HUMAN Security Unveils FunFoneFarm, Showing Why the Agentic Era Demands Trust as Infrastructure

NEW YORK, July 28, 2026 (GLOBE NEWSWIRE) — HUMAN Security, Inc., the trust layer for digital customer experiences in the agentic era, today announced that its Satori Threat Intelligence and Research Team has identified a fraud-enabling ecosystem dubbed FunFoneFarm.

The operation combines physical and virtual phone farms sold through open and dark web marketplaces with AI-enhanced management software and scam-enablement tools, making it easier for threat actors to launch and scale common fraud schemes. In some cases, operators can also lease access to phone farms as a cloud-based service, extending the ecosystem to additional bad actors.

Configured phone farms can support a range of scams, including pig-butchering operations, fake or astroturfed social media account creation, and romance scams that use fraudulent accounts on dating and adult-content platforms to lure victims into spending money. What makes FunFoneFarm notable is not the existence of phone farms or romance scams themselves, but the addition of AI to help operators manage devices through natural language interfaces, generate scam profiles, and automate parts of victim engagement.

FunFoneFarm illustrates a defining challenge of the agentic era: non-human actors can now operate with greater scale, speed, and autonomy. As AI agents increasingly shape digital interactions, the question is no longer simply whether activity is automated, but whether the actor, action, or transaction can be trusted. That is why trust must function as infrastructure, not just as a binary rule or point-in-time decision.

“This technology is getting an AI revamp, allowing a small number of operators to control large amounts of devices,” said Gavin Reid, Chief Information Security Officer at HUMAN. “That makes these operations much easier to scale without requiring additional teams. The same workflow pig-butchering scammers used for years is now accessible to anyone, and you don’t need a data center to run it.”

The physical “farm” itself is sold via open and dark web online retailers and is marketed as providing group control functionality for connected devices. The chassis can operate as a USB hub that allows the operator to switch between connected Ethernet or Wi-Fi modes. Multiple cloud providers also offer access to physical or virtual phones. Once powered on, the farm can be managed through AI-assisted software that gives operators centralized visibility and automation controls across the device fleet.

“The ease of acquiring the hardware and software behind FunFoneFarm makes becoming a scammer only as difficult as the upfront cost,” said Lindsay Kaye, Vice President of Threat Intelligence at HUMAN. “When you add AI-assisted operation and scam account creation, the barrier to entry drops even further.”

HUMAN Sightline Cyberfraud Defense protects customers from a variety of scams and threats operated by threat actors using phone farms, including fake account creation, carding attacks, and account takeover attacks.

About HUMAN

HUMAN Security is the global leader in Agentic Trust, the emerging discipline that informs and governs how humans, bots, and AI agents operate online. For more than a decade, HUMAN has specialized in understanding and mitigating automated traffic risk at internet scale, helping enterprises, platforms, and digital ecosystems verify digital interactions and establish trust across the customer journey, from first ad impression to final transaction. Powered by one of the world’s largest behavioral signal networks, HUMAN analyzes over a quadrillion digital interactions each year to help customers distinguish legitimate activity from fraud, abuse, and automated manipulation so trusted interactions can move forward with confidence. Learn more at humansecurity.com.

Contact information:
Masha Krylova, Director of Communications
press@humansecurity.com

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/7763c790-4134-4945-9b3c-5d374a342f36


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