English(EN) TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks

LLM驱动的框架实现了自适应激光雷达攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了TACTIC，一个利用多模态大语言模型（MLLM）执行自适应路侧激光雷达攻击的新型框架。该系统使用本地感知数据和路侧图像构建语义场景图，使其能够选择和配置诸如“推开”和“幻影障碍刹车”之类的攻击原语。在CARLA模拟中，TACTIC实现了100%的碰撞率，显著优于固定或随机选择的攻击策略，并通过异步重新规划提高了响应时间。 AI

影响展示了LLM协调针对物理系统的复杂、上下文感知攻击的潜力，引发了安全担忧。

排序理由学术论文，详细介绍了新框架及其模拟性能。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yiming Gao, Shaocheng Luo · 2026-10-01 04:00

TACTIC：面向路边激光雷达攻击的时空感知大语言模型战术规划

arXiv:2609.39969v1 Announce Type: cross Abstract: Physical LiDAR attacks are often evaluated using fixed primitives and manually selected parameters, despite their strong dependence on surrounding traffic. We present TACTIC, a scene-aware framework that uses a multimodal large la…

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TACTIC：面向路边激光雷达攻击的时空感知大语言模型战术规划

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