LLMs and generative AI have shown effectiveness in many software domains.

Despite their issues with accuracy, hallucinations, nondeterminism, and limited context windows, LLMs are already demonstrating incredible

results finding real software vulnerabilities today. We will explore how agent design and structuring of the vulnerability discovery process can

overcome some of these limitations to find bugs in large codebases using LLMs.

We will also discuss how LLMs can complement and even surpass traditional techniques for vulnerability discovery and

remediation on real world software systems.

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