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Fake AI Attacks Spread Across US Companies: How MSSPs Can Stop Incidents Early

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Fake AI Attacks Spread Across US Companies: How MSSPs Can Stop Incidents Early

Attackers are impersonating popular AI products to distribute malware, steal browser sessions, and compromise business accounts. ANY.RUN linked 1,258 public sandbox analyses to fake AI campaigns between April and October of 2026.

The campaigns also increase operational pressure on MSSPs serving US businesses, which accounted for 16% of country-attributed targets.

Similar attacks appearing across different customers can trigger repetitive investigations, consume analyst capacity, and extend response times, making it harder to maintain consistent service levels as threat activity grows.

The campaigns investigated by ANY.RUN use familiar AI brands to encourage employees to execute malicious commands, install compromised applications, or submit credentials through fraudulent authentication pages.

Attackers impersonate Claude, ChatGPT, GitHub Copilot, Gemini, and other AI services. Their lures appear in sponsored search results, legitimate hosting services, shared AI conversations, and counterfeit download websites.

One campaign impersonating Claude demonstrates how easily a routine software installation can become an infection.

In the attack analyzed through ANY.RUN's Interactive Sandbox, the victim encounters a page resembling the official Claude download website. Clicking the Windows download button copies a command to the clipboard and displays instructions for executing it through PowerShell.

Instead of installing the expected application, the command launches mshta.exe, retrieves a malicious HTA file, and triggers an encoded PowerShell execution chain.

The technique presents a difficult detection scenario. The employee initiates the process voluntarily, the attack relies on legitimate Windows utilities, and the malicious payload appears only after several execution stages.

For MSSPs managing multiple customer environments, this creates a recurring operational challenge: the initial indicators vary, while the underlying attacker behavior remains similar.


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