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You know that feeling when you’re at a party, and you can piece together everyone’s drama just by listening to random conversation fragments? 🍷 That’s basically what I did to a multi-million dollar AI system last month.
I was surviving on cold pizza and lukewarm coffee, scrolling through a new target’s documentation. “Privacy-First AI!” it screamed. “Your data never leaves your device!” 🤔 Yeah, right. I’d heard that one before.
My target was “SynapSafe,” a company using Federated Learning to train medical AI models across hospitals. The sales pitch was beautiful: hospitals keep their patient data, and only tiny “model updates” get sent to the central server. No data leakage! Totally secure!
Spoiler alert: It wasn’t. Here’s how I turned their privacy-preserving AI into a data-sniffing bloodhound. 🐕
Phase 1: The Recon — Finding the Secret Meeting Room
Most hunters look for api/v1/login. I look for the digital equivalent of the executive washroom. For a federated learning system, that means finding where the model updates are gathered and processed.