---
title: "Your LLM Stack Is a 2008 Database With Better Marketing: Why ML Security Is Dominated by Misconfiguration, Not Missing Features"
category: "talks"
date: "2026-06-29"
time: "11:10am-11:30am"
track: "Security"
room: "Track 5"
speakers: ["Lovina Dmello"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Security"
scheduleRoom: "Track 5"
scheduleLabels: ["Security", "Track 5", "sponsor", "confirmed"]
---
# Your LLM Stack Is a 2008 Database With Better Marketing: Why ML Security Is Dominated by Misconfiguration, Not Missing Features

## Conference Context
- Date/time: 2026-06-29 · 11:10am-11:30am
- Track/room: Security · Track 5
- Speaker(s): Lovina Dmello
- Session type/status: sponsor · confirmed

- Track: Security
- Room: Track 5
- Session type: sponsor
- Status: confirmed

## Session Description
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.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[lovina-dmello]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Your LLM Stack Is a 2008 Database With Better Marketing: Why ML Security Is Dominated by Misconfiguration, Not Missing Features ## Conference Context - Date/time: 2026-06-29 · 11:10am-11:30am - Track/room: Security · Track 5 - Speaker(s): Lovina Dmello - Session type/status: sponsor · confirmed - Track: Security - Room: Track 5 - Session type: sponsor - Status: confirmed ## Session Description 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.

### Speaker And Company Context
- [[lovina-dmello|Lovina Dmello]] — Senior Software Developer at [[nvidia|NVIDIA]].

### Topics Covered
- [[agent-security]]
- [[agentic-search]]
- [[coding-agents]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.