AI Trust and Safety: How to Spot Google AI Overview Scams

Google’s AI Overviews are changing how people search. Instead of a list of blue links, users see a single, confident answer box that feels authoritative. That’s useful when it works. But as recent reporting has shown, it can also become a dangerous vector for scams—especially when people search for phone numbers or support lines.

This is where AI trust and safety stops being a theoretical topic and becomes a real-world business and consumer risk. If your customers call a fraudulent number surfaced by an AI summary, they’re not just at risk of financial loss; your brand’s credibility and security posture are on the line.

In this article, we’ll unpack how these scams work, what risks they create for individuals and enterprises, and practical steps you can take—both as a user and as a company—to stay safe in an AI-first search world.

If you are looking to harden your organisation’s AI posture beyond public search, you can learn how Encorp.ai helps automate AI risk management and governance across your internal AI stack: AI Risk Management Solutions for Businesses.

Why Google AI Overviews are becoming a scam vector

When people are in a hurry—locked out of a bank account, trying to reach airline support, or dealing with a billing problem—they often search for a customer service number and call the first result that looks legitimate.

AI Overviews are designed to meet that need quickly. They:

Summarise content from multiple web pages.
Highlight a single “best” answer for the query.
Present that answer in a conversational, confident tone.

In many cases, this works fine. But with contact information, the margin for error is extremely small.

How AI Overviews surface contact details

AI Overviews are powered by large language models (LLMs) that ingest and synthesise information from across the web. For "What is the customer support number for X?", the model looks for patterns like:

Phone numbers formatted near brand names.
Phrases like “support hotline”, “customer service”, or “helpline”.
Structured data or business listings that expose contact info.

The danger arises when the model picks up a fraudulent number that has been planted online—often on low-quality websites or misleading business listings—and promotes it as the definitive answer.

Recent examples of fraudulent phone numbers in AI answers

Investigations by outlets such as The Washington Post and Digital Trends have documented cases where Google’s AI-generated answers displayed scam numbers for support lines, including for banks and credit unions.[2] In some cases, victims:

Searched for a bank or government agency number.
Called the AI-suggested number.
Reached a scammer who posed as a legitimate agent and requested card details, login codes, or remote access to devices.

The original WIRED article that inspired this piece walks through exactly how these patterns show up in the wild.[1]

For scammers, this is a dream scenario: the trust users place in Google’s interface is transferred directly to the fraudulent phone number.

How these fake contact details end up in AI summaries

To understand how to defend against these scams, it helps to see the mechanics behind them.

Data scraping and low-quality sources

Modern LLMs and search systems are built on large-scale web scraping. While major platforms implement filters and quality signals, they inevitably ingest:

Low-quality content farms.
Scraped or duplicated business listings.
User-generated content with minimal moderation.

Scammers take advantage of this by publishing fake contact details in multiple places across the web, often alongside the names of well-known companies. This creates a false signal of “consensus” for the model.

From an AI data security and integrity perspective, this is a classic poisoning pattern: attackers inject misleading data into the training or retrieval corpus, hoping models will repeat it as truth.

Authoritative sources like NIST’s AI Risk Management Framework highlight data quality and provenance as core pillars of trustworthy AI—but public web data remains noisy and adversarial.

Lack of verification in synthesis models

LLMs are fundamentally pattern matchers, not fact-checkers. When generating an AI Overview, they:

Predict the most likely text continuation, given the prompt and retrieved documents.
Optimise for fluency and relevance, not for verified accuracy.

This means they can:

Combine a legitimate brand name with a malicious phone number found in a fringe source.
Present speculative or unverified details in a confident tone.

From an AI trust and safety standpoint, the gap is clear: the system doesn’t consistently validate critical fields (like contact numbers) against trusted registries before showing them to users.

Risks to users and companies from AI Overview scams

These scams sit at the intersection of social engineering, data poisoning, and UX design. Both individuals and organisations bear the impact.

Financial fraud and social engineering risks

For individuals, the primary threats are:

Direct financial loss: Scammers may request card details, bank login credentials, or one-time passwords.
Account takeover: Once they have enough information, attackers can reset passwords, take control of accounts, or initiate fraudulent transfers.
Device compromise: Some scams involve asking users to install remote access tools or malware under the guise of “support”.

This aligns with patterns tracked by organisations like the FTC and Europol around tech support and banking scams.

Reputational damage to brands and banks

For companies, especially in regulated sectors (financial services, healthcare, government), the risks include:

Brand erosion: Customers associate the scam experience with your brand, even if the root cause is an external AI system.
Regulatory exposure: Supervisors may ask how you manage AI risk management and customer protection across third-party channels.
Operational burden: Contact centres must handle more fraud-related calls, disputes, and remediation.

If you operate in banking or fintech, this becomes part of a broader AI fraud detection challenge: not just detecting suspicious transactions, but understanding how AI-generated interfaces are shaping customer behaviour before fraud even occurs.

Practical steps users can take right now

While platforms work to improve safeguards, there are concrete things individuals can do today.

1. Verify numbers on official sites or apps

Never rely solely on an AI-generated answer for contact information.

Instead:

Go directly to the company’s official website (typed or bookmarked, not via an ad).
Use the contact or “Help” section to find support numbers.
For banks or utilities, prefer the phone number printed on your card, statement, or official correspondence.
Use the organisation’s official mobile app, which usually includes verified contact options.

This simple habit dramatically reduces the risk of calling a spoofed number and supports good AI data privacy hygiene by ensuring you only share sensitive information through verified channels.

2. Use browser signals and two-step verification

Complement AI answers with extra verification:

Check the domain in your browser’s address bar before clicking any contact links.
Be suspicious of numbers listed on domains that look unrelated to the brand.
If a support agent asks for unusually sensitive information (PINs, full passwords, remote access), hang up and call back using a verified number.
Enable multi-factor authentication (MFA) on your accounts, so even if some information is leaked, attackers have a harder time taking over your accounts.

Guidance from organisations like ENISA stresses this layered approach to digital security.

3. Report suspicious listings to platforms

If you encounter a suspicious number:

Report it via the search platform’s feedback mechanisms (e.g., “Report inaccurate information”).
Inform the affected brand through a secure, verified channel.
If you’ve shared financial or identity details, contact your bank and relevant authorities immediately.

User reports help platforms strengthen their AI customer service and trust frameworks by feeding real-world abuse signals back into their systems.

What companies should do to protect customers

Individuals can only do so much. Enterprises must assume some responsibility for the broader digital ecosystem in which their customers operate.

Monitor search and AI outputs for spoofed contact info

Organisations should treat search and AI interfaces as part of their attack surface. That means:

Periodically querying major search engines and AI assistants for your brand + “support number”, “customer service”, “helpline”, etc.
Monitoring for mismatched or suspicious contact details.
Documenting findings as part of your AI risk management and incident response process.

Some teams integrate this into their security operations centre (SOC), combining OSINT tools with manual spot checks.

Publish verified contact endpoints in authoritative places

To make it harder for scammers to outrank or confuse legitimate data:

Ensure your official website clearly lists support numbers and channels.
Maintain accurate business listings on major platforms (Google Business Profile, Apple Maps, etc.).
Use structured data (schema.org) markup where appropriate so search systems can reliably parse your contact endpoints.

This isn’t a complete defence, but it strengthens your AI data security posture by giving AI systems better, more authoritative signals.

Work with search platforms to flag bad listings

Especially for high-risk sectors:

Establish contacts or escalation paths with major platforms for reporting malicious listings or AI answer issues.
Participate in sectoral information-sharing (e.g., ISACs) to learn about emerging scam patterns.
Document and periodically review your secure AI deployment strategy, including how you rely on—or defend against—external AI systems in customer journeys.

Collectively, these measures reduce the window of exposure when scammers successfully manipulate public data.

How Encorp.ai helps (enterprise controls and safeguards)

Public AI search is just the most visible layer. Inside your organisation, you may also be deploying chatbots, virtual agents, and internal copilots that answer user questions, surface contact details, or initiate workflows.

If those systems are not governed carefully, they can:

Repeat outdated or incorrect contact information.
Expose sensitive data from internal knowledge bases.
Be poisoned by low-trust data sources.

Encorp.ai focuses on secure AI deployment in enterprise environments, with a strong emphasis on AI trust and safety by design.

Key capabilities relevant to this problem space include:

Private agents and vetted knowledge sources

Rather than letting your internal agents scrape the open web, we help you:

Build agents that answer from curated, vetted knowledge bases.
Restrict retrieval to trusted repositories (e.g., your CRM, service desk, policy docs).
Enforce source-level permissions, supporting robust AI data security and AI data privacy.

This significantly reduces the risk that internal AI tools will surface spoofed contact information or unverified advice.

RAG/LLM ops controls for verified data

We implement retrieval-augmented generation (RAG) patterns that:

Attach citations to every answer, so users can see where data came from.
Allow you to mark certain fields (like phone numbers) as verification-required, forcing the system to check against a canonical store before answering.
Log prompts and outputs for AI fraud detection and audit.

These controls mirror the best-practice recommendations from bodies like OECD’s AI Principles and the UK’s AI assurance guidance.

Continuous monitoring and alerting for spoofed info

Encorp.ai’s AI Risk Management Solutions for Businesses are designed to automate parts of your governance and monitoring stack:

Track how often your agents mention specific contact details.
Detect anomalies, such as new or rarely used phone numbers appearing in responses.
Trigger alerts so your security or compliance teams can investigate quickly.

By treating AI behaviour as a monitored, governed asset—not a black box—you move from reactive clean-up to proactive defence.

Conclusion: staying safe in an AI-first search world

As AI-generated answers become the default interface to information, the stakes of AI trust and safety increase for both users and enterprises.

Key takeaways:

AI Overviews can surface fraudulent contact numbers because they synthesise data from a noisy, adversarial web without always verifying critical fields.
Users should never rely solely on an AI answer for support numbers; always cross-check via official sites, apps, or printed materials.
Companies need to treat search and AI interfaces as part of their extended attack surface, monitoring for spoofed details and improving the visibility of verified contact endpoints.
Inside the enterprise, secure, governed AI deployments with strong data controls and monitoring are essential to prevent your own agents from amplifying bad information.

If you’re responsible for AI strategy, security, or customer experience and want to operationalise these safeguards, explore how Encorp.ai’s AI risk management and secure deployment offerings can help: AI Risk Management Solutions for Businesses.

You can also learn more about our broader AI services and approach at https://encorp.ai.

If you are looking to harden your organisation’s AI posture beyond public search, you can learn how Encorp.ai helps automate AI risk management and governance across your internal AI stack: AI Risk Management Solutions for Businesses.