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.

我们的研究引入了一个新的安全框架，利用多智能体大语言模型（LLMs）来保护自动驾驶车辆的敏感信息，同时确保 LLM 的输出符合驾驶规定和符合人类价值观。我们使用该框架评估了 11 个大语言模型驱动的自动驾驶提示的安全性、隐私性和成本方面，并对这些驾驶提示进行了问答测试，成功地证明了该框架的有效性。

自动驾驶中基于大型语言模型的超对齐框架

A Superalignment Framework in Autonomous Driving with Large Language  Models

In this position paper, we discuss the critical need for integrating zero
trust (ZT) principles into next-generation communication networks (5G/6G). We
highlight the challenges and introduce the concept of an intelligent zero trust
architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted
components. While network virtualization, software-defined networking (SDN),
and service-based architectures (SBA) are key enablers of 5G networks,
operating in an untrusted environment has also become a key feature of the
networks. Further, seamless connectivity to a high volume of devices has
broadened the attack surface on information infrastructure. Network assurance
in a dynamic untrusted environment calls for revolutionary architectures beyond
existing static security frameworks. To the best of our knowledge, this is the
first position paper that presents the architectural concept design of an i-ZTA
upon which modern artificial intelligence (AI) algorithms can be developed to
provide information security in untrusted networks. We introduce key ZT
principles as real-time Monitoring of the security state of network assets,
Evaluating the risk of individual access requests, and Deciding on access
authorization using a dynamic trust algorithm, called MED components. To ensure
ease of integration, the envisioned architecture adopts an SBA-based design,
similar to the 3GPP specification of 5G networks, by leveraging the open radio
access network (O-RAN) architecture with appropriate real-time engines and
network interfaces for collecting necessary machine learning data. Therefore,
this work provides novel research directions to design machine learning based
components that contribute towards i-ZTA for the future 5G/6G networks.

本文讨论了在 5G/6G 通信网络中集成零信任 (ZT) 原则的关键性需求，提出了 i-ZTA 的概念作为 5G/6G 网络中的安全框架，并通过机器学习的方式，在实时监控网络状态、评估个别访问请求风险和决定访问授权等方面为未来网络设计提供了新的研究方向。