AI-Assisted Exploit Development: An XNU Case Study

[ Abstract ]

A first-hand account of working with AI agents to turn two macOS vulnerabilities into controlled kernel read/write and root, and a look at where the models helped, where they did not, and what that means for modern mitigations.

Powerful frontier models have been increasingly successful at turning money into vulnerabilities in mature attack surfaces. Recent models have also begun to show a promising ability to turn vulnerabilities into proof-of-concept exploits. Given the speed of change around model capability, it's no surprise the security community has had a hard time assessing the jagged edges of capability when it comes to agent assisted exploit development. We share our experience working with agents to exploit two interesting vulnerabilities in a macOS to gain controlled kernel read and write and how we gained root code execution from there. We'll discuss the exploit techniques we used, how we worked with AI to discover, implement, and refine them. Finally, we reflect on when and how our model use was efficient for exploit development (for reasoning, tool development, analysis, or not at all).