  Over the last year, significant advancements have been made in the realms of
large language models (LLMs) and multi-modal large language models (MLLMs),
particularly in their application to autonomous driving. These models have
showcased remarkable abilities in processing and interacting with complex
information. In autonomous driving, LLMs and MLLMs are extensively used,
requiring access to sensitive vehicle data such as precise locations, images,
and road conditions. These data are transmitted to an LLM-based inference cloud
for advanced analysis. However, concerns arise regarding data security, as the
protection against data and privacy breaches primarily depends on the LLM's
inherent security measures, without additional scrutiny or evaluation of the
LLM's inference outputs. Despite its importance, the security aspect of LLMs in
autonomous driving remains underexplored. Addressing this gap, our research
introduces a novel security framework for autonomous vehicles, utilizing a
multi-agent LLM approach. This framework is designed to safeguard sensitive
information associated with autonomous vehicles from potential leaks, while
also ensuring that LLM outputs adhere to driving regulations and align with
human values. It includes mechanisms to filter out irrelevant queries and
verify the safety and reliability of LLM outputs. Utilizing this framework, we
evaluated the security, privacy, and cost aspects of eleven large language
model-driven autonomous driving cues. Additionally, we performed QA tests on
these driving prompts, which successfully demonstrated the framework's
efficacy.
