Mayur Madnani

Mayur Madnani

Staff Software Engineer

About

Mayur Madnani builds at the intersection of massive scale and cutting-edge AI. With experience across JioHotstar, Intuit, Walmart, and SAP, he specializes in ML infrastructure and distributed systems that power products used by millions every day. An inventor at heart, Mayur combines deep technical rigor—reflected in his patents and publications—with a passion for mentorship and community building. From being featured in Times Square for his impact as a mentor to competing in global autonomous racing leagues, he is driven by a constant pursuit of pushing the boundaries of applied AI.

As Large Language Models (LLMs) evolve from isolated chat interfaces into autonomous agents capable of interacting with external systems, the attack surface has dramatically expanded. We are no longer just trying to prevent an AI from generating toxic text; we are defending highly privileged, transactional pipelines against systemic exploitation.

This session explores the Full Stack of LLM application security. We will begin by demystifying the modern threat landscape, exploring how inherent training biases and mathematical generation methods can be actively weaponized to trigger targeted hallucinations and supply-chain vulnerabilities. From there, we will demonstrate how attackers utilize sophisticated direct and indirect prompt injections to reliably bypass standard semantic guardrails.

Crucially, attendees will see how these front-door exploits pivot directly into the transaction layer, hijacking agent tool access to exfiltrate private data or execute unauthorized actions. Finally, the session will shift from Red Team to Blue Team. We will deconstruct why traditional heuristic guardrails fail and introduce robust structural design patterns that secure AI workflows by design.

Attendees will leave with actionable, framework-agnostic strategies to stress-test their environments and automate the defense of their evolving LLM applications.

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