AI for improved vulnerability research
Google's Project Zero is developing ways to use large language models (LLMs) to automate vulnerability research. This is a new approach that could help find vulnerabilities missed by traditional methods. LLMs are good at code comprehension and can mimic the iterative approach of human security researchers.



Key features of Project Naptime
* Naptime is a framework that equips LLMs with tools to perform vulnerability research.
* These tools include a code browser, debugger, and scripting environment.
* The framework also allows for automatic verification of the LLM's output.

Naptime's effectiveness
Google's tests show that Naptime significantly outperforms previous LLM approaches in finding buffer overflow and memory corruption vulnerabilities. This suggests that Naptime is a promising tool for improving security research.

Future considerations
* More work is needed before Naptime can be widely used in real-world security applications.
* Researchers believe that with further development, LLMs have the potential to outperform traditional vulnerability discovery methods.

Similar projects
* HPTSA by Meta: This project also uses AI agents for vulnerability discovery, but it focuses on teams of agents with a planning component. It aims to address limitations of single agents in exploring various vulnerabilities and long-term planning.

* CyberSecEval 2 by Meta: This suite is designed to evaluate the capabilities of LLMs in finding and exploiting memory safety issues. Project Naptime showed significant improvements when tested with this benchmark.