Agentic AI has reduced the time between "I found a vulnerability" and "I have a working proof-of-concept", but that speed is only an asset if it comes with discipline. This talk walks through how LLM-driven agents can be wired into an exploit generation workflow: automating the tedious parts of testing, such as synthesis, payload iteration, and PoC scaffolding against scoped targets, while keeping a human firmly in the loop on the decisions that matter. Using the Linux kernel as a case study, the talk illustrates how this approach can turn crashes found by fuzzers into working proof-of-concepts.
