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TACTIC: Temporal and Context-Aware LLM Tactical Planning for Roadside LiDAR Attacks
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 language model (MLLM) to coordinate state-adaptive roadside LiDAR attack
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