Google has introduced Project Naptime, a novel framework designed to enhance automated vulnerability research using large language models (LLMs). This new system allows an AI agent to interact with a target codebase using specialized tools that replicate the workflow of a human security researcher. The initiative aims to automate and improve the efficiency of vulnerability discovery, enabling humans to take “regular naps” while the AI performs the research.
The Naptime architecture includes several key components such as a Code Browser tool for navigating the codebase, a Python tool for fuzzing in a sandboxed environment, a Debugger tool for observing program behavior, and a Reporter tool for task progress monitoring. By leveraging advances in code comprehension and general reasoning, Naptime enables the LLM to emulate human-like behavior in identifying and demonstrating security vulnerabilities.
Project Naptime is both model-agnostic and backend-agnostic, showing particular strengths in detecting buffer overflow and advanced memory corruption flaws. According to benchmarks from CYBERSECEVAL 2, the system has achieved top scores in these areas, outperforming previous benchmarks set by models like OpenAI’s GPT-4 Turbo.
Overall, Naptime aims to closely mimic the iterative, hypothesis-driven approach of human security experts. Google’s researchers highlight that this framework not only enhances the AI’s ability to identify and analyze vulnerabilities but also ensures the accuracy and reproducibility of the results.
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