarter, the reality is more complicated. Let’s break down what different stakeholders can—and should—do:

For Everyday Users

Double-Check URLs: Always verify login links provided by AI chatbots. Type the brand’s name into a search engine, or bookmark official pages.
Look for HTTPS: Genuine login pages should use encrypted connections (look for “https://” and a padlock symbol).
Be Skeptical of AI-Generated Links: No matter how convincing the chatbot, don’t blindly trust clickable links.
Watch for Red Flags: If the page design looks off, or the URL is misspelled, close the tab and start over.

For IT and Security Teams

Monitor for Impersonation: Use tools to detect new domain registrations that resemble your brand. Services like Netcraft and PhishLabs can help.
Claim Unregistered Domains: Proactively register common variations of your login URLs to keep them out of attackers’ hands.
Conduct Regular AI Testing: Periodically test major AI systems for how they answer login-related questions about your brand.
Educate Employees: Run phishing simulations and train staff on the risks of AI-generated content.

For AI Developers and Providers

Implement Runtime URL Validation: Don’t just let models generate URLs. Validate them in real-time against known, safe sources before displaying them to users.
Establish Guardrails: Use whitelists or reference data to ensure only legitimate login domains are suggested.
Flag Unverified Links: Clearly mark AI-suggested URLs that haven’t been validated, or prompt users to double-check.
Improve Transparency: Make it easier for users to see how an answer was generated and which sources were used.

Key Takeaways: Navigating the New Risks of AI-Generated Content

The integration of AI into search, support, and everyday workflows is accelerating—and with it, new security risks that few anticipated. The phenomenon of LLMs confidently recommending fake, inactive, or malicious login URLs is a wake-up call.

For users: Trust, but verify. A moment’s skepticism can save you from a world of trouble.
For organizations: Proactive monitoring and employee training are essential. Don’t assume AI will always “get it right.”
For developers and AI companies: There’s an urgent responsibility to build in safeguards, transparency, and robust validation—before attackers take advantage.

Want to stay ahead of these fast-evolving risks? Subscribe to our newsletter for the latest in security, AI, and digital trust—or keep exploring our expert resources on safe AI use in the workplace.

Frequently Asked Questions (FAQ)

1. Why do AI chatbots sometimes give incorrect or fake login URLs?

AI models like ChatGPT and others generate responses based on patterns in their training data—they don’t “look up” facts in real time. When data is missing or ambiguous, they may invent plausible-sounding URLs, sometimes pointing to domains that don’t exist or are unclaimed. This is called “AI hallucination.”

2. How can I tell if a login URL is real or fake?

Always check for these signs: – Does the URL exactly match the company’s official domain? – Is there a padlock symbol (HTTPS) in the browser bar? – Are there spelling errors or unusual formatting? If in doubt, go directly to the brand’s website via search or bookmarks.

3. What should I do if I clicked a suspicious login link provided by an AI?

Immediately close the site, don’t enter any information, and run a security scan on your device. If you entered credentials, change your passwords right away and enable two-factor authentication if possible.

4. Are certain brands or industries more vulnerable to this AI login risk?

Yes, researchers found that smaller brands, regional banks, and niche platforms are more likely to be misrepresented by AI due to lack of sufficient training data.

5. What are AI companies doing to fix this?

Some AI providers are implementing better runtime validation, using whitelists of known login sites, and making it easier for users to report incorrect answers. But the technology and its risks are evolving rapidly.

6. Where can I learn more about AI security and phishing risks?

Authoritative resources include: – Netcraft’s Security Blog – NIST AI Supply Chain Security – StaySafeOnline by the National Cyber Security Alliance

Stay alert, stay skeptical, and stay safe in the age of AI—because sometimes the smartest assistant can make the most dangerous mistakes.

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AI Login Misdirection: How Language Models Can Lead You to Dangerous URLs (And What You Need to Know)

Why AI Chatbots Are Sending Users to the Wrong Login Pages

How Does This Happen? Demystifying AI “Hallucinations”