Meta AI repeatedly gave strangers a journalist’s publicly listed work phone number while claiming it was the chatbot’s own WhatsApp contact. Rob Price of Business Insider documented three such encounters in July 2024 involving people in Peru and Argentina. Meta acknowledged that public web material used for training can contain personal information, but it did not confirm how Price’s number reached the model or describe a technical fix.
What happened
In mid-July 2024, a person in Peru added Rob Price to a WhatsApp group after asking Meta AI how to add the chatbot to a group. Group members believed Price was Meta AI because the assistant had supplied his work number and directed them to contact it.
Price reported two more incidents involving users in Argentina. In each case, Meta AI again returned his phone number and represented it as the way to reach the assistant. Futurism quoted a translated response: “You can add me to a WhatsApp group as if I were just another contact. You only need to save my phone number.”
Business Insider described the behavior as Meta AI “pretending to be me and giving my phone number out to strangers.” Price characterized it as Meta’s technology “lying to people about me and my phone number.”
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Why strangers believed the number was Meta AI’s
Meta AI was available in WhatsApp individual chats and group conversations. When users asked how to add it, the model produced Price’s number instead of a verified service contact. Because the answer included an actionable instruction to save the number and message it, users treated the response as an official setup step.
The number was not presented as a random example. It was Price’s real work line, and the people who received it contacted him believing they were reaching Meta’s assistant.
Was the phone number private?
Price had published the work number broadly to solicit news tips. The reporting therefore treats it as publicly available information, not a confidential personal number. Public availability did not authorize Meta AI to reuse it as a chatbot identity or send it to unrelated users.
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This distinction matters: a model can repeat information that is technically public while still causing harm through a misleading context, unexpected audience, or false implication of endorsement.
What Meta said—and what remains unknown
After Price contacted Meta’s communications team, the unsolicited messages stopped. Meta spokesperson Emil Vazquez said, “Meta AI was trained on publicly available information online, which may include personal information. For example, if we collect a public blog post it may include the author’s name and contact information.”
Vazquez also said, “This is new technology, and it may not always return the response we intend, which is the same for all generative AI systems.”
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Meta did not confirm that Business Insider articles supplied Price’s number. Price estimated that nearly 300 Business Insider stories mentioning Facebook included the number, but that count was his own and was not independently audited. He suggested that the model may have formed a mistaken association among Facebook, Meta, artificial intelligence and his phone number; that explanation remains an inference, not a demonstrated cause.
Axel Springer, Business Insider’s parent company, said it had no agreement granting Meta access to its data for AI training. No public technical postmortem identified the specific data path or model behavior responsible.
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Could the problem be reproduced?
Price said he could not reliably reproduce the behavior. In his own tests, Meta AI gave inconsistent answers: at times it claimed to be unavailable on WhatsApp, and at other times it returned different phone numbers. That inconsistency makes it impossible to infer a stable rule from the three reported encounters.
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The documented evidence is limited to three episodes over several weeks, each supported by screenshots shared by users. No prevalence study, affected-user total or independent measurement established how widespread the behavior was.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why a language model can produce this kind of answer
Generative AI systems predict plausible text from patterns in their training and system context; they do not automatically verify that a phone number belongs to the service they are describing. A model can therefore combine a real person’s published contact detail with an incorrect concept—such as the identity of Meta AI—and state the result confidently.
Exact explanations are difficult to audit because a provider may not expose the training examples, retrieval records, safety filters or intermediate computations that led to one response. A public number can be repeated accurately yet still be assigned the wrong owner, purpose and audience.
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What users and publishers should take from the incident
For people whose contact details appear online
- Assume that a public number may be copied, indexed or reproduced outside its original context.
- Use a dedicated tip line or forwarding number when practical, rather than a primary personal number.
- If strangers report that an AI system gave them your number, preserve screenshots, dates, language and the exact prompt before contacting the provider.
- Ask the provider to investigate impersonation and unauthorized disclosure, while recognizing that a public number may not qualify as confidential data under every policy or law.
For anyone receiving an AI-provided contact number
- Do not assume a phone number is official merely because a chatbot supplies it.
- Verify contact details through the company’s published app, help center or verified account.
- Do not add unknown numbers to groups or share personal information until the identity is confirmed.
For publishers and AI providers
- Public-web collection can include names and contact details even when the surrounding article was not intended as training data.
- Systems that answer “how do I contact or add this assistant?” need strict controls against inventing or misassigning phone numbers.
- Incident response should address both the output and the person whose information was misrepresented, with a clear escalation path and a record of remediation.
Was there a confirmed fix?
No public technical fix or postmortem was described. The unwanted contacts stopped after Price reported the issue, but he could not reproduce it, so the available account does not establish whether Meta changed a model, filter, data source or account-specific behavior.
What this case demonstrates
The incident combines two separate risks. First, a model can make a confident false association and present it as an instruction. Second, training or retrieval from public pages can expose personal information in a new and misleading context. The case also shows why a provider’s acknowledgement that public data may contain personal information is not the same as confirming a particular source, mechanism or fix.
The Bottom Line
Meta AI gave Rob Price’s publicly posted work number to at least three strangers while claiming it was the chatbot’s WhatsApp contact. Meta acknowledged that public online material can contain personal information, but did not confirm the source, establish the root cause or publish a technical remediation.
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