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Stratagems #14: Leo Found an AI Leak. He Wasn't the First to Find It.
TL;DR: Leo stumbles upon an AI model leak during an archive cleanup and realizes a competitor’s secret data is being reverse-written into CoreStack’s training cache. He chooses not to report it, uncovering a design that enables data leakage across systems.
During post-audit archiving, Leo discovers train outputs with an unfamiliar model version in CoreStack's cache. He covertly copies a line and hides it, noticing a FinOptima prefix and a suspicious ‘acl-train’ keyword. A deeper dive reveals FinOptima’s Cross-Reference Engine reverse-writing cache into CoreStack’s training directory on each API call. It’s not a bug but a deliberate design that enables data leakage. Leo’s actions highlight a dangerous path from discovery to concealment.
Question for the room: Have you encountered a similar cross-system leakage risk in training pipelines, and how did you address it without exposing sensitive findings?
— via dev.to
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