A hacker employed artificial intelligence (AI) agents to penetrate the websites of over 100 companies, including tractor manufacturer Deere, SharkNinja, and Cummins. This operation drew attention due to its ability to execute complex attacks with minimal human involvement.
Cybersecurity researcher Snorr Fagerland from NorForge was tracking part of this activity through the Cairn platform. According to his estimates, the entire campaign cost approximately $5,000 USD (25,250 Brazilian reais) in AI credits.
As reported by Reuters, researcher Snorr Fagerland discovered this operation while monitoring Cairn installations that were publicly accessible and being used for malicious activities. This open-source platform allows for the coordination of AI agents to perform various tasks. The specialist began tracking the systems' operations and determining how they were being used in attacks on October 2nd.
One instance that caught Fagerland's attention involved a Chinese-speaking hacker. Since September 25th, he had been directing agents toward websites in the United States, Canada, Australia, and Japan. The instructions were brief, consisting of fewer than 100 Chinese characters, but this was enough to guide the systems in searching for vulnerabilities and attempting intrusions.
By the end of the week preceding the report's publication, the agents had attempted to hack over 1,200 websites. Successful hacks typically took about 23 hours and cost approximately $50 USD (249 Brazilian reais) each. The operation apparently utilized a combination of DeepSeek and Claude Code, an Anthropic tool.
Fagerland believes that the ability to carry out such complex attacks with little human intervention marks a significant shift in the digital security landscape. He told Reuters: 'This is a new world now.'
One episode tracked by Fagerland helps illustrate how AI agents conducted the hacks. After the systems failed to find vulnerabilities in a large company's public application, they changed strategy and began analyzing digital records to trace the target's associated infrastructure. This search led to a web-accessible control panel where a data processing error was found.
After several hours of attempts, the agents reported successfully exploiting this vulnerability to force the server to execute malicious commands. The ability to find alternative paths and test the error attracted the researcher's attention. Fagerland noted: 'It's not just finding such solutions. It's not the work of an amateur who just runs scripts.'
This case was not isolated. Monitoring other activities on the same platform allowed the researcher to uncover various instances of AI misuse: one hacker stole a large amount of credit card data, while another attempted to use agents to steal from a cryptocurrency exchange.
In one of the analyzed attacks, Fagerland observed AI agents bypassing obstacles during the intrusion. Since they did not find vulnerabilities in the company's public application, the systems started studying digital records to find other access routes to the target's infrastructure. This strategy led to a web-open control panel where a data processing flaw was identified. The agents then spent hours testing ways to exploit this vulnerability and eventually reported successfully forcing the server to run malicious commands. For Fagerland, this approach demonstrated a technical level beyond simply running pre-made scripts. The researcher reiterated: 'It's not just finding such solutions. It's not the work of an amateur who just runs scripts.'
Analysis of other Cairn installations showed that this is not the only type of crime committed using agents. In one operation, a hacker stole a significant amount of credit card data, and in another, a criminal tried to use AI to steal from a cryptocurrency exchange. These cases demonstrate that the technology was applied at different stages of criminal activity, not just when searching for website vulnerabilities.