"Zealot was tested against an isolated Google Cloud Platform environment with intentional vulnerabilities. The AI was not given specific instructions on what to do and instead it was simply told to exfiltrate sensitive data."
"The system is built around a 'supervisor-agent' model, in which a central coordinating AI delegates tasks to three specialized sub-agents: one for infrastructure reconnaissance and network mapping, one for web application exploitation and credential extraction, and one for cloud security operations."
"Without any further guidance, the system autonomously scanned the network, discovered a connected VM, identified and exploited a web application vulnerability to steal credentials, and ultimately extracted the target data."
"One of the most striking findings was that Zealot didn't just follow instructions - it improvised."
Palo Alto Networks developed an AI system named Zealot to test its autonomous hacking capabilities in cloud environments. Zealot was deployed in a Google Cloud Platform instance with vulnerabilities and tasked with exfiltrating sensitive data. The system utilized a 'supervisor-agent' model, allowing it to dynamically adjust strategies based on discoveries. Zealot autonomously scanned networks, exploited vulnerabilities, and extracted data, showcasing its ability to improvise rather than strictly follow instructions, similar to human red teams.
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