The offensive security world is being reshaped by Large Language Models (LLMs). While traditional exploit development demands years of reverse engineering expertise, we are now witnessing an acceleration where LLMs can reason about binaries, generate exploit scaffolding, and uncover patterns missed by humans. This talk explores the frontier of LLM-assisted binary exploitation, combining symbolic execution, automated fuzzing, and reinforcement learning to push vulnerability discovery beyond human speed.

We will demonstrate pipelines where LLMs act as intelligent assistants for reversing: reasoning about assembly flows, suggesting exploit primitives, and automating payload generation. Real-world case studies highlight how AI tools can identify exploitable memory corruption bugs faster than manual triage, while also reducing false positives common in conventional fuzzers.

The session also covers defensive implications: how attackers may scale zero-day discovery using LLMs, and how defenders can counter by integrating AI into detection and patch pipelines. This is not about “AI hype” but concrete methods, code snippets, and open-source tooling you can experiment with.

Hackers will leave with a realistic view of where LLM-assisted exploitation stands today, what’s possible tomorrow, and how to prepare for an era where machine speed challenges human ingenuity.