Collaborative driving systems leverage vehicle-to-everything (V2X) communication across multiple agents to enhance driving safety and efficiency. Traditional V2X systems take raw sensor data, neural features, or perception results as communication media, which face persistent challenges, including high bandwidth demands, semantic loss, and interoperability issues. Recent advances investigate natural language as a promising medium, which can provide semantic richness, decision-level reasoning, and human-machine interoperability at significantly lower bandwidth. Despite great promise, this paradigm shift also introduces new vulnerabilities within language communication, including message loss, hallucinations, semantic manipulation, and adversarial attacks. In this work, we present the first systematic study of full-stack safety and security issues in natural-language-based collaborative driving. Specifically, we develop a comprehensive taxonomy of attack strategies, including connection disruption, relay/replay interference, content spoofing, and multi-connection forgery. To mitigate these risks, we introduce an agentic defense pipeline, which we call SafeCoop, that integrates a semantic firewall, language-perception consistency checks, and multi-source consensus, enabled by an agentic transformation function for cross-frame spatial alignment. We systematically evaluate SafeCoop in closed-loop CARLA simulation across 32 critical scenarios, achieving 69.15% driving score improvement under malicious attacks and up to 67.32% F1 score for malicious detection. This study provides guidance for advancing research on safe, secure, and trustworthy language-driven collaboration in transportation systems. Our project page is https://xiangbogaobarry.github.io/SafeCoop.

翻译：协同驾驶系统利用多智能体间的车联万物（V2X）通信来提升驾驶安全性与效率。传统V2X系统以原始传感器数据、神经特征或感知结果作为通信媒介，长期面临带宽需求高、语义信息丢失及互操作性等挑战。最新研究探索将自然语言作为一种前景广阔的媒介，其能以显著更低的带宽提供丰富的语义信息、决策级推理能力及人机互操作性。尽管前景广阔，这一范式转变也为语言通信引入了新的脆弱性，包括消息丢失、幻觉、语义篡改及对抗攻击。本研究首次系统性地探讨了基于自然语言的协同驾驶中的全栈安全与安保问题。具体而言，我们构建了攻击策略的完整分类体系，涵盖连接中断、中继/重放干扰、内容欺骗及多连接伪造等类型。为缓解这些风险，我们提出一种智能体防御框架（命名为SafeCoop），该框架集成语义防火墙、语言-感知一致性校验及多源共识机制，并通过跨帧空间对齐的智能体转换函数实现。我们在闭环CARLA仿真环境中对32个关键场景进行系统评估，SafeCoop在恶意攻击下实现了69.15%的驾驶评分提升，恶意检测F1分数最高达67.32%。本研究为推进交通系统中安全、可靠、可信的语言驱动协同研究提供了指导。项目页面详见 https://xiangbogaobarry.github.io/SafeCoop。