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

AI Engineer World's Fair 2026 digest Program Adjacent pick 29 June – 2 July 2026

Recording

Speakers: Lovina Dmello (sponsor session)

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

  • Why: ShadowRay exposed over a billion dollars of data through a missing default config — not a zero-day — and that pattern is the rule in production ML. Useful security context for the multi-tenant GPU infrastructure the PM operates.
  • What: A synthesis of 139 peer-reviewed papers with five findings: misconfiguration (not missing features) is the dominant failure mode; adversarial defenses impose 15–30% inference overhead, which is why almost no production system runs them; ML-specific security tooling lags general DevOps tooling by years; security/data-science/ops expertise silos create persistent gaps; and LLM/multi-tenant GPU threats (prompt injection, RAG poisoning, GPU side channels) evolve faster than defenses. Plus a four-pillar defense-in-depth framework, a six-category threat taxonomy, and a four-level security maturity model (from abstract only).
  • How: Literature synthesis plus framework construction; the "which 3 misconfigurations account for most of the risk" claim is a talk deliverable to verify against the slides.
  • Where applicable: Production ML/LLM platforms generally, especially multi-tenant GPU serving; sponsor session, so expect a product tie-in, but the paper synthesis gives it substance.