Your LLM Stack Is a 2008 Database With Better Marketing: Why ML Security Is Dominated by Misconfiguration, Not Missing Features

Official Schedule Context

ShadowRay exposed over a billion dollars of data through a missing authentication check. It wasn't a

zero-day. It wasn't a clever new attack class. It was a default config someone never flipped off.

That story is not the exception in production ML, it's the rule. We synthesized 139 peer-reviewed

papers on production ML security across access control, runtime security, infrastructure, and

operations. Five findings stood out, and one of them upends how most teams think about ML security:

Misconfiguration, not missing features, is the dominant failure mode. The mechanisms exist. Teams

aren't using them, or are using them wrong. - Adversarial defenses impose 15–30% inference overhead,

which is why almost no production system actually runs them. - ML-specific security tooling lags

general DevOps tooling by years. - Security, data-science, and ops teams operate in expertise silos

that create persistent gaps no single team can see. - LLM and multi-tenant GPU threats are evolving

faster than defenses (prompt injection, RAG poisoning, GPU side channels). This talk walks through

the four-pillar defense-in-depth framework, the six-category threat taxonomy that maps each attack

to its primary and secondary defenses, and a four-level security maturity model that matches

overhead budgets to deployment contexts. You leave knowing where your stack actually sits and which

3 misconfigurations account for most of the risk.

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