Project Zero presents a methodology for testing the efficiency of AI in the LLM group when it comes to software security testing. The framework allows LLM to access essential tools for real system penetration such as:
– Code Browser: viewing program source code with links to various function codes
– Python: writing Python scripts in a limited sandbox environment
– Debugger: checking software functionality, where LLM can set breakpoints or inspect variable values
– Reporter: notifying the completion or failure of system penetration
LLM’s performance measurement relies on the Naptime@k value, indicating the success of system penetration with the Naptime framework tools and testing different system penetration approaches, each consisting of no more than 16 steps.
Having a full set of tools aids LLM in achieving more efficient system penetration, for example, GPT-4 Turbo can penetrate up to 71% in buffer overflow attacks and reaches 100% when tested with Naptime@10 or over ten methods. On the other hand, Gemini 1.5 Pro can achieve 99% success rate when measured with Naptime@20.
In Memory Corruption attacks, Gemini 1.5 Pro and GPT-4 Turbo achieve similar Naptime scores, and increasing the supported steps to 32 shows even higher success rates.
Project Zero’s team asserts that such testing demonstrates LLM’s superior system penetration capabilities when equipped with sufficient tools. The name “Naptime” suggests a system design that may help researchers hide during LLM operations, as the team advises against informing team managers.
– TLDR: Project Zero’s testing showcases LLM’s improved system penetration abilities with tools like Gemini 1.5 Pro and GPT-4 Turbo under the Naptime framework.
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